Dissecting Biological and Synthetic Soft–Hard Interfaces for Tissue-Like Systems

Yin Fang; Xiao Yang; Yiliang Lin; Jiuyun Shi; Aleksander Prominski; Clementene Clayton; Ellie Ostroff; Bozhi Tian

doi:10.1021/acs.chemrev.1c00365

. Author manuscript; available in PMC: 2022 Jul 9.

Published in final edited form as: Chem Rev. 2021 Oct 22;122(5):5233–5276. doi: 10.1021/acs.chemrev.1c00365

Dissecting Biological and Synthetic Soft–Hard Interfaces for Tissue-Like Systems

Yin Fang ¹, Xiao Yang ², Yiliang Lin ³, Jiuyun Shi ⁴, Aleksander Prominski ⁵, Clementene Clayton ⁶, Ellie Ostroff ⁷, Bozhi Tian ⁸

PMCID: PMC8917063 NIHMSID: NIHMS1767435 PMID: 34677943

Abstract

Soft and hard materials at interfaces exhibit mismatched behaviors, such as mismatched chemical or biochemical reactivity, mechanical response, and environmental adaptability. Leveraging or mitigating these differences can yield interfacial processes difficult to achieve, or inapplicable, in pure soft or pure hard phases. Exploration of interfacial mismatches and their associated (bio)chemical, mechanical, or other physical processes may yield numerous opportunities in both fundamental studies and applications, in a manner similar to that of semiconductor heterojunctions and their contribution to solid-state physics and the semiconductor industry over the past few decades. In this review, we explore the fundamental chemical roles and principles involved in designing these interfaces, such as the (bio)chemical evolution of adaptive or buffer zones. We discuss the spectroscopic, microscopic, (bio)chemical, and computational tools required to uncover the chemical processes in these confined or hidden soft–hard interfaces. We propose a soft–hard interaction framework and use it to discuss soft–hard interfacial processes in multiple systems and across several spatiotemporal scales, focusing on tissue-like materials and devices. We end this review by proposing several new scientific and engineering approaches to leveraging the soft–hard interfacial processes involved in biointerfacing composites and exploring new applications for these composites.

Graphical Abstract

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1. INTRODUCTION

1.1. Underexplored Dynamics at Soft–Hard Interfaces

Due to their diverse and tunable physical and chemical properties, soft^1-7 and hard^3,8-10 materials have been extensively explored in fundamental studies of biological and material dynamics and diverse applications. Elucidation of the underlying processes that yield either soft or hard materials or their individual behaviors has enabled numerous advances, such as the development of strain-adaptive stiffening triblock copolymers,¹¹ the discovery of hyper-expandable and self-healing macromolecular crystals¹² and other soft crystals,¹³ and the synthesis of boron arsenide crystals¹⁴ with ultrahigh thermal conductivity. However, our understanding of the dynamics at, or enabled by, soft–hard interfaces is surprisingly scattered (Figure 1a). There is a pressing need for a unified view of the processes at these soft–hard interfaces and understanding their implications across multiple disciplines, such as basic materials science,^2,3,15-19 energy and environmental science,^2,20-22 catalysis,^23,24 biomedical research,^20,25 geobiology,^26,27 food science,²⁸ and even cosmetics.²⁰.

Figure 1. — Soft–hard interfaces cover a wide range of length and time scales. (a) The interfaces are diverse and cover those in molecular crystals, nanocrystal superlattices, nanowire nucleation and growth, biomineralization in both healthy and pathological conditions, active battery interfaces, soil chemistry and geobiology, and bioelectronics and implants. The soft and hard components are represented by colors within the range of the respective color bars (upper right). While the overall length scale ranges from subnanometer to above centimeter, the critical interfacial processes all occur at the molecular and nanometer scales where the interfacial mismatches are minimal. (b) Time scale is broad, and includes mechanical, electrical, and chemical events (shown here), such as bond vibration and enzyme turnover.

Several important interfacial mismatches exist at soft–hard interfaces, such as mismatches in mechanical properties, electrical conductivities, chemical diffusivity, and reactivity. These mismatches, while obstacles to our fundamental understanding of the interfaces, constitute a multifaceted platform for the discovery of novel smart materials. Chemical and physical processes at soft–hard interfaces may be distinct from those observed in homogeneous materials (e.g., molecular reaction-diffusion in a hydrogel matrix^29,30) or other heterostructure interfaces used for catalysis³¹ or electronics^32,33 (e.g., polyelemental nanoparticles³⁴ and high-order colloidal hybrid nanoparticles³⁵).

1.2. Definition of “Soft” and “Hard” and Its Relevance to Materials Chemistry

In Chemistry literature, the definitions of “hard materials or phases” (H) and “soft materials or phases” (S) are sometimes confusing. Soft materials can include a large collection of materials, including droplets, hydrogels, polymers, foams, and most biological components³⁶ (Figure 2a). Additionally, the boundary between “soft” and “hard” is not clear-cut. For example, crystals are usually considered hard and brittle. However, the soft ionic bonding in organic–inorganic halide perovskite³⁷ crystals has yielded facile ion transport and defect tolerance, and the dynamic bonding between hydrogel matrix and ferritin molecules has produced self-healing and antifragmentation behaviors in hyper-expandable macromolecular crystals.¹²

Stiffness or rigidity describes the material’s capacity to resist deformation in response to an applied force. It is determined by the elastic modulus and the material structures (e.g., cross-sectional area and length). While the elastic modulus is a property (i.e., it is a constant within a certain range), the stiffness is dependent on the material’s geometry. For example, while the elastic modulus is the same (or quite close) in bulk silicon and silicon nanowire, the stiffness is drastically different such that a silicon nanowire can easily bend while the bulk material cannot. For uniaxial deformation, we typically use the Young’s modulus (E), as defined by the initial slope of the uniaxial stress–strain curve (Figure 2b). Chemical reactions such as in situ mineralization can stiffen a deformable matrix.^3,16 In contrast, chemical or biochemical degradation can eliminate the stiff components in a composite to improve compliance.

Strength represents the maximum stress a material can tolerate before it reaches a certain limit or a transition (Figure 2b). Specifically, ultimate tensile strength is the maximum stress on the stress–strain curve. Another strength, the yield strength is the stress that initiates plastic deformation of a material. Strength is related to the bulk or internal structures and the defects. Chemical approaches such as incorporation of self-healing^38,39 or ionic/covalent cross-linking⁴⁰ moieties inside the materials can increase the strength.

Hardness describes the resistance of a material surface to indentation. Chemical processes (e.g., cross-linkage) or coating strategies at the surface can be used to improve the surface hardness of a material.

Toughness quantifies the resistance of a material to fracture (Figure 2b). Toughness is usually calculated by integrating the area under a stress–strain curve, i.e., the energy consumed before the fracture point. Chemical interactions between multiple components in a hierarchical layout (such as the brick-and-mortar structure in nacre³) can integrate superb toughness and stiffness in a single composite material.

Based on these definitions, stiffness or modulus (Figure 2a) is the dominant figure-of-merit in Chemistry literature to define a hard or soft material and is highly relevant to a material’s deformability. In particular, as the stiffness is size-dependent, one can create a highly deformable material that has intrinsically high modulus, such as a silicon nano-membrane.⁴¹ Stiffness, strength, hardness, and toughness can all be tuned chemically. In this review, we define an interface as a soft–hard interface if the two phases have a modulus or bending stiffness difference of at least one order-of-magnitude.

1.3. Soft–Hard Interfaces Are Common to Both Living and Nonliving Systems

Soft–hard interfaces are critical for many life processes.⁴² For example, studies of interfaces in nacre biogenesis revealed that epithelial cells produce all of the components required for the nacre composites and that several proteins^43,44 control the crystallization of aragonite (the hard granules in the composite).³ In bone growth, the highly coordinated processes of chemical transport, storage, and release and resorption of phosphate and calcium ions confer strength, self-healing properties, and stability.¹⁶ Perturbation of this homeostasis yields pathological mineralization or dissolution of bone.¹⁶ The principles gleaned from studies of natural interfaces can be applied to synthetic materials to confer properties such as ultrahigh toughness and strength³ and environmental adaptability.

Interfacial chemical processes can display cooperative assembly, periodic instability, or other dynamic activities necessary for material morphogenesis (Figure 3a). This principle underlies many mineralization processes where a biphasic interaction between the organic matrix and inorganic mineral precursors produces intricate three-dimensional (3D) architectures, including dodecahedral silica cages⁴⁵ (Figure 3b) or silica rings⁴⁶ over individual micelles and mesostructured materials.^47-52 In a nanowire growth system, the soft–hard interfacial chemistry produces periodic sawtooth faceting,⁵³ twin plane superlattices,⁵⁴ and the alloy droplet-assisted periodic deposition of atomic gold lines.⁵⁵

Figure 3. — Soft–hard interfacial processes have essential roles. (a) The interfacial process can yield new morphologies difficult to obtain with soft or hard components alone. It can stabilize the components against fragmentation, degradation, or disintegration. It also enables signal transductions between the soft and hard components. (b) 3D reconstructed cryo-TEM image of a dodecahedral silica cage, showing the intricate 3D architecture of the interface and the nanoscale feature size. The cryo-TEM image is modified with permission from ref 45. Copyright 2018 Springer Nature. (c) The high curvature from nanopillar structures recruits multiple curvature-sensing proteins, with protein names shown on the right. The image is modified with permission from ref 56. Copyright 2017 Springer Nature.

1.4. Harnessing Soft–Hard Interactions to Improve Composite Performance or Signal Transduction

Biphasic behaviors^57,58 can be controlled by tuning the mechanical, electrical, and chemical interfacial interactions between soft and hard components, leading to improved material and device performance. In orthopedic applications, the ideal biomaterial surface (i.e., for the implant-tissue interface) should include surface interactions or topography that resist bacterial adhesion and enhance integration with host cells and tissues.⁵⁹ In energy research, the incorporation of both hard and soft material components can improve device performance. For example, the M13 virus forms a hydrogel with superaerophobic properties. When assembled over a photoelectrochemical device surface, the hydrogel layer promotes the elimination of the gas bubbles generated during the photoelectrochemical reaction. This soft–hard composite significantly improves photoelectrode performance, as it helps maintain catalytic activity and reduces the overpotential for chemical reactions.⁶⁰

van der Waals interactions, hydrogen bonding, electrostatic force, or chemical cross-linking at soft–hard interfaces are critical for stability, damage tolerance, and integration (Figure 3c) in heterogeneous materials or devices.^21,23,61-64 For example, in mechanical engineering, when hard particles or fibers are used to increase the stiffness of a polymer matrix, the interfacial interactions between the filler materials and the polymer are critical for mechanical durability and avoiding phase separation.⁶¹ Likewise, conformal polymer coating over brittle ceramic mesostructures⁶⁴ has significantly improved compressive strength and toughness.

The intermolecular forces or chemical cross-linking at the interfaces can also improve bonding of hydrogels or elastomers onto various substrates, including metals, plastics, and even Teflon.^65,62 They can produce mineral sheet-based hydrogels capable of biocatalysis²³ or improve the longevity of a battery device^21,22 through dynamic processes (e.g., ring sliding of polyrotaxane⁶⁶) at the interface between the binder (e.g., PAA,^21,66 alginate,^21,54,67 and PEDOT^21,68) and the active material (e.g., Si,^21,66,67,69 LiNi_xCo_yMn_1-x-yO₂ [ref 68]). Preservation of soil organic matter (SOM) or tissue remains over mineral surfaces or in vertebrate fossils⁷⁰ represents another example of soft–hard interface-enabled stability. Samples from Cretaceous period dinosaur bones show structures indicative of endogenous collagen fibrils with 67 nm-wide periodic banding.⁷⁰

Chemical, topographical, mechanical, and electrical processes at soft–hard interfaces underlie many sensing and modulation applications in biomaterials science, as they enable diverse signal transduction and cascade reactions^15,71,72 (Figure 3a). For example, nanopillars have been used to study the effect of cell-material interfacial curvature on the mechano-biochemistry of the plasma membrane. Using nanopillars to control plasma membrane curvatures (+50 nm to ~−500 nm), it was reported that only the positively curved membranes are hotspots for proteins such as clathrin and dynamin⁵⁶ (Figure 3c).

1.5. Aims of This Review

Here, we review the role and importance of mechanical, electrical, and chemical activities at the interfaces between soft and hard materials. We discuss the length and time scales for the interfacial processes. For many (bio)chemical events at the cell-bioelectronic device interface, these scales are the mesoscopic length scale and millisecond time scale. We describe the unique microenvironment of interfacial mechanical, electrical, and chemical processes, including ion accumulation in the cell-bioelectronics cleft and focal adhesion production at cell contact sites. We discuss the tools used to reveal interfacial processes, such as immunohistochemical labeling and focused-ion-beam (FIB) milling in biointerfaces. Next, we propose a new interaction framework to classify the soft–soft (SS), soft–hard (SH), and hard–hard (HH) interactions involved in diverse soft–hard composites. To prove the validity of this soft–hard interaction framework, we first discuss its relevance in chemical processes at both naturally occurring and synthetic soft–hard interfaces, such as the inflammatory biochemical response and the light-triggered faradaic modulation effect at cell-silicon interfaces. We then apply the validated interaction framework to rationalize recent research advances in tissue-like materials, bioelectronics, and robotics, where SS, SH, and HH interactions beyond chemical ones (such as mechanical interactions) are involved. Finally, we use the validated interaction framework to predict new opportunities in tissue-like systems.

2. LENGTH AND TIME SCALES

Soft–hard interfaces are widely distributed throughout natural and synthetic systems (Figure 1). For example, through the interface between a soft mechanophore and a rigid ligand, macroscopic isotropic stress can be translated into atomic-level anisotropic strain for bond activation.⁷³ Soft–hard interfaces also play critical roles in the self-assembly⁴⁵ and transport properties^74,75 of nanocrystal solids, toughening in biominerals,^3,76 liquid alloy-catalyzed synthesis of semiconductor nanowires,^54,55,77-81 and precipitation of nanocrystals inside the alloy droplet.⁷⁷ In terms of mesoscopic level interfaces, tailored chemical interactions between the binder and the electrode^66,67,69 can increase the number of cycles in a battery, thereby improving battery performance;^21,22 cellular coordination can deposit renal stones at cell membranes and extracellular matrices;⁸² and rapid ionic dynamics at cell/semiconductor interfaces can enable either electronic sensing^15,83,84 or modulation^85-88 in excitable tissues. Soft–hard interfaces at the macroscopic level can also influence the chemical and biochemical dynamics of SOM^89,90 at mineral surfaces and the formation of biofilms on the surface of medical implants⁵⁹ or naval equipment.

While these soft–hard interfaces cover a wide range of length scales, the critical chemical processes all occur at the molecular or nanoscopic level (Figure 1a). For example, bioelectronics devices operate through the modulation of ion channel dynamics,^86,91-93 and the proteins in SOM become stabilized through molecular interactions with mineral lattices.^90,94 The fact that diverse soft–hard interfaces overlap within such a unique size regime suggests several guiding principles for functional integration of soft and hard components in either naturally occurring or synthetic systems (Figure 4a).

Figure 4. — Processes at soft–hard interfaces are unique. (a) Although different energies display different dependence on object size or dimensions, they converge at the molecular and nanoscopic scale. At this length scale, efficient signal transduction and minimal mismatches are expected at soft–hard interfaces. At this size regime, the boundaries between soft and hard materials, and the boundaries between living and nonliving systems, become blurred. This energy convergence can explain the critical roles of nanoscale interfacial processes at many soft–hard interfaces.³⁶ Reproduced with permission from ref 96. Copyright 2019 Nature Springer. (b) Dendritic binders and nanoclays self-assemble to form stable hydrogel. The feature sizes of the building blocks are at the molecular and nanoscopic levels, where multiple energy terms show similar amplitude such that the soft–hard interfaces can establish in a seamless manner. (c) To mitigate interfacial mismatches, additional buffer mechanisms are available. These mechanisms include the use of connecting linkers and deposits (additive strategy), the formation of a modulus gradient over the distance between the soft and hard phases (spatial strategy), and gradual and less perturbative condensation of the hard component at the interface (temporal strategy). (d) The soft–hard interfaces can produce the strain hotspots, which may be leveraged for special chemical processes or applications. For example, ring sliding of polyrotaxane close to the silicon/binder interfaces can be triggered during the operation of silicon-based battery anode.

The time scales of chemical processes at soft–hard interfaces cover an equally broad range (Figure 1b).³⁶ Fast processes include bond dissociation and electron transfer, which typically happen on the picosecond or subpicosecond time scale. As soft–hard interfaces can involve material growth and degradation, or cellular migration and development, some slow chemical processes can take hours to years.

3. UNIQUE (BIO)CHEMISTRY AT SOFT–HARD INTERFACES

Leveraging or mitigating the mismatches at soft–hard interfaces may yield new interfacial chemical processes. Additionally, interfacial chemical processes are influenced by both the activities of, and the interplay between, the soft and hard components. In this regard, the interfacial chemistry at soft–hard interfaces would be challenging to achieve, or inapplicable, in purely soft or hard media. Understanding unique interfacial properties, such as the confined space and the buffer zone, may guide our approach to exploring new chemical processes.

3.1. Utilizing the Confined Space to Mitigate Interfacial Mismatches

Many soft–hard interfaces are confined to the molecular or nanoscopic scale. This confinement is driven by the convergence of the amplitudes of many energy terms (e.g., electrostatic, mechanical, and chemical energies) at the molecular and nanoscopic scales (Figure 4a).^62,95 This energy convergence³⁶ represents one direct solution¹⁷ to mismatches between soft and hard components. It can give rise to a wide range of intricate nanocomposites, such as a multifunctional hydrogel self-assembled from nanoclays and dendritic binders²³ (Figure 4b) and the silica-cetyltrimethylammonium bromide (CTAB) interface in the silica cage⁴⁵ (Figure 2b). The convergence also suggests a pathway for diverse or multimodal signal transductions at, or close to, the interfaces (Figure 4a), where nonchemical modulations such as mechanical deformation¹² or optoelectronic stimulation^15,36 could trigger interfacial chemical or biochemical processes. For example, in the presence of balanced magnetic force and electrostatic repulsion, optical illumination of titanate and niobate nanosheets triggers in situ vinyl polymerization to yield anisotropic hydrogel composites.⁶³

3.2. Utilizing Buffer Zones to Mitigate Interfacial Mismatches

While multiple energies can converge at molecular-scale areas or volumes (Figure 4a), interfacial mismatches usually persist if the dimensions of the individual components are far above the molecular scale. Across different natural and synthetic soft–hard interfaces, we identify three chemical or biochemical buffer zone strategies (Figure 4c) to alleviate additional mismatch issues. These indirect solutions to mismatches can achieve seamless integration of soft and hard components.

3.2.1. Additive Strategy—Linkers or Deposits at the Interface.

At the interface between cells and a stiff substrate, linkers and deposits serve as the transition regime between the soft and hard components (Figure 4c, left). Chemical processes at this interface involve focal adhesion formation⁷¹ and extracellular matrix (ECM) deposition at the rigid surface of the substrate. For example, integrin aggregates on ECM substrates serve as the interfacial additive in focal adhesion formation in biological systems.⁹⁷ Formation of the nascent adhesion is usually accompanied by traction forces that induce conformational changes in cytoskeletal proteins, such as talin and vinculin. Folding and unfolding of the intracellular proteins then triggers multiple phosphorylation events and enzymatic reactions, which activate cytoskeletal contractility and generate more traction force over the focal adhesion.⁹⁷ This series of events can “stiffen” the cellular contact, creating an adaptable transition from the cells to the rigid substrates. Another example involves the interfacial load-bearing protein linker Atrina pectinata foot protein-1 (APFP-1) which contains L-3,4-dihydroxyphenylalanine (DOPA)-containing and mannose-binding domains. APFP-1 is found at the junction between stiff byssus and soft tissue in the bivalve mollusk; the DOPA domain forms Fe³⁺-DOPA complexes in the byssus and the mannose-binding domain attaches to the cells and tissues.⁹⁸ Other attachment linkers include microbial nanowires^99,100 that also facilitate electron transfer between minerals and microorganisms in soil.

3.2.2. Spatial Strategy—Graded Regime.

A modulus gradient over a long distance (up to centimeters) can efficiently reduce mechanical mismatch at a soft–hard interface for improved material or device durability and performance (Figure 4c, middle). At tendon-bone attachment sites, bone cells create a modulus gradient through precise control of mineralization across hard and soft tissues. Similar soft–hard gradients can be seen at interfaces between cranial sutures and the periodontal ligament,¹⁶ in squid beak,^101,102 and in sucker ring teeth.¹⁷ Another way to achieve the gradient is to establish an interpenetrating network in which the soft components are directly incorporated inside the hard component. This scenario is exemplified in the synthesis of mesostructured silica^50,51 (e.g., SBA-15). During self-assembly,⁵⁰ parts of the triblock copolymer chains are initially embedded inside the silica walls, producing a microporous “corona” upon template removal.¹⁰³ The interpenetrating network also increases the actual contact area, shrinks feature sizes at the interfaces, and enhances the interfacial roughness; similar principles have been used in promoting implant biointegration.¹⁰⁴

3.2.3. Temporal Strategy—Hardening the Composite over Time.

Linkers or graded regimes facilitate the transition from soft to hard phases by adding new molecular materials at the interfaces or by increasing the spatial range of the interfaces. Robust and “minimally invasive” soft–hard integration can also be established through sequential chemical transformation from a soft–soft precursor (Figure 4c, right). The hardening process usually involves heterogeneous crystal nucleation and growth^105,106 over a soft matrix or cross-linking of a polymeric network to increase the local modulus.^101,102 For example, during bone mineralization, cell-mediated deposition of hydroxyapatite (Ca₁₀(PO₄)₆(OH)₂) begins with the production and distribution of initially soft and amorphous precursors, such as calcium-chelating polyPi matrix vesicles (an enzymatically cleavable substrate).¹⁶ Synthetic equivalents include the sol–gel transition (i.e., the soft–hard transition) during precipitation of mesostructured silica.⁵⁰

3.3. Leveraging Interfacial Mismatches for New Capabilities

Various interfacial mismatches may be mitigated through the convergence of energy amplitudes or establishment of a buffer zone; however, the interfaces still represent the transition between soft and hard components. Therefore, we can expect to find the largest gradients of mismatched properties or behaviors at soft–hard interfaces (i.e., the interfaces represent hotspots for triggered chemical processes), an aspect which can be explored for unconventional stimuli-responsive chemical processes.^73,107-109 Additionally, as soft and hard materials respond differently to external stimuli such as mechanical stress or electrical charging, temporary mechanical instability or chemical imbalance can occur locally at the interface, triggering (bio)chemical responses. In Si-based lithium batteries for example,⁶⁶ polyrotaxane has been incorporated into the poly(acrylic acid) (PAA) binder. This incorporation enables conversion of the large binder-Si interfacial strain into the ring sliding movement of polyrotaxane during a Si-based lithium-ion battery operation (Figure 4d); this triggered process has improved the anode cycling stability.

3.4. Rationale for Exploring New Chemistry at Soft–Hard Interfaces

Interfacial mismatches, and the natural and synthetic strategies to mitigate or leverage them, suggest a spectrum of exploratory pathways toward new chemical processes (Figure 5). New supramolecular chemistry may be discovered through the formation of soft–hard interfaces in a confined space. Examples include the electrochemical synthesis of monolayer atomic crystal molecular superlattices (MACMS).^110,111 Indirect seamless integration of soft and hard components through buffer zones can help reveal new homeostatic or biomimetic principles in coupled chemical reactions, by which the soft–hard interfaces evolve spatiotemporally. Finally, mismatches can be intentionally retained to yield mechanical instability¹¹² or chemical imbalance, because of the different responses of soft and hard components to external stimuli. These mismatches can then be used to explore new stimuli-responsive chemistry or chemical processes far from equilibrium.

Figure 5. — Soft–hard interfaces present several pathways of exploring new interfacial processes, by either mitigating or leveraging the mismatches at the interfaces. Shown here only highlights the chemical processes.

4. TOOLS TO STUDY INTERFACIAL PROCESSES

Mechanical characterization methods, such as tensile and compressive deformation, dynamic mechanical analysis, shear rheometry, pipet or micropipette aspiration, (nano)-indentation, and atomic force microscopy, can provide important mechanical properties of the soft or hard components, or their integrated systems. These studies can yield the stress–strain behaviors upon uniaxial or dynamic deformation, and highly localized mechanical property probing and mapping. While it is challenging to use these tools to probe the interface mechanics directly, the mechanical information collected through a suite of such studies may provide clues for the interfacial processes.

Many conventional tools (Figure 6) can be used to study the chemical composition and chemical dynamics at soft–hard interfaces (Table 1). While these tools cannot reveal the mechanical properties of the soft or hard components, they provide essential molecular- or atomic-level chemical information that is responsible for the observed mechanical behaviors (which usually occur at scales above the micrometer level). Electron energy loss spectroscopy (EELS), energy-dispersive X-ray spectroscopy (EDS) and secondary ion mass spectrometry (SIMS) use electrons (i.e., EELS, EDS) or primary ions (i.e., SIMS) as the incident beam. They determine the elemental information (EELS, EDS, and SIMS) or even the state of valence electrons (EELS) by collecting inelastically scattered electrons (EELS), X-rays (EDS), and secondary ions (SIMS). Laser-assisted atom probe tomography (APT) can provide both elemental composition and subnanometer 3D imaging by decomposing the voltage-biased (DC voltage, 3–15 kV) sample with a laser beam. In addition to determining the composition, other tools can reveal the morphology, phase, and chemical bonding near soft–hard interfaces (Figure 6). Detailed descriptions of these tools, including their working principles and spatiotemporal resolutions, can be found in review papers published elsewhere.^113-116

Table 1.

Characterization of Selected Chemical Processes at the Soft–Hard Interfaces^a

	composite materials	soft components	hard components	characterization of the chemical processes
cooperative integration	mesoporous material synthesis⁵⁰	surfactants or block copolymers	inorganic precursors, oligomers or their cross-linked network	SAXS, NMR, and TEM were used to capture the immediate products, biphasic self-assembly dynamics and material structures during the mesostructure formation.
	high water content hydrogel²³	telechelic dendritic macro-molecules	clay nanosheets	rheology test and cryo-TEM showed that electrostatic interaction between positively charged guanidinium ion pendants and negatively charged clay nanosheets stabilizes the hydrogel.
	monolayer atomic crystal molecular superlattice¹¹⁰	lauryl- or cetyl- or octadecyl-trimethylammonium bromide	monolayers separated from black phosphorus, WeSe₂, SnSe, NbSe₂ etc.	cross-sectional TEM, XRD, XPS were used to study the interactions between the negatively charged monolayer atomic crystals and the positively charged surfactant layers.
	twin plane superlattice in nanowires^54,137	gold-based alloy droplets	InAs, GaP, InP, etc.	environmental TEM showed that the variation of alloy/semiconductor interface geometry in a growing nanowire and surface energy minimization introduces periodic twin planes and sidewall faceting.
directed organization	P. carterae coccolith shell¹⁹	coccolith plate, soluble macromolecules	calcite nanocrystals	AFM, HAADF-STEM, EELS, and ATR-FTIR revealed that specific interactions between soluble macromolecules and coccolith plate direct calcite precursors for mineralization at the plate periphery.
	bone¹³⁸	type I collagen matrix, osteoblast, etc.	apatite nanocrystals	Cryo-TEM, tomography and MD simulation suggest that the positive net charge close to the C-terminal end of the collagen promotes amorphous calcium phosphate infiltration, and that the charged amino acid clusters promote mineralization.
	squid beak¹⁰²	hydrated chitin network	protein-cross-linked and dehydrated chitin network	SDS-PAGE, rheometry, and turbidity measurements revealed that the modulus gradient in squid beak is created by chitin-binding proteins (DgCBPs), histidine-rich proteins (DgHBPs) based coacervates, the dynamic interaction of these proteins with the chitin network, and hydration/dehydration.
	synthetic nacre¹³⁹	chitosan, β-chitin, silk fibroin	aragonite (CaCO₃)	cross-sectional TEM confirmed that Ca(HCO₃)₂ decomposes in the presence of poly(acrylic acid) and Mg²⁺ within the scaffold.
	dodecahedral silica cage⁴⁵	mesitylene-swelled CTAB micelles	primary silica cluster	Cryo-TEM and tomography showed that primary silica clusters are symmetrically assembled on the surface of oppositely charged surfactant micelles.
	2D Au–Si sheet over liquid alloy⁸⁰	liquid Au–Si alloy droplet	Au–Si solid sheet	environmental TEM highlighted the crystallization of ordered layers of Au–Si over the alloy droplet surface near the eutectic temperature.
	2D metal oxides over liquid metal¹⁴⁰	Ga–In–Sn liquid metal	HfO₂, Al₂O₃, Gd₂O₃, Ga₂O₃	TEM and AFM confirmed the formation of metal oxides over the liquid metal surface.
	photosynthetic bacteria¹⁴¹	Moorella thermoacetica	CdS nanoparticles	TEM and EDS showed that when Cd²⁺ and cysteine are supplied to the CO₂-reducing bacterium M. thermoacetica, CdS nanoparticles precipitate over the bacterium surface in situ.
	peptide nanowire arrays¹⁴²	Tyr-Ser-Ala-Thr-Phe-Thr-Tyr peptides	MoS₂ substrate	AFM and MD simulation revealed the nucleation and growth dynamics of peptide array formation over MoS₂ substrate.
	protein arrays and patterns¹⁴³	helicalrepeat (DHR) proteins	mica substrates with controlled K⁺ lattice sites	de novo protein design, AFM, and SAXS were used to achieve nanowire and honeycomb arrays of proteins on mica.
signal transductions	kidney crystal granulomas⁸²	tubular cells, giant cells, fibroblasts, etc.	CaOx crystals	histology straining and TEM revealed kidney stone location and formation dynamics.
	cell/nanostructure interface⁵⁶	cells (SK-MEL-2 and genome-edited SK-MEL-2 line)	quartz nanopillars and other nanostructures	immunostaining, FIB-SEM, and fluorescence imaging showed that positive curvature of nanostructures recruits clathrin-mediated endocytosis proteins, such as clathrin and dynamin.
	neuron/coaxial Si nanowire interface⁸⁶	DRG neuron	p-type/intrinsic/n-type coaxial Si nanowire	APT and patch-clamp revealed that atomic gold at the coaxial Si nanowire surface promotes the photoelectrochemical reaction, which reduces the local electrochemical potential at the neuron/nanowire interface and elicits action potentials in neuron.

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SAXS: small-angle X-ray scattering; NMR: nuclear magnetic resonance; TEM: transmission electron microscopy; SEM: scanning electron microscopy; XPS: X-ray photoelectron spectroscopy; XRD: X-ray diffraction; AFM: atomic force microscopy; HAADF-STEM: High-angle annular dark field-scanning transmission electron microscopy; EELS: electron energy loss spectroscopy; ATR-FTIR; attenuated total reflection-Fourier transform infrared spectroscopy; EDS: energy-dispersive X-ray spectroscopy; SDS-PAGE: sodium dodecyl sulfate polyacrylamide gel electrophoresis; PCR: polymerase chain reaction; FIB: focused ion beam; APT: atom probe tomography.

Biochemistry tools are particularly powerful when living systems are involved at soft–hard interfaces (Figure 6 and Table 1). For example, using time-of-flight secondary ion mass spectrometry, amino-acid fragments typical of collagen fibrils were found in Cretaceous period dinosaur bones, highlighting the fact that tissue residues can persist throughout the fossil record.⁷⁰ Integrated RNA-Seq and proteomics revealed two protein families, chitin-binding proteins, and histidine-rich proteins, in the soft–hard transition region of squid beak.^101,102 In another example, immunohistochemical (IHC) staining^117,118 showed significant upregulation of glial intermediate filament proteins and the ECM molecules laminin and collagen IV, together with the softening of nearby tissue¹¹⁸ upon insertion of rigid sharps into the brain.

Theoretical and computational tools (Figure 6), such as molecular dynamics simulations, have revealed numerous dynamic chemical processes at soft–hard interfaces,^61,119 including those that cannot be probed experimentally. These chemical processes include molecular wrapping over carbon nanotube sidewalls,^120-122 translocation of nanoparticles in cells and tissues,^123-125 and packing of nanowires in vesicles.^124,126

4.1. Challenges in Characterizing Soft–Hard Interfaces

While the current tools have revealed important chemical insights, characterizations at soft–hard interfaces are generally more complicated than those performed on simple soft or hard components.

Soft–hard interfaces are usually embedded, requiring tools that can either directly visualize the interfaces nondestructively or provide 3D chemical or structural reconstruction of the interfaces after destructive sample preparations (e.g., APT¹²⁷ or computed X-ray tomography). Currently, tools capable of interfacial probing are limited, but we can expand the investigative tool kit by looking into existing approaches adopted in other areas. For example, optoacoustic imaging exploits ultrasonic acoustic waves that are generated by light-matter interactions. Optoacoustic imaging achieves a greater detection depth than imaging methods that rely on photons; the latter typically yields very limited depth of penetration due to photon scattering. While optoacoustic imaging has been used intensively for in vivo imaging^115,116,128 and has recently been used to reveal hidden underdrawings in paintings,^129,130 its utility in the study of dynamics at synthetic soft–hard interfaces is less explored and represents a promising tool. Other nondestructive methods to potentially reveal the mechanical properties at the soft–hard interfaces include magnetic resonance elastography and ultrasonic shear wave elastography,^131,132 which are usually used for probing biological tissues too. In particular, the ultrasonic elastography^131,132 uses ultrasonic pulses to generate shear waves through the material or biological tissues. While this technique is usually used to reveal various pathologies in biological tissues, it could be broadly extended to measure a composite’s mechanical heterogeneity through strain imaging and shear wave imaging.

Special preparation methods must be considered for samples that contain soft–hard interfaces. Certain characterizations require cutting through soft–hard interfaces, such as FIB milling for serial block imaging of 3D volumes in SEM¹³³ or generation of thin specimens for STEM and APT imaging. However, soft–hard interfaces display mechanical mismatches, which may necessitate cryogenic operations to produce fully hydrated frozen samples,¹³⁴ preserve the soft material structures, and avoid delamination at the soft–hard interfaces during FIB milling.

Finally, soft or hard materials usually use different sets of tools for their characterization. For example, while hard materials are usually stable under the electron beam in TEM, soft materials are prone to significant sample damage and artifact generation.^114,135,136 Likewise, confocal laser scanning microscopy (CLSM) is a powerful technique for solving the static or dynamic distribution of biomolecules in soft systems (Figure 6), but it usually provides minimal information on hard materials. In this regard, correlative microscopy and spectroscopy is needed to provide information on both the soft and hard phases at the interfaces.

4.2. Studying the Dynamics of Interfacial Chemical Processes

There are two broad approaches to investigating the dynamics of chemical processes at soft–hard interfaces (Figure 6).

First, in situ microscopy and spectroscopy have been widely used to probe chemical dynamics in directed organization, cooperative integration, or signal transduction at interfaces (Figure 6a). Tools such as cryo-TEM,^106,114 environmental TEM or TEM in liquid cells,^135,136,144 SAXS, CLSM, and field-effect transistor-based electrical recording display high spatial or temporal resolution and high sensitivity for capturing localized, transient, and weak chemical events. For example, using environmental TEM, the Ross group conducted multiple studies of the chemical dynamics at alloy droplet-semiconductor nanowire interfaces^{33,53,77,78,80,81,145-150} (Figure 6a, second from left), including that of sawtooth faceting,⁵³ ledge flow,^81,148 twin plane or other defect formation,^137,148 and silicide quantum dot growth inside an alloy droplet and its stepwise merging into the nanowire backbone.⁷⁷ Electrical transport spectroscopy (ETS) was recently used for in situ recordings of molecular intercalation of 2D layered semiconductors,¹¹¹ representing a novel electronic method for studying soft–hard interfacial chemistry (Figure 6a, right).

Second, for a process that occurs over a longer duration (e.g., > a few days), single-shot, static and ex situ measurements are usually the best option (Figure 6b), as in situ recording is less feasible. The dynamic information can be inferred from measurements at multiple time points. In particular, correlative microscopies and spectroscopies alongside theory and computations studies can be used to integrate information on morphology, phase, molecular and elemental identity, and chemical bonding.

5. FRAMEWORK TO CLASSIFY SOFT-HARD INTERFACES

In this section, we move beyond chemical interfacial processes. Unique processes at the soft–hard interfaces are determined by the diverse soft material interactions (SS), the hard material interactions (HH), and the interactions between the soft and hard components (SH). Depending on the context of the discussion, the SS/HH could be the intramolecular interactions that hold the constituent phase together, the restoring forces upon elastic deformation, or physical or biological behaviors of the constituent. Likewise, the SH could be adhesion or bonding, chemical or biological recognition, or signal conversion (e.g., from photoelectrochemical to electrophysiological signals) at the soft–hard interfaces.

Based on the relative dominance of the roles played by the three interactions in determining the spatial organization or the temporal sequence of the soft–hard interface, we propose a soft–hard “interaction framework”, which covers both the spatial and temporal aspects of the interactions (Figure 7, Supplementary Video 1).

Each color domain in Figure 7a-d (spatial aspect) is the overlapped area from two half-triangles which are defined by two inequalities that need to be satisfied simultaneously. The arrows in Figure 7e,f (temporal aspect) denote the signal transduction directions. This framework, especially the notion of “relative dominance” is primarily qualitative, although quantitative information like bending stiffness or power can inform the material choices for interface designs. For example, the bending stiffness of a brain tissue could be in the range of 10⁻⁴~10⁻¹ nN m. If we would like to establish very compliant brain interfaces (i.e., SS > HH) from pure silicon-based membranes, we could use a 10 nm-thick membrane (bending stiffness, ~10⁻⁵ nN m, ref 15). Another example is establishing a biointerface between a single bacterium and a single silicon nanowire for optically triggered metabolic control. The power consumption of a bacterial cell is ~10⁻¹² W, while the power output from a coaxial silicon nanowire-based photovoltaic device is ~10⁻¹⁰–10⁻⁹ W (ref 15). This power difference makes the signal transduction from silicon nanowire to bacterium possible, which has been proved experimentally.^151-153

Additionally, as shown in Figure 4a, the energy amplitude is size-dependent except for the thermal energy. This provides a rationale to tune the “relative dominance” (i.e., SS, HH, and SH) and the domains within the interaction framework through size and shape control of the constituent materials. For example, an epidermal electronic device needs to follow the texture and mechanics of the skin tissues. The device composite usually contains both the high modulus materials and an elastomer substrate. This requires that the high modulus components, such as semiconductors or metals, would display low bending stiffness (i.e., to reduce the HH), so that the sensing or modulation functions enabled by these materials maintain during the stretching of the epidermal electronics. Reducing the “relative dominance” of HH can be achieved by using thin membranes and/or serpentine structures for the high modulus components. We wish that this framework could unify seemingly random and disconnected soft–hard examples in both the naturally occurring and the synthetic systems. This unified framework then may help us design new soft–hard interfaces by projecting the commonalities of the existing examples.

5.1. Spatial Aspect of the Interaction Framework

5.1.1. Deterministic Assembly.

5.1.1.1. Relative Dominance: SS > SH and HH > SH.

Deterministic assembly, shown in the first interaction domain (Figure 7a, pink area), combines the soft and hard phases via mechanical manipulation (e.g., folding, mixing, and layer-by-layer lamination) or top-down microfabrication. The role of the interaction between the soft and hard phases (i.e., SH, such as interfacial adhesion) is relatively minor in constructing the final composite, compared to that of the interaction within the soft phases (i.e., SS, such as dynamic hydrogen bonding in a double network hydrogel) or within the hard phases (i.e., HH, such as deformation of a Si membrane). Most composite materials can be fabricated following this framework, as the original properties of the soft or hard phase can be well maintained upon composite formation.

5.1.2. Cooperative Integration.

5.1.2.1. Relative Dominance: SH > SS and SH > HH.

Cooperative integration (Figure 7b, green area) establishes a final composite structure that SS or HH alone cannot achieve. SS or HH cannot overrule SH, otherwise stable soft (i.e., dominated by SS) or hard phases (i.e., dominated by HH) will form.

5.1.3. Directed Organization–Initiated from the Soft Phase.

5.1.3.1. Relative Dominance: SS > SH > HH.

In this domain (Figure 7c, orange area), the structural or chemical information encoded in or dynamically displayed by the soft phase (i.e., SS) directs the organization of the final composite, forming a composite structure that cannot be achieved by HH alone. The soft phase must remain stable throughout the chemical processes. Therefore, SH cannot be the most significant interaction, as that would destabilize the soft template. However, SH is also essential as it transfers the information or dynamics from the soft phase to the hard phase. HH cannot overrule SH, otherwise stable hard phases (i.e., dominated by HH, such as uncontrolled crystallization) with structural or chemical properties that may not match those of the soft phase will form.

5.1.4. Directed Organization–Initiated from the Hard Phase.

5.1.4.1. Relative Dominance: HH > SH > SS.

In this last spatial aspect (Figure 7d, blue area), the static or dynamic information encoded in the hard phase (i.e., HH) guides the organization of the final composite, establishing a composite structure that cannot be achieved by SS alone. The hard phase must remain stable throughout the interfacial organization. Therefore, SH cannot be the most significant interaction, as that would destabilize the hard template. As above, SH is also essential as it transfers the information or dynamics from the hard phase to the soft phase. SS cannot overrule SH, otherwise stable soft phases (i.e., dominated by SS, such as uncontrolled polymerization) will form and “deterministic assembly” would dominate (i.e., SS > SH and HH > SH).

5.2. Temporal Aspect of the Interaction Framework

5.2.1. Signal Transduction–Initiated from the Soft Phase.

5.2.1.1. Temporal Sequence: SS → SH → HH.

Soft phase-initiated signal transduction (Figure 7e) typically deals with phenomena that involve biological components or stimuli-responsive synthetic materials, e.g., bacteria-enabled rapid degradation of materials in the soil. As HH, SH, and SS can be temporarily (and spatially) separated, represent different types of interactions (e.g., chemical, mechanical, and biological), and involve cascades of reactions (or signal amplification), their relative dominance is hard to define or arbitrary.

5.2.2. Signal Transduction–Initiated from the Hard Phase.

5.2.2.1. Temporal Sequence: HH → SH → SS.

The last interaction category (panel f) focuses on signal transduction that is initiated by the hard phase, e.g., tissue inflammatory response at a rigid implant surface. As above, as HH, SH, and SS can be temporarily (and spatially) separated, represent different types of interactions (e.g., mechanical and biological), and involves a cascade of reactions (or signal amplification), their relative dominance is hard to define or arbitrary.

Multiple soft–hard interaction frameworks can coexist in a single material system, although they are often relevant to different stages of the system, such as material preparation or biointerface application. For example, many stretchable bioelectronic devices are organized through controlled buckling via compressive force from the elastomeric substrates. This 3D device preparation process can be classified as a directed organization that is initiated from the soft phase (i.e., S₁S₁ > S₁H > HH); S₁ and H denote the elastomer and the electronic circuits, respectively, and all three interactions are focused on mechanical behaviors. After the device is prepared, biointerfaces for either electronic sensing or stimulation can be established. In the case of sensing, the soft–hard framework follows signal transduction initiated from the soft phase, i.e., S₂S₂ → S₂H → HH. In the case of stimulation, the soft–hard framework instead would follow signal transduction initiated from the hard phase, i.e., HH → S₂H → S₂S₂. In these two signal transduction scenarios, S₂ and H denote electrogenic cells (e.g., neurons or cardiomyocytes) and the electronic circuits, respectively, and all of these interactions are relevant to (bio)electrical processes.

6. PROOF FOR THE INTERACTION FRAMEWORK

In section 5, we proposed an interaction framework to classify the SS, SH, and HH interactions involved in the structural formation or (bio)chemical dynamics of soft/hard composites. Section 6 will prove the utility of this new framework using established examples in both biological and synthetic systems.

6.1. Deterministic Assembly

Deterministic assembly (a) preserves the original properties of the soft and hard phases. Examples of deterministic assembly in biological systems are rare, as biological soft–hard interfaces are active and usually form via cooperative integration (b) or directed organization (c and d). However, numerous examples of deterministic assembly exist in synthetic systems, especially those prepared by homogeneous mixing (e.g., the process of preparing a blend of carbon nanotubes and conducting polymers for stretchable bioelectronic device^154,155) or top-down fabrication (e.g., the process of fabricating a long-lived bioelectronic device^83,156). Therein, SS and HH interactions dominate so that the stable performance of the soft and hard materials can persist upon the integration of the soft and hard components. The role of the SH interaction is relatively minor (SS > SH, HH > SH) in terms of the formation of the composite structure, although the SH interaction can improve bonding between the two phases and can yield improved physical properties like enhanced toughness. SH cannot override SS or HH; otherwise, disassembly of the original soft or hard phases would occur. To prove the validity of our proposed soft–hard interaction framework, we will focus on other interaction domains (b–f).

6.2. Cooperative Integration

In cooperative integration of soft–hard interfaces, phase separation into uncontrolled soft-only (i.e., SS > SH) or hard-only (i.e., HH > SH) domains must be inhibited. Therefore, the relative dominance of the three interactions (Figure 7b) usually follows SH > SS, SH > HH (Figure 8a). Both the hard and soft components display dynamic chemical or structural changes during the integration. The SH interaction usually involves coordinate covalent bonding, electrostatic interactions between charged groups, hydrogen bonding, or van der Waals interactions. The SH interaction can be reversible, suggesting a mechanism for environmental adaptability and stimuli responsiveness.

Figure 8. — Interaction framework–cooperative integration can be verified with existing examples. (a) The cooperative integration diagram. (b) Synthesis of mesostructured inorganic materials often involves self-assembly of inorganic precursors (positively charged I⁺ or negatively charged I⁻) with cationic surfactants (S⁺) or nonionic block copolymers (N⁰). The soft–hard (SH) interactions can involve protonation (H⁺) and anion coupling (X⁻), and occur in either acidic or basic conditions. In block copolymer-based synthesis, e.g., with pluronic F127, the hydrophilic segments such as PEO are inserted inside the inorganic walls, producing a graded regime. The addition of cosurfactant such as sodium bis(2-ethylhexyl) sulfosuccinate (AOT) can tune the interface chemistry and the curvature. (c) Electrochemical intercalation of black phosphorus (BP) by CTAB molecules produces a soft–hard superlattice structure. Electrochemical reactions are shown in the lower left panel. As the superstructure cannot be formed by either the black phosphorus or the CTAB molecules alone, the cooperativity at the soft–hard interfaces is the key to the final composite structure.

6.2.1. Naturally Occurring Process.

Numerous naturally occurring examples of soft–hard interfaces involve cooperative assembly, especially those related to morphogenesis and development.^3,16,57,157 The shape of the final complex composite depends on the dynamic organization of both the soft (e.g., intracellular membranes, collagen, gluten matrix, cytoplasm, or cellulose) and the hard (e.g., silica, hydroxyapatite, starch granules, cell walls, or ligin matrix) components. For example, in the tooth of a marine mollusc, the chiton Chaetopleura apiculate, the nanocrystalline magnetite (Fe₃O₄) is self-assembled with chiton-based organic fibers.⁷⁶ In another example of coccolithophore formation,^42,158 the coccoliths (i.e., the individual plates) are nucleated intracellularly and then are exocytosed and assembled over the surface of the single-cell algae. Given these topics have been reviewed elsewhere,^3,16,57,157 we will focus our discussion on synthetic chemical processes below (Table 1).

6.2.2. Synthetic Process.

Cooperative integration^{50,51,75,159,160} underlies the synthesis of numerous organic–inorganic composites, such as the growth of ligand-protected nanocrystals and their ordered superstructures^75,157 (Figure 1a), the cross-linking of clay-dendrimer hydrogels²³ (Figure 4b), and the precipitation of mesostructured silica and transition metal oxides.^50,51 Composites formed via cooperative integration usually display molecularly integrated soft–hard interfaces with strong SH interactions (Figure 8). For example, in the block copolymer (e.g., Pluronic F127)-directed synthesis of mesostructured silica at low pH, the protonated hydrophilic segments of the polymer usually interact with the positively charged hydrolyzed silica oligomers through anion coupling (i.e., SH interaction). The hydrophobic moieties of the block copolymers self-assemble into micelle-like aggregates (i.e., SS interaction), while the silicate oligomers extend these aggregates into a 3D network (i.e., HH interaction) and form the precipitates (Figure 8b). Changes to the SH interaction though the addition of cosurfactants or other minor solvents can change the interfacial curvature and the resulting assembled structures (e.g., with a space group of $I m \bar{3} m$ , $F m \bar{3} m$ , or p6mm, or $I a \bar{3} d$ ; Figure 8b). Seamless integration is established via formation of a graded regime; portions of the hydrophilic block copolymer segments are incorporated inside the cross-linked silica matrix, making a transition from the soft copolymer core to the hard silica matrix. Hardening of the composite over time (i.e., additional HH interaction) can be accomplished by elongated sol–gel reactions and subsequent hydrothermal treatment.

Besides integration from molecular precursors, cooperativity among SS, SH, and HH interactions can be established through chemically induced organic phase incorporation which transforms existing inorganic aggregates (Figure 8c). This process is equivalent to the “swelling” of hard materials by soft components. One such example is the synthesis of a large range of MACMS.¹¹⁰ Synthesis starts with bulking crystals (e.g., black phosphorus, WeSe₂, SnSe, and NbSe₂) and molecular intercalation species (e.g., lauryl- or cetyl- or octadecyl-trimethylammonium bromide), and the final superlattices consist of alternating layers of monolayer 2D atomic crystals (e.g., phosphorene) and molecular layers (Figure 8c). The intercalation of bulk crystals and ultimate monolayer formation is controlled by the electrochemical potential (Figure 8c). To further expand the family of MACMS, one can envision incorporating photosensitive,^161-163 thermosensitive or other responsive molecules^162,164-168 into the soft phases of MACMS. Other intercalation chemistry can be used to prepare soft–hard interfaces, such as those based on polyoxometalates¹⁶⁹ and layered hydroxides.^170-173

Likewise, a transformation of existing soft aggregates (e.g., droplets and microgels) through their dynamic interactions with hard components can also yield cooperative syntheses, such as the spontaneous transformation from coacervates to vesicles by polyoxometalate-mediated protocell synthesis¹⁷⁴ and alloy droplet instability-induced growth of nanowire twin-plane superlattices^54,145 and sawtooth faceting.^53,147

6.2.3. Takeaway Message.

By satisfying SH > SS and SH > HH, cooperative integration achieves materials or devices that are only possible when the interfacial interactions (SH) are most dominant. The cooperatively integrated systems usually display internal architectures or functions that are not a simple sum of the constituent materials, so it is a preferred approach for creating new materials or functions. If both SS and HH become more dominant than SH (i.e., SS > SH, and HH > SH), we would achieve the deterministic assembly and a sum of the constituent’s properties and structures. If either SS or HH becomes more dominant than SH (i.e., SS > SH, or HH > SH), the soft (i.e., S) or the hard (i.e., H) phase becomes the stable template to guide the organization of the counterpart, yielding the directed organization.

6.3. Directed Organization

While cooperative integration produces composites de novo or from structurally different precursors, templated precipitation or cross-linking occurs at the predefined material surface or its interior (Table 1). In this class of chemical process, the strength of the three interactions usually follows SS > SH > HH if the soft materials are the template and define the growth (Figure 9a) or HH > SH > SS if the hard materials are the template (Figure 10a). The templates usually contain catalytic sites or heterogeneous nucleation sites for precipitation or cross-linking of the other components. Variation of the surface or the backbone chemistry of the templates can therefore yield different SH interfaces, producing a series of composites that preserve the template geometry (i.e., prescriptive or predictive⁵⁷). This is in contrast to cooperative integration, where a change in interfacial chemistry usually accompanies a change in the entire composite, even yielding emergent structures.⁵⁷ Templated precipitation or cross-linking underlies many biomineralization processes and biomimetic syntheses, such as those templated by polymer scaffolds,¹³⁹ DNA nanostructures,¹⁷⁵ or bacterial cells.¹⁴¹

Figure 9. — Interaction framework–directed organization from the soft phase can be verified with existing examples. (a) The interaction framework for the soft phase-directed organization. (b) In a naturally occurring example, the production of chitin-binding proteins (DgCBPs) and histidine-rich proteins (DgHBPs) based coacervates, the dynamic interaction of these proteins with the chitin-based soft template, and hydration/dehydration create the modulus gradient. Panels c–e are synthetic examples. (c) Schematic of the strategy enabling programmed deposition of electrostatic charges. Patterns of positive, negative, and neutral patterns allow for selective material deposition and preserve the prescribed charge in 3D materials. Reproduced with permission from ref 176. Copyright 2020 Springer Nature. (d) The optically patternable hydrogels triggered volumetric metal deposition and intensification at defined 3D locations. Upon volume shrinkage, mineralized networks such as a silver nanostructure and optical metamaterials can be generated. Reproduced with permission from ref.¹⁷⁷ Copyright 2018 AAAS. (e) Liquid alloy droplets or liquid metal droplets can template the growth of 2D inorganic sheets over their surfaces. The droplets also serve as the chemical reactors where at least one chemical element is extracted from the droplets for the sheet production. ΔG(S) is the free energy change for the solid skin. N_Au and N_Si are the area density of Au and Si atoms in the skin layer. ΔΓ(S) is the change in free energy upon taking N_Au + N_Si atoms from the reference solids. μ_Au(l) and μ_Si(l) are the chemical potentials of the atoms in the liquid.

Figure 10. — Interaction framework–directed organization from the hard phase can be verified with existing examples. (a) The interaction framework for the hard phase-directed organization. (b) De novo designed proteins form ordered structures over the mica surface. The upper schematic shows the model of DHR-mica interface, with repeats 1–3 (glutamate, Glu, side chains), repeats 4–6 (α-helical secondary structures), repeats 7–9 (full DHR backbone), repeats 10–12 (backbone and amino acid side chains), repeats 13–15 (all atoms), and repeats 16–18 (external protein surface). The lower AFM image shows one honeycomb lattice. The inset area is 200 nm × 200 nm. This panel is adapted with permission from ref 143. Copyright 2019 Springer Nature.

6.3.1. Naturally Occurring Process.

Directed organization processes often use organic matrices as templates to build soft–hard interfaces. For example, during coccolith plate formation, macromolecular recognition (i.e., the SH interaction) directs calcium ions to coccolith organic backbone sites (formed via SS) and promotes calcite deposition (formed via SH and HH).¹⁹ Bones grow and develop through cell-controlled local mineralization over a collagen fibril matrix,¹³⁸ a process which involves secretion and chemical transformation of precursor phosphate and calcium granules.³ The bone composite consists of type-I collagen fibrils (formed via SS) and evenly distributed hydroxyapatite nanoplatelets (formed via SH and HH). Active inhibitors (such as pyrophosphate (PPi)¹³⁸) throughout the soft tissues regulate bone growth by preventing unwanted mineral formation (i.e., suppressing HH).¹³⁸ When local mineralization over the collagen fibril matrix is desired, PPi is enzymatically degraded by osteoblasts to abolish the mineralization inhibition. Phosphate and calcium ions are then released from their reservoirs to promote nucleation and growth of hydroxyapatite nanoplatelets at the pre-established collagen sites and hardening of the bone tissues.

The directed organization occurs in many biological tissues that display a gradual and spatially defined hardening (Table 1). For example, in the biocomposite beak of the jumbo squid (Dosidicus gigas), there exists a 200-fold modulus gradient from the soft beak base to the hard distal rostrum^101,102 (Figure 9b). Through proteomics and transcriptomics, it was revealed that the chemical dynamics of chitin-binding proteins (DgCBPs), the histidine-rich proteins (DgHBPs), and water play major roles in this soft–hard transition. Specifically, DgCBPs and chitin fibers are biosynthesized and react into a hydrophilic scaffold (i.e., a process controlled by SS), producing the soft guiding component. As the jumbo squid ages, hydrophobic DgHBPs are expressed and condensed into coacervate droplets, followed by diffusion toward one end of the chitin-DgCBP scaffold. The His residues in the DgHBPs cross-link and dehydrate the chitin-DgCBPs scaffold¹⁰² (i.e., the action of SH and HH). The variation in cross-linking density and water concentration across different parts of the beak, therefore, produces the modulus gradient (Figure 9b).

6.3.2. Synthetic Process.

Biomimetic synthesis often follows the directed organization approach (Table 1). Soft templates (such as hydrogels, polymeric scaffolds, or liquid crystals⁵²) usually contain an accessible interior with active internal surface or domain chemistry to synthesize hard materials, with interactions following SS > SH > HH. Rigid materials can nucleate and grow over the internal surfaces of the soft materials, and ultimately develop into a soft–hard composite. For example, macroporous scaffolds can be made from naturally occurring polymers such as collagen or chitosan, or from synthetic materials with either designer backbones or surface modifications (e.g., with peptides screened from phage display^178-181 technologies). Synthetic nacre can be produced using a laminated chitosan matrix, made by freeze-drying, to form a chitosan-CaCO₃ composite.¹³⁹ To avoid matrix disintegration at soft–hard interfaces, the scaffold is stabilized by acetylation and transformation into β-chitin (i.e., enhancing SS interaction). To enable a sufficient scaffold/mineral precursor interaction (i.e., SH interaction), a peristaltic pump-driven circulatory system delivers mineralization precursors including Ca(HCO₃)₂, PAA, and Mg²⁺. Finally, silk fibroin infiltration and hot-pressing¹³⁹ are used to enhance the toughness and strength of the initial soft–hard interfaces. Besides porous scaffolds, nonporous lyotropic liquid crystals⁵² have also been used to grow mesostructured semiconductors and metals.

In a different example of synthetic directed organization, Zheng and co-workers used a 3D printing method to prepare a mosaic of distinct surface charge regions (Figure 9c), which then served as a substrate for deposition of multiscale functional materials through electrostatic attraction (SS > SH > HH). This method allows volumetric production of several active materials such as metals, ceramics, semiconductors, and colloidal systems into site-specific architectures for applications such as tactile sensing and internal wave mapping.¹⁷⁶

Hydrogels can respond to environmental changes through shape variations. This mechanism can be leveraged for dynamic actuation of inorganic components deposited within the hydrogel matrices or over their surfaces. Oran et al. used hydrogels¹⁷⁷ to direct nanostructured architectures made of metals and semiconductors with virtually any 3D geometry. The optically patternable hydrogels enabled volumetric deposition of hard material precursors at defined 3D locations. With volume shrinkage, the authors achieved nanoscale electronic components such as a 3D network of silver nanostructures and optical metamaterials. In this fabrication process, two steps involve soft–hard interfacial interactions (Figure 9d). The first step is the mineralization at defined hydrogel locations (S₁S₁ > S₁H > HH; S₁S₁ and S₁H focus on the soft template effect and interface-induced mineralization, respectively). The second step involves volume shrinkage of the hydrogel and densification of the inorganic components (S₂S₂ > S₂H > HH; S₂S₂ and S₂H focus on the hydrogel shrinkage and interfacial connection between the hydrogel and the mineralized species, respectively).

Soft templates are also found in micelles,^45,46 droplets,^{80,140,150,182} self-assembled monolayers,^183,184 and DNA origami structures,¹⁷⁵ where the external surface triggers interface formation, and the relative dominance of three interactions follow SS > SH > HH. For example, it was recently demonstrated that a class of delicate but well-controlled silica nanostructures could be synthesized by coating the surface of DNA frameworks with silicate oligomers.¹⁷⁵ Spatial resolution down to ~3 nm was achieved. Upon silica coating, the Young’s modulus of the composite increased 10-fold. Gallium-based eutectic alloys such as EGaIn (gallium + indium) and Galinstan (gallium + indium + tin) have been used as both the reactors and the templates¹⁴⁰ to grow oxide skins. The oxides that yield the lowest Gibbs free energy (ΔG_f) form the inorganic skins that encapsulate the alloy droplets (Figure 9e, right). This chemical process,¹⁴⁰ as well as many other surface solidification examples including the formation of an Au₄Si₈ sheet⁸⁰ over the surface of a Si/Au droplet (Figure 9e, left), exploits the minimization of free energy to form a soft–hard interface over an initially homogeneous soft substrate.

Biological cells can also serve as soft templates, for example, in the in situ synthesis of a CdS/bacterial hybrid for artificial photosynthesis at extracellular interfaces.¹⁴¹ When Cd²⁺ and cysteine are supplied to the CO₂-reducing bacterium M. thermoacetica, CdS nanoparticles precipitate over the bacterium surface in situ. The precipitation of CdS involves the HH, the living behaviors of bacterium M. thermoacetica provides the SS, and the interfacial recognition and nucleation of the mineral species depend on the SH. In this way, nonphotosynthetic bacteria are converted to photosynthetic ones via soft–hard interfaces, which integrate the light responsiveness of the CdS nanoparticles and the biosynthetic properties of the bacteria.¹⁴¹ Other examples include the use of polyphenol-based chemistry to assemble InP/yeast hybrids for light-driven regeneration of redox cofactors,¹⁸⁵ and the biogenic growth of iron sulfide over Shewanella for improved electron transfer in microbial fuel cells.¹⁸⁶

Studies of hard templates with well-defined lattice structures^142,143 have recently revealed significant new chemical insights into biomolecule/substrate interactions (i.e., SH interaction), particularly in the nucleation and growth of 2D frameworks, tilings, and chiral structures. In these cases, free-standing biomolecular systems alone do not produce the patterns, confirming HH > SH > SS (Figure 10a). In one recent example, cleaved MoS₂ substrates were used to study the nucleation mechanism in 2D array assembly.¹⁴² Tyr-Ser-Ala-Thr-Phe-Thr-Tyr peptides with acylated and amidated N and C termini formed 2D arrays along with three equivalent directions over freshly cleaved MoS₂ substrates.¹⁴² Molecular dynamics simulations, atomic force microscopy, and theoretical studies were employed to uncover the chemical interaction and dynamics at the peptides/MoS₂ substrates interface. This work verified the long-standing speculation that nucleation of 1D structures, in contrast to that of 2D and 3D structures, occurs without a free energy barrier. Another recent study reported that de novo designed helical repeat (DHR) proteins assembled over a geometrically matched K⁺ sublattice on muscovite mica (001) surfaces (Figure 10b)¹⁴³ (i.e., establishing the strength of SH). The concentration of the K⁺ sublattice (i.e., tuning HH) affected the packing density and forms (individual proteins or liquid crystals) of the attached DHR proteins (i.e., tuning the area of SH). Additional modifications of interprotein chemical interactions (i.e., tuning SS) at the end-to-end or trimeric interfaces maintained the DHR/mica interfacial binding (i.e., SH > SS) but allowed for the extension of the attached DHR proteins into nanowire or honeycomb arrays.¹⁴³

6.3.3. Takeaway Message.

In the directed organization, SS > SH > HH or HH > SH > SS should be satisfied. One of the phases, either the soft phase (i.e., S) or the hard phase (i.e.,H) serves as a template or a confined space to guide the nucleation, growth, or the dimension and size control of the counterpart phase. Depending on the context of the discussion, the SS/HH could be the intramolecular interactions that hold the constituent phase together, the restoring forces upon elastic deformation, or others.

A unique aspect of the directed organization is that the final material/device structure usually follows more closely to only one of the constituents, i.e., the template or the confined space. However, the final material/device structure would maintain that of both the soft and hard constituents in the deterministic assembly and should be different from both the soft and hard constituents in the cooperative integration.

Additionally, in the directed organization, the role of SH is still essential (i.e., it is not the least dominant interaction), because reactions or processes can only occur in the presence of the soft–hard interfaces. If the observed reactions or processes can happen without the interfaces (i.e., they are driven by purely SS or HH), they are not within the scope of this review.

6.4. Signal Transduction

The last functional chemical process, (bio)chemical signal transduction, predominantly applies to systems that contain biological components or stimuli-responsive polymers (Table 1). Signal transduction processes are distinct from cooperative integration and directed organization processes in several ways. First, effective signal generation, propagation, and even amplification usually require that multiple chemical, physical or biological processes are coupled together. This spatiotemporal complexity makes it challenging to define the relative dominance of SS, SH, and HH interactions. Second, in a signal transduction process, SS, SH, and HH interactions usually occur in temporal order, following a signal transduction sequence of either SS → SH → HH or HH → SH → SS. For example, HH-triggered processes (HH → SH → SS) involve final (bio)chemical changes to the soft components (i.e., SS) as a result of their interaction with the hard surfaces (i.e., SH; Figure 11a). Third, while the soft and hard components are usually tightly attached in cooperative integration and directed organization, they can be spatially distant in signal transduction processes as the transduction pathways can occur across a long distance and in a systemic manner.

Figure 11. — Interaction framework–signal transduction can be verified with existing examples. (a) The interaction framework for the hard phase-initiated signal transduction. (b) The formation of kidney crystal granulomas is an example of cooperative and multiple interfacial processes in a soft–hard hybrid generation. (c) A photoelectrochemical process at a coaxial silicon nanowire/neuron interface can elicit action potential propagation in cells. Adapted with permission from ref 86. Copyright 2018 Springer Nature.

6.4.1. Naturally Occurring Process.

Signal transduction involving SOM^27,89,90,94 and minerals is one example of an HH-triggered process. SOM is primarily formed through the decomposition of plant litter and its mineralization to inorganic carbon. Many SOM molecules can irreversibly absorb onto mineral surfaces (i.e., SH) to yield SOM-mineral complexes, with patchy domains (including aromatic carbon, aliphatic carbon, carboxylic/amide carbon, phenolic/pyrimidine carbon, or imidazole carbon) at the submicrometer level.²⁷ As the mineral sites are usually polar, strong electrostatic interactions occur between the minerals and the SOM molecules, triggering microbial signal transduction.⁹⁰ Specifically, absorption of the SOM molecules selectively promotes nitrogenous compound (particularly the amide) accumulation over mineral surfaces. The polar functional groups of the absorbed proteinaceous materials are exposed to the soil environment and recruit microbes from remote areas.

Renal crystal granuloma formation⁸² is also an HH-triggered process. Type 2 crystal-induced kidney injuries arise from intratubular crystal deposition due to a series of sequential events⁸² (Figure 11b) that involve the interfacial physicochemical behavior of the minerals, the mineralization regulators, and biochemical signaling pathways. Specifically, calcium oxalate (CaOx) crystal nuclei form upon supersaturation of CaOx in the urine. The CaOx crystals are phagocytized into the tubular cells, where they activate the TNF signaling pathway and produce crystal adhesion proteins (e.g., CD44 and annexin II) at the tubular cell membranes. The adhesion proteins next trigger attachment of more CaOx crystals and the crystal plug in the tubular lumen causes a tubular obstruction. Finally, cells proliferate on the rigid CaOx plug surfaces and the crystal plug translocates to the interstitial compartment and forms the crystal granulomas.⁸²

SS-triggered natural processes (i.e., SS → SH → HH) can involve cells that modulate the chemical or physical properties of inorganic materials. For example, electron transfer from Geobacter metallireducens GS-15 (i.e., SH) reduces minerals that contain Fe³⁺ or Mn⁴⁺ and modulates their redox activities in the soil environment (i.e., HH; ref 99). This mechanism and other redox microbiology processes are extensively reviewed elsewhere^99,187 and they are also the basis for many microbial fuel cell studies¹⁸⁷ in synthetic systems.

6.4.2. Synthetic Process.

HH-triggered synthetic signal transduction usually yields chemical or biochemical changes in soft phases (i.e., SS) due to interfacial delivery (i.e., SH) of chemical, mechanical,⁷¹ topographical,⁷¹ or electrical, electrochemical, and thermal stimuli^15,85-88 from hard phases (Figure 11a). Signal transduction can even be harnessed to stabilize special soft–hard interfaces, as in the synthesis of anisotropic hydrogels where photoexcitation of titanate or niobate nanosheets (i.e., HH) produces hydroxyl radicals to cross-link acrylamide derivatives.⁶³ Here, we focus on material-cell interfaces.^{15,59,85-88,117,188,189}

In orthopedic implant infections, microbial community formation (i.e., SS) at the implant surface (i.e., HH) involves bacterial recognition of adhesive matrix molecules (i.e., SH) that are preconditioned at implant surfaces.⁵⁹ These recognition moieties include collagen-binding adhesion proteins and fibronectin-binding proteins.⁵⁹ Engineering the implant surface topography and chemistry^71,104 to inhibit this biochemical interaction can generate an antifouling effect or even bactericidal activity.⁵⁹ Another example of a material-induced biochemical effect in cells is the recruitment of curvature-sensing membrane proteins over nanopillar substrates.^56,190 These chemical processes usually occur over a time scale of minutes to weeks.

While the above two cases denote slow signal transductions, high-speed (i.e., submillisecond to seconds) neural or cardiac modulations^{15,85-88,188,189,129} (i.e., SS) can be achieved by electrochemical or photoelectrochemical interfacial processes (i.e., SH) with semiconductor or metal devices (i.e., HH). For example, atomic gold-decorated coaxial p-type/intrinsic/n-type (PIN) silicon nanowires or gold nanoparticle-decorated PIN silicon nanomembranes were recently used as optically triggered biomaterials (Figure 11c). When interfaced with excitable cells, the atomic or nanostructured gold on the silicon surface facilitates a light-induced faradaic effect to depolarize cells and instigate action potentials.⁸⁶ The cleft between the silicon (or other modulation materials) and the isolated cells is usually very important, because local variations in ions or other chemicals could produce a larger modulation effect when in a confined volume.¹⁹¹

Finally, SS-triggered synthetic processes (i.e., SS → SH → HH) underlie essentially all electronic, electrochemical, or electromechanical sensing and energy conversion systems where the soft components are biological systems or stimuli-responsive polymers; these topics^15,164,168 will not be reviewed here.

6.4.3. Takeaway Message.

Different from the spatial aspect reflected in deterministic assembly, cooperative integration, and directed organization, the signal transduction focuses on the temporal aspect of the interaction framework. The signal can be triggered either by the soft phase or the hard phase. The soft–hard interface usually involves energy transduction, biosynthesis, or even signal amplification. In section 7, we will discuss the construction of devices or materials from the spatial aspect (i.e., using the language of deterministic assembly, the cooperative integration, and the directed organization), and analyze the sensing or modulation biointerfaces from the temporal aspect (i.e., the notion of signal transduction).

7. DISSECTION OF THE SOFT–HARD INTERFACES IN TISSUE-LIKE SYSTEMS

In section 6, we used diverse examples to validate the new soft–hard interaction framework. This section will apply this validated framework to dissect the soft–hard interfaces in several tissue-like systems such as bioelectronics and robotics. First, we will highlight a few biomechanical aspects of biological tissues and suggest opportunities for tissue-like synthetic materials.

7.1. Biomechanical Aspects of Biological Tissue

7.1.1. Mechanical Components for Building Living Soft–Hard Interfaces.

Biological tissues comprise spatially heterogeneous, mechanically rigid, hierarchical structures. Common ECM components include collagen, fibronectin, elastin, glycosaminoglycans (GAGs), laminin, tenascin, and vitronectin. Upon stretching or compression, the reorganization of collagen fibers contributes to dynamic and nonlinear tissue behavior, which activates a natural protection mechanism against tissue destruction. Elastin, another primary ECM component, is expressed in many organs, including the blood vessels, arteries, and lungs. Elastin displays high linear elastic extension, which contributes to the elastic deformation of the ECM. The elastin and collagen networks are highly entwined and work in unison to execute tissue mechanics.

Cells interact with the ECM through focal adhesion structures. They can remodel the ECM in response to mechanical, biochemical, and pathological cues. Cytoskeletal structures maintain and actuate cellular mechanics, while also helping the cell to adapt to the mechanics and dynamics of the ECM. For example, cytoskeletal intermediate filaments provide robust support against physical forces in the environment and help the cells resist mechanical stress. Highly cross-linked actin filaments undergo polymerization and depolymerization to remodel the cytoskeletal network in response to physical forces. Another set of critical cytoskeletal proteins, the microtubules, provides the molecular tracks for intracellular transport and displays the dynamic instability that shapes how cells respond to their mechanical environment.

7.1.2. Unique Behaviors in Biological Systems.

Biological tissues also exhibit complex rate-dependent and time-dependent mechanical responses, leading to “trainable behaviors.” These behaviors often rely on soft–hard interactions and the hierarchical structure of the tissue. They can originate from the viscoelasticity, poroelasticity, plasticity, or other nonlinear strain–stress correlations, of the tissue.

Viscoelastic mechanical behavior is typical in biological tissues. Viscoelasticity comprises instantaneous elastic behavior and time-dependent viscous behavior. Viscoelastic materials display properties common to both pure elastic solids and viscous liquids. Energy dissipation in a classic elastic solid is completely in-phase in response to the input energy; the energy is stored and ultimately released without loss during cyclic loading. In contrast, energy dissipation in a viscous fluid is out-of-phase with the input energy because the input energy is dissipated by internal friction and reconfiguration. The features of viscoelasticity fall between these two extremes. The in-phase response of the elastic property defines a storage modulus, and the out-of-phase response of the viscous property yields a loss modulus. The deformation frequency can affect the ratio of the loss modulus-to-storage modulus, contributing to the dynamic mechanical behavior of biological tissues.

ECM viscoelasticity arises by breaking and reforming weak cross-links, releasing entanglements, and unfolding proteins. Most cross-links in the ECM network are noncovalent bonds that can break, slide, or slip and thus dissipate energy under mechanical stimuli with dissociation rates fast enough for stress relaxation or material creep within the relevant time frame. Release of polymer entanglements and unfolding of ECM proteins, such as fibrin and collagen, also dissipates energy and contributes to the viscoelasticity of ECM. The viscoelastic cytoskeleton is involved in numerous subcellular activities such as actomyosin-based contractility and actin polymerization-enabled pushing. The interplay between the cells and the ECM, together with inorganic components, determines the diverse and highly heterogeneous tissue mechanics.

Tissue poroelasticity is caused by the flow of water into or out of the ECM and the cells. Water in the tissue moves into or out of the ECM under tension or compression; the resulting ECM poroelasticity produces energy dissipation. A slightly different mechanism underlies cell poroelasticity as the less permeable cell membrane prevents substantial water movement. Instead, cytoskeleton deformation causes transient pressure gradients and results in intracellular water movement, leading to the poroelastic effect. Since poroelasticity operates through the movement of water, the dissipation of shear stress through poroelasticity is much less than that through viscoelasticity.

7.2. Need for Advanced Tissue-Like Materials

Traditional soft materials such as silicone are frequently used in bioelectronics and robotics applications. While silicone is typically deemed biocompatible, its mechanical properties still do not match biological tissues’ viscoelasticity and strain-stiffening behavior. Recently, it was reported that the high surface stresses of silicone liquids and gels can stimulate the formation of multinucleated monocyte-derived cell masses, suggesting a foreign body response.¹⁹² Several methods have also been proposed to mitigate the biocompatibility issues associated with silicone through surface topography¹⁹³ and modulus¹⁹⁴ control. In this regard, there is still a need for more biocompatible and tissue-like soft materials. This can usually be achieved by using composites to construct the soft phase, as different soft components can help tune the SS interactions.

Biological tissues possess a unique ECM structure, wherein the ECM provides mechanical and biochemical cues for cells and regulates intercellular communication. Thus, composite materials with structures similar to those of cell-ECM may provide dynamic and nonlinear tissue-like properties. For example, Rogers et al. demonstrated deterministic methods to achieve low-modulus thin films with nonlinear stress–strain responses precisely matched to those of skin.¹⁹⁵ Skin-like composites were achieved by integrating the wavy filamentary network of polyimide and a breathable elastomer; these two components mimicked the collagen/elastin structure and the biological ground substance, respectively. This example utilizes SS interactions to achieve better tissue-like biointerfaces.

Gong and co-workers developed “self-growing” polymeric materials comprising an effective mechanochemical transduction element for responding to external repeated mechanical stress (Figure 12a). They demonstrated that muscle-like hydrogel materials can generate self-growth behavior; these materials could be used for soft robotics and intelligent devices.¹⁹⁶ Fang et al. developed tissue-like materials by incorporating cellular-scale starch granules in a hydrogel matrix¹⁹⁷ (Figure 12b). The authors revealed that mechanical stress on the hydrogel matrix could induce reorganization and training dynamics of the incorporated starch granules. These dynamic responses enabled various tissue-like behaviors, including anisotropy, programmability, and trainability. The starch-based tissue-like materials have broad applications, including implantable devices, medical-surgical tools, and humanized robotics. He and co-workers recently presented a novel strategy toward a multi-length-scale hierarchical hydrogel architecture that mimicks natural tendons (Figure 12c). In their work, the authors prepared poly(vinyl alcohol) hydrogels with engineered structural anisotropy. With a freezing-assisted salting-out treatment, they discovered micrometer-scale honeycomb-like pore walls and interconnected nanofibril meshes. These hydrogels demonstrate outstanding fracture toughness and fatigue resistance.¹⁹⁸

Figure 12. — Several strategies have been developed to produce soft tissue-like materials. (a) Self-growing muscle-like hydrogels. Reproduced with permission from ref 196. Copyright 2019 AAAS. (b) Granules-enabled tissue-like materials from granule/hydrogel composite. Reproduced with permission from ref 197. Copyright 2020 ELSEVIER. (c) A multilength-scale hierarchical hydrogel architecture that mimicks tendon was made by directional freezing and salting out methods. Reproduced with permission from ref 198. Copyright 2021 Springer Nature.

These recently reported tissue-like materials may serve as substitutes for conventional soft materials (e.g., PDMS) in future bioelectronics and robotics applications. In particular, these advanced tissue-like soft materials (i.e., an advanced soft phase, S) may help create more soft–hard composites (i.e., soft matrix-hard electronics) that follow the cooperative integration pathway, i.e., SH > SS, SH > HH, or the directed organization pathway, i.e., SS > SH > HH or HH > SH > SS, which is currently still lacking in bioelectronics and robotics.

7.3. Tissue-Like Deformable Electronics

In the previous section, we discussed a few of the components and properties that are unique to biological tissues. In the following sections, we will use the soft–hard interface framework (i.e., with notions of HH, SH, and SS) to present several biointerfaced materials and devices, in particular those related to bioelectronics and robotic systems (Table 2). We once again note that the definition of the soft and hard phases (i.e., S and H) that involve the same device depends on the context of the discussion. For example, the S can be the elastomer from a bioelectronic device if the whole device is treated as a soft–hard composite. Alternatively, the S can be the biological tissues if we discuss using the same bioelectronic device for sensing or modulation. In the following sections, we use S₁ and S₂, or H₁ and H₂, to indicate the differences if confusion may occur.

Table 2.

Classification of Tissue-Like Systems with the Soft–Hard Interaction Framework

Soft–hard interfaces	soft components	hard components	Soft–hard interaction framework
wavy Si nanoribbon/PDMS interface²⁰⁰	PDMS or other elastomers	Si nanoribbons	cooperative integration: SH > SS and SH > HH
complex 3D structures through compressive buckling²¹⁴	PDMS or other elastomers	diverse inorganic materials for electronic components	directed organization: SS > SH > HH
3D bendable nanoelectronics²¹⁶	SU-8	metal layers that yield strong residual stress	directed organization: HH > SH > SS
intrinsically stretchable electronics through nanoconfinement effect²¹⁷	elastomer phase	conjugated polymer	Cooperative integration: SH > SS and SH > HH
strain-insensitive intrinsically stretchable transistor array²¹⁸	elastomer substrate with patterned stiffness	conjugated polymer	deterministic assembly: SS > SH and HH > SH
stretchable electronics for neonatal intensive care²⁰³	elastomer and ionic liquid	metal and other circuit components	deterministic assembly: SS > SH and HH > SH
PVA/Au nanomesh prior to PVA dissolution²¹⁹	PVA nanofibers	Au layers	directed organization: SS > SH > HH
laser-engraved graphene-based sensor²²⁰	polyimide	graphene layers	directed organization: SS > SH > HH
self-healing electronics²²¹	self-healing elastomer (e.g., PDMS-MPU0.4-IU0.6)	carbon nanotubes and Ag nanowires	directed organization: SS > SH > HH
stretchable hydrogel electronics²²²	hydrogel	electronic and optoelectronic devices	deterministic assembly: SS > SH and HH > SH
conductive on-skin tape through direct drawing²²³	skin and polymer components of the ink	Ag flakes	directed organization: SS > SH > HH
macroporous nanoelectronic scaffold²¹²	SU-8	metal layers	deterministic assembly: SS > SH and HH > SH
macroporous nanoelectronic scaffold²¹²	SU-8	metal layers	directed organization: HH > SH > SS
neuron-like electronics²²⁴	SU-8	metal layers	deterministic assembly: SS > SH and HH > SH
neural recording from the neuron-like electronics²²⁴	neurons	metal recording device	signal transduction: SS → SH → HH
multilayered Si membrane⁸⁵	porous PDMS	Si nanostructured membrane	deterministic assembly: SS > SH and HH > SH
neuromodulation from the multilayered Si membrane⁸⁵	isolated neurons or neural tissues	Si nanostructured membrane	signal transduction: SS → SH → HH
organogenesis-directed formation of 3D bioelectronics²²⁵	growing cardiac organoid	Au and Pt electrodes	directed organization: SS > SH > HH
nanowire-polymer mesh¹⁸⁸	SU-8 mesh	Si nanowires	deterministic assembly: SS > SH and HH > SH
optically triggered cardiac training from nanowire-polymer mesh¹⁸⁸	cardiomyocytes and cardiac tissues	Si nanowires	signal transduction: HH → SH→ SS
phagocytosis-enabled nanowire/cell composite^85,226-228	mammalian cells	Si nanowires	cooperative integration: SH > SS and SH > HH
genetically targeted assembly of polymers²²⁹	neurons or neural tissues	conjugated polymers	directed organization: SS > SH > HH
phototactic actuation from a hydrogel composite pillar²³⁰	PNIPAAm	Au nanoparticles	signal transduction: HH → SH→ SS
ferromagnetic soft continuum robot²³¹	polymers and hydrogels	magnetic microparticles	signal transduction: HH → SH→ SS
magnetically steerable helical microswimmers²³²	mouse macrophages and primary splenocytes	helical magnetic microparticles	cooperative integration: SH > SS and SH > HH

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We will first look at the design and implementation of deformable electronics. Stretchability is a highly desirable electronics characteristic, especially when biointerfaces are concerned. For example, deformable electronics must accommodate the contraction of a muscle or the movement of the beating heart to faithfully and continuously map their electrical signals. Over the past few years, a collection of strain-tolerable stretchable electronics ranging from conductors to semiconductors have been developed.¹⁹⁹ Most of the designs involve SS > SH > HH (i.e., directed organization) or SH > SS/HH (i.e., cooperative integration) approaches, where SS denotes the physical or chemical processes in the deformable substrate that supports the electronic components (e.g., elastic deformation of PDMS), SH represents those at the substrate-electronics interface (e.g., interfacial covalent bonding between PDMS and Si), and HH represents those of the stretchable and deformable inorganic components (e.g., elastic deformation of Si ribbons). The general approach involves engineering designs that at least reduce the HH interactions (i.e., making the high modulus materials softer) through thickness reduction.

7.3.1. Stretchable Electronics through Geometric Engineering.

Stretchability in nonstretchable inorganic materials-based electronics is typically achieved through geometric engineering. When supported by an elastomeric substrate, structurally designed inorganic materials can be configured with the stretchability^41,200,201 needed to interface with, interrogate, and modulate the skin,^202-204 brain,^205-207 heart,²⁰⁸ bladder,²⁰⁹ and kidney.²¹⁰ In particular, the Rogers group has developed buckled thin strips of Si bonded in wavy structures to enable stretchable bioelectronics²⁰⁰ (Figure 13a). According to the SS/SH/HH framework, the wavy structures are obtained via a cooperative self-patterning process between the Si nanoribbons and the elastomer substrate upon compression, i.e., SH > SS, and SH > HH (Figure 13b). SH is most dominant because the wavy elastomer/Si interface cannot be produced by the elastomer or the Si itself, i.e., the interface is generated through a cooperative process. Notably, this engineering approach is not limited to specific materials, but generically applicable to many material candidates. In these examples, the focus is to tailor the HH (i.e., the bendability of Si nanoribbons) and SH (i.e., the bonding between the elastomer and the Si nanoribbons) interactions, such that SH > SS and SH > HH can be satisfied to yield ideal device mechanics or morphology at the biointerface. In this case, the thickness of the high modulus materials (i.e., H) is usually on the order of micron or submicron meters to minimize the dominance of HH in the interaction framework.

More complex stretchability involves a geometric transformation of planar 2D structures into various extended 3D architectures (Figure 13c). From the SS/SH/HH framework, the S₁S₁ > S₁H₁ > H₁H₁ approach (i.e., directed organization, Figure 13d) is preferred over the S₁H₁ > S₁S₁/H₁H₁ approach(i.e., cooperative integration), where S₁S₁ and H₁H₁ denote the mechanical interaction within the elastomer and the electronic components, respectively. The contact regions for S₁H₁ action (i.e., the bonding between the elastomer and the electronic elements) only serve as focal points and the final soft substrate morphology is the same as the original; this is different from the wavy structure mentioned in the previous paragraph²⁰⁰ where the soft substrate also largely deforms upon compression (i.e., SH is most dominant). In these 3D electronics designs, most of the stretchable interconnects are untethered from the elastomeric substrate such that out-of-plane deformations are enabled for enhanced stretchability.²¹³ Based on the SS/SH/HH framework, the untethered components help reduce SH such that SS > SH > HH can be satisfied easily.

Xu and Rogers reported the assembly of complex 3D structures through compressive buckling to enable routes to previously inaccessible 3D constructs.²¹⁴ The resultant electronics with open network architectures—we now define the entire device as a hard phase, H₂—can be integrated with biological systems (a new soft phase, S₂) in 3D volumetric states for biophysical studies. For example, Rogers and co-workers showed that the microfabricated 3D networks can serve as compliant, electrical/chemical/thermal/optical signal transduction interfaces (i.e., S₂H₂) to neural spheroids and assembloids²¹¹ (Figure 13c).

Moreover, to break the constraint of 2D integration, 3D integrated stretchable electronic devices were engineered by building electronic components layer-by-layer on elastomers and creating vertical interconnect accesses through the layers.²¹⁵ For example, Rogers and co-workers reported a vertical multilayer stack of arrays of electrodes and sensors that were integrated with catheters for minimally invasive cardiac therapy.²⁰⁸

7.3.2. 3D Bendable Electronics through Residual Stress Engineering.

Residual stress can be harnessed to enable the fabrication of complex 3D inorganic materials from their 2D layered precursors. A similar approach has been used in the production of 3D bendable nanoelectronics for intracellular probing.²¹⁶ For example, several publications from the Lieber group showed that residual stress (i.e., HH) from Cr/Pd or Cr/Au metal layers, is sufficient to cause deformation of the SU-8 substrate (i.e., SS) via Cr-enabled metal/SU-8 adhesion^{212,216,233-240} (i.e., SH interaction at the interface) (Figure 13e). In a typical fabrication, layers of SU-8 and metals were fabricated on ultrathin SU-8 polymer ribbons above a sacrificial layer (e.g., Ni or PMMA). The SU-8 substrate supported the nanoelectronic components, such as the semiconductor nanowires and the metal interconnects (e.g., Pd or Au). Upon the lift-off process, the interfacial stress between different layers (i.e., HH) would bend the supported nanoelectronic device components upward to a predictable height and angle.²¹⁶ This approach has also been used to fabricate a reticular scaffold-like nanoelectronic network for tissue engineering.²¹² These examples follow the HH > SH > SS approach, as the residual stress from the metal layer interfaces (i.e., HH) actuates the 3D structure formation (Figure 13f). In these cases, the SU-8 or other polymeric substrate must be bendable enough (i.e., SS is small or its role in this interaction framework is least dominant) to allow for the 2D-3D shape transformation.

7.3.3. Stretchable Electronics through Nanoconfinement Effect.

A different concept, self-assembly enabled nanoconfinement^241,242,217 (i.e., SH > SS, SH > HH), has been recently introduced for achieving intrinsically stretchable electronics. Organic thin-film transistors have been utilized extensively as wearable electronics. Notably, the Bao group utilized the nanoconfinement effect to achieve skin-like electronics based on polymer semiconductors with large stretchability, without compromising charge transport mobility. This was achieved via a nanoscale phase separation between the conjugated-polymer (i.e., the hard phase, H, as conjugated-polymers usually display larger modulus than that of elastomers) and the elastomer phase (i.e., the soft phase, S), where the interfacial interaction between the two phases (i.e., SH) was key to maintaining the nanostructured architecture.²¹⁷ Moreover, a photopatterning method for stretchable devices that can be fabricated on a mass scale with high device density on flexible substrates was also developed.²⁴³ Thousands of electronic circuits can be fabricated onto a stretchable platform. The high stretchability and minimal mechanical constraint of these intrinsically stretchable bioelectronics may enable many previously challenging biological applications, for example, morphing electronics that intimately interface with rapidly growing tissues during adolescent development.²⁴⁴

7.3.4. Stretchable Electronics through Interlayer Insertion.

A graded layer or buffer zone is established in many naturally occurring or synthetic soft–hard composites to promote seamless integration between mechanically different phases. This principle has recently been adopted in the design and application of stretchable electronics. Nam and co-workers showed that an-atomic-thick interlayer between a metallic film and a flexible substrate yields strain-resilient device conductivity (Figure 14a,b).²⁴⁵ Without the interlayer, the metallic film is brittle and undergoes an unperturbed straight fracture under tensile strain. With the interlayer, the metal layer displayed progressive tortuous fractures, showing a ductile behavior. Overall, the addition of the interlayer enhanced the strain-resilient electrical property with a resistance-locking behavior. The authors employed this method and realized a stretchable electroluminescent light-emitting device. In this example, the soft–hard framework switches from SS/SH₁/H₁H₁ to SS/SH₂/(H₂H₂ + H₁H₁), where H₁ and H₂ represent the metal and 2D materials, respectively. When the flexible substrate undergoes deformation (i.e., SS), the 2D interlayer bends readily (H₂H₂ is least dominant given the ultrasmall bending stiffness of the interlayer) through interfacial adhesion (i.e., SH₂) between the elastomer and the interlayer.

Figure 14. — Atomically thin-material interlayers enable strain-resilient electrodes. (a) Schematic of a fractured metal film on single-layer graphene forming the conductive and flexible electrode. (b) Conceptual plot comparing resistance in bare metal and metal-interlayer material in the function of applied strain. The metal-interlayer material possesses a large, stable “resistance locking” region. (c) Optical image of a flexible transistor array under 0% (left) and 100% (right) deformation. The varying concentration of cross-linkers in conductive polymers allows for the fabrication of the device with the prescribed stiffness allowing redistribution of strain to the nonactive areas. (d) Representative structure of a flexible transistor device. (SC, semiconductor material) Panels a and b were reproduced with permission from ref 245. Copyright 2021 Springer Nature. Panels c and d reproduced with permission from ref 218. Copyright 2021 Springer Nature.

7.3.5. Stretchable Electronics through Stiffness Patterning.

Very recently, Wang et al. developed a strain-insensitive intrinsically stretchable transistor array using an elastomer substrate with patterned stiffnesses²¹⁸ (Figure 14c,d). This method is similar to the creation of graded mechanical zones between soft and hard phases. The authors increased local stiffness in the elastomer by increasing the local cross-linking density. The high stiffness elastomer domains supported the active regions of the devices and reduced their strain to preserve device performance. This method is general and has been used to fabricate high-gain amplifiers for sensing electrophysiological signals. In this example, the soft–hard framework switches from S₁S₁/S₁H/HH to S₁S₁/S₂S₂/S₂H/HH, where S₁ and S₂ represent the soft and stiff elastomer, and H is the organic semiconductor device. In this case, S₁S₁, S₂S₂, S₂H, and HH interactions are related to material stretching or interfacial adhesion.

7.4. Tissue-Like Wearable and Epidermal Electronics

Living systems demonstrate highly sensitive, dynamic, and error-tolerant transmission of complex signals through two routes: bioelectrical signaling and biochemical signaling.²⁴⁶ Bioelectrical signaling is achieved through ion fluxes and cell membrane potential charges and presents in many parts of the body such as the brain, heart, and muscle.²⁴⁷ In these electrically active cells and tissues (neurons, cardiomyocytes, muscle cells, etc.), continuous recording and analysis of electrical signals can significantly help scientists understand biological processes and further direct the diagnosis and treatment of diseases.²⁴⁸ The past few decades have seen the development and wide application of bioelectronics devices for electro-signal recording and stimulation of brain, heart, and muscle. Bioelectrical signal recording devices typically include electroencephalography (EEG),²⁴⁹ electrocardiography (ECG),²⁵⁰ and electromyography (EMG).²⁵¹ One example of an electrical stimulating device is the transcutaneous electrical nerve stimulation (TENS) device, which reduces muscle spasms and pain using mild electrical current on a wide range of body parts, including the knee, neck, back, and pelvis.²⁵² Many of these devices require epidermal stimulation/recording electrodes to convert signals between ion flow in biological tissue and electron flow in the electronics at the skin-electrode interface. Impedance is significantly affected by a number of factors, including the degree of epidermis hydration and the electrode’s conformal contact onto the skin.^253,254

Epidermal electronics, electronic skin, or e-skin, refers to devices that can mimic and enhance the properties of human skin, such as stretchability, mechanical toughness, self-healing, and various sensing abilities.²⁵⁵ To function properly, the e-skin needs to satisfy the following criteria: sufficient adhesion to moving surfaces such as human skin, prosthetics, and robotics; strong mechanical properties that can withstand lateral tension, compression, stretching, and twisting to cope with diverse movements; self-healing ability that enables long-term durability; biocompatibility which ensures no harm to the human body when applied on the skin; and various sensing capabilities including sensing of temperature, physical stimuli, chemicals, and electrophysiological signals. In the numerous studies carried out in this area, most of the epidermal electronics were prepared following deterministic assembly (i.e., HH > SH, SS > SH, where H and S denote the electronics components and the soft matrix, respectively; Figure 15). We highlight a few below (Table 2).

Figure 15. — Flexible ultrasonic device has high mechanical resilience and can conform to the skin enabling remote sensing of blood pressure. (a) Schematic of the ultrasonic device with the array of piezoelectric rods embedded in an epoxy matrix. (b) Optical image of the device under strain showing its high conformability. (c) The device was made by the deterministic assembly, where the properties of the soft and hard phases are well-defined. Reproduced with permission from ref 256. Copyright 2018 Springer Nature.

Rogers and co-workers used stretchable electronics with advanced physiological measurements for neonatal intensive care.²⁰³ The devices provided continuous real-time monitoring of skin temperature, ECGs and photoplethysmograms (PPGs) that yielded measurements of a range of physiological parameters after on-board analysis. Xu and co-workers described a skin-attached ultrasonic device (Figure 15a,b) that was integrated into the conformal stretchable system to noninvasively and continuously capture blood pressure deep in the human body.²⁵⁶

Someya and co-workers reported ultrasoft and stretchable sensors for epidermal recording. Their conductive nanomesh structure consisted of PVA fibers and a thin-layer Au coating. The PVA/Au interface formation step followed SS > SH > HH, as PVA fibers (i.e., S) served as the template for the Au layers (i.e., H). The nanomesh system demonstrated promising tissue-like properties; it was inflammation-free, highly gas-permeable, and lightweight.²¹⁹

Xu, Wang, and co-workers reported a skin-like and integrated wearable sensor that can monitor a series of physiological health indicators, such as blood pressure, glucose, and caffeine, among other biomarkers. They assembled polymer composites and piezoelectric lead zirconate titanate (PZT) ultrasound transducers to achieve highly integrated wearable conformal sensors.²⁵⁷ Gao and co-workers reported a flexible and laser-engraved sensor for monitoring essential biomarkers, such as uric acid (UA) and tyrosine (Tyr). In this case, the device formation follows SS > SH > HH, as the graphene layers were locally converted from the polymer substrate (template). The authors demonstrated that the laser-engraved graphene-based chemical sensor (LEG-CS) could precisely and simultaneously detect UA and Tyr in human sweat. This indicator can help clinical doctors diagnose cardiovascular disease, gout, type 2 diabetes, and renal disease.²²⁰

Current and future tissue-like materials are also suitable for e-skin or wearable electronics applications. The mechanical toughness and stretchability of tissue-like materials can ensure high e-skin performance even on the most flexible joints of the body/robotics/prosthetics. The adaptability, trainability, and even memory effects of the materials would further enhance the performance of the e-skin or wearable electronics. The self-healing ability of tissue-like materials would enable fast and spontaneous healing upon breakage and promote a longer service time. Last but not least, tissue-like materials consist of biocompatible components. Therefore long-term use on human skin would not lead to concerns related to inflammation or other toxicities.

7.5. Self-Healing Tissue-Like Electronic Systems

Living organisms can heal themselves naturally upon injury or damage.²⁵⁸ The healing process includes the replacement of destroyed or damaged tissue with newly generated tissue. For example, when the skin is wounded, hemostasis is automatically triggered; platelets in the blood begin to stick to the injury site and form a clot to plug the broken vessel. The inflammation stage is then initiated to remove damaged and dead cells along with pathogens. The proliferation phase follows, and new tissue is grown. The entire healing process is wrapped up in the maturation phase, with the maturation and remodeling of collagen and cells.^259,260 The healing process of biological tissues can thus be summed up in four stages: formation of a primary clog to prevent further damage; removal of damaged/dead cells and pathogens; regeneration of new tissues; and realignment of new cells and collagen.

Various molecular interactions have been explored to realize self-healing behaviors in synthetic materials or devices (Table 2). Hydrogen bonds represent one type of reversible molecular force that can enable self-healing. When the material breaks, hydrogen bonds on the surfaces of the two pieces can automatically reform upon physical attachment. This creates a relinked structure that prevents further rupture, similar to the self-repair process in biological tissues. Many polymers possess extensive reversible Schiff bases that form a significant number of hydrogen and ionic bonds.^262-264 Ionic bonds can also automatically reform after breakage due to the reversible electrostatic interactions of oppositely charged ions.²⁶⁵ Following the formation of the primary linkage by hydrogen or ionic bonds on the surface, further hydrogen bonds and ionic bonds are established in deeper layers. As more bonds form, the two pieces gradually become more firmly attached, similar to the regeneration of cells or ECM in tissue. In nervous tissue regeneration, bioelectrical signal transduction along neuronal processes can be partially or even fully recovered during the self-healing process through the regrowth of axons and the re-establishment of synaptic connections. This serves as a biomimetic principle in many self-healing bioelectronics device designs.

The dynamic behaviors of molecularly designed polymers have enabled several advances in self-healing stretchable electronics and bioelectronics. For example, the Bao group developed a class of self-healing PDMS-based elastomers (PDMS-MPUx-IU1-x) using a one-pot polycondensation reaction of bis(3-aminopropyl)-terminated poly(dimethylsiloxane) with 4,4′-methylenebis(phenyl isocyanate) and isophor-one diisocyanate. This yields both strong and weak hydrogen networks within a single elastomer matrix, which can inhibit crack propagation along a defect and realize a highly stretchable, tough, and self-healing elastomer.²⁶¹ Using this PDMS-based elastomer (e.g., PDMS-MPU_0.4-IU_0.6) as the matrix, the Bao group embedded carbon nanotubes and silver nanowires on one side of the elastomer. They achieved self-healing electronics through the dynamic movement of the polymer chains upon physical contact with the broken parts of the devices²²¹ (Figure 16a,b). This example follows SS > SH > HH (Figure 16c), as the self-healing and nanostructure recontact (i.e., HH) is driven by the molecular motions of the elastomers (i.e., SS) and the elastomer-nanomaterials interactions (i.e., SH).

Figure 16. — Self-healing elastomers enable regeneration in electronic systems. (a) Schematic of hydrogen bonding combinations in the self-healable elastomer. Reproduced with permission from ref 261. Copyright 2018 Wiley. (b) Schematic of the autonomous resistance recovery in the carbon nanotube (CNT) network embedded in the self-healing polymer. (1) In the original state, the material possesses high conductivity due to interweaving CNTs in the network. (2) Mechanical damage (e.g., cut) increases the material’s resistance. (3) Mechanical stability and conductivity are regained after contact between cut parts is established. (4) Due to the dynamic nature of the polymer, the CNT network recovers, and the cut part is indistinguishable from the pristine material. Adapted with permission from ref 221. Copyright 2018 Springer Nature. (c) Directed organization following SS > SH > HH can be used to describe the self-healing behavior of the 1D nanostructure-incorporated PDMS elastomer.

7.6. Tissue-Like Smart Wound Bandages

Skin, the first barrier against infection, possesses excellent regenerative abilities that enable rapid healing upon injury.^266,267 However, certain underlying health conditions, such as diabetes and severe burns, can cause significant skin damage and overwhelm the skin’s regenerative ability, failing to complete the inflammation, proliferation, and maturation processes.²⁶⁸ Such chronic wounds with slow healing speeds are at higher risk of infection; infection further slows the healing rate and increases the risk of recurrence.²⁶⁹ Thus, chronic wounds are currently a critical cause of limb amputations and represent a global healthcare challenge.²⁷⁰ Traditional wound management schemes can be time-consuming, costly, and passive and effective treatments that can address the following three aspects are greatly desired: (i) wound coverage and a physical barrier against environmental pathogens, (ii) continuous monitoring and timely reporting of infections, and (iii) timely and rapid localized treatment with drugs or antibiotics. In the past decade, hydrogels have received significant attention as components of wound dressings as they can maintain a moist environment and provide a physical barrier to protect wounds.²⁷¹ Hydrogels can also be merged with infection monitoring and drug-release systems. Among potential biomarkers for wound condition monitoring, pH and temperature are the most widely used as they are the most intimately linked with infections and inflammations.²⁷² Drugs and growth factors can also be combined with treatment patches to promote the healing process. Drug carriers that are thermoresponsive,²⁷³ pH-responsive,²⁷⁴ and electric-responsive^275-277 have been developed and incorporated into hydrogel-based wound dressings for controlled drug delivery. It has also been reported that external electrical stimulation at the wound site promotes cell migration and proliferation, thereby accelerating the wound healing process.^278,279

Zhao and co-workers developed a dry double-sided tape (DST) made from a combination of biopolymers for tissue adhesion. Rapid covalent bonding with the amine groups on the tissue surface resulted in a soft–hard interface that further improved the robustness and adhesion stability of the DST. The DST rapidly (<5 s) adhered to wet tissues in vitro and in in vivo animal models.²⁸⁰ While these examples do not involve rigid materials, the DST system can be readily integrated with other electronics or optoelectronics for wound healing applications. Indeed, work from the same group demonstrated stretchable hydrogel electronics that served as a wearable bandaid, sensed temperature and light, and delivered medicine to the skin.²²² Conductive wires, semiconductor chips, light-emitting diodes (LEDs), and temperature sensors were embedded in the hydrogel to enable soft and stretchable electronic devices that function in the constantly changing environment of human body. In these examples, the soft–hard composite formation follows deterministic assembly, i.e., SS > SH, HH > SH as the hydrogels and the electronic/optoelectronic components are assembled manually.

Yu and co-workers developed an ultraconformal, customizable, and deformable thin layer conductive tape through direct drawing over a flexible mask (Figure 17a). In this case, the device assembly followed the directed organization pathway (Figure 17b), i.e., SS > SH > HH, because the local skin curvature and roughness defined that of the conductive device. The portable thin layer tape monitored electrophysiological signals during motion. Electrical stimulation from the conductive on-skin electrodes accelerated the healing of skin wounds²²³ (Figure 17c).

Figure 17. — Electronic inks enable the printing of electronic circuits directly on the skin and achieve unprecedented conformity. (a) A demonstration of an electronic ink deposition using polyimide stencil. (b) The material shows direct organization on the surface of the skin, following the directed organization pathway. (c) Drawn on skin electronics have been used to promote wound healing through electrical stimulation. The images show accelerated healing in the treated region (top part of the wound) compared to the untreated region (bottom part of the wound). Reproduced with permission from ref 223. Copyright 2020 Springer Nature.

For the development of precision medicine, the wound bandage can also be equipped with a closed-looped, dual drug release system that is both pH- and electrically responsive.²⁷⁷ For example, a pH sensor can trigger drug release to the wound when infections are detected.^281-285 Looking forward, smart wound bandages based on more tissue-like hybrid materials would provide protection to the wound, continuously monitor infection and inflammation, and apply localized treatment through “smart” drug release upon infection.

7.7. Tissue-Like Neural and Cardiac Devices

Both 2D and 3D bioelectronics have been developed for neural and cardiac interfaces. While biointerface studies over conventional 2D cell layers can provide a mechanistic understanding of signal transduction at device-cell interfaces, interrogating the 3D cellular architectures or 3D tissue surfaces could more faithfully recapitulate endogenous biological processes in living animals. This goal necessitates interfacing bioelectronics with 3D tissue organizations. This section focuses on the discussion of nano-enabled neural and cardiac bioelectronics (Table 2), given the large volume of reviews on topics related to other types of devices.^286-289

7.7.1. Neural Interfaces.

In the context of implantable neural probes for electrophysiology studies in live animals, evidence suggests that structural and mechanical differences between conventional neural probes and their cell targets can disrupt the native tissue and physiological processes^117,290-293 they are designed to interrogate, leading to neuronal loss and neuroinflammatory responses.^290,291 Disruption of the native tissue includes the proliferation of astrocytes and microglia and the depletion of neuronal nuclei and neurofilaments.^294,295 This foreign-body response acts as a barrier that precludes the device from stably recording and modulating neural circuits over a prolonged time.

Lieber and co-workers have been working toward minimally invasive neural probes by implanting 3D macroporous nanoelectronics with tissue-like flexibility into live animal brains and retinas to record electrophysiological activity.^233,296,297 The devices were fabricated through deterministic assembly, following S₁S₁ > S₁H, HH > S₁H where S₁ and H denote SU-8 and metal layers, respectively (Figures 18). These mesh probes are robust and time-invariant in their properties, exhibiting sustained stability with a long lifetime that allows for functional long-term single-unit recordings (signal transduction: S₂S₂ → S₂H → HH, S₂ and H are brain tissues and metal layers, Figure 18d) over 8 months and stable tissue interface over a year^298,299 (Figure 18c). To further address the baseline structural and mechanical distinctions between neural probes and their neuron targets, Yang and Lieber implemented a bioinspired neural probe design, neuron-like electronics (NeuE) (Figure 18a), where the key building blocks mimic the subcellular structural features and mechanical properties of neurons.²²⁴ NeuE is structurally indistinguishable and intimately interpenetrated with neurons, and exhibits a functionally stable interface with the neural tissue following implantation (Figure 18b). Moreover, the NeuE subcellular structural features were shown to facilitate association of endogenous neural progenitor cells (enhanced SH). Hong and Lieber reported tissue-like mesh electronics that formed seamless contact with the curved retina upon injection. The tissue-like mesh electronics have high-density channels that can stably record retinal ganglion cell (RGC) activity for at least 2 weeks, and substantial data revealed periodical circadian rhythms in RGC response.²⁹⁹ Overall, these tissue-like neural interface devices possess integrated tissue-like properties that enable high-quality electrical performance and long-term use without rejection.

Figure 18. — Bioinspired architecture allows to produce minimally invasive and biocompatible neuron-like electrodes. (a) Schematics of neuron-like electronics (NeuE) and neurons showing their structural similarity at the subcellular level and the network level (inset). NeuE is fabricated using photolithography. Submicrometer-thick and a few micrometer-wide neurite-like SU-8/gold/SU-8 interconnects are connected to platinum electrodes. (b) Two-photon fluorescence image of the interface between neurons (green) and NeuE (red) at 6 weeks after injection into the mouse brain. The white dashed circle indicates an electrode. (c) Bar chart showing the number of neurons recorded by each electrode as a function of postimplantation time. Bar colors are coded according to the brain regions spanning from the cortex (CTX) to hippocampal CA1 and CA3. (d) While device fabrication follows deterministic assembling, the signal transduction for neural recording starts from a different soft system, i.e., neural tissues. Panels a–c are reproduced with permission from ref 224. Copyright 2020 Springer Nature.

In addition, a mesh-like flexible silicon membrane allowed for conformal attachment and sufficient adhesion on the surface of a mouse brain, enabling control of brain activity in the somatosensory cortex (signal transduction: HH → S₁H → S₁S₁, S₁ and H are brain tissues and silicon membranes, respectively). ⁸⁵ In the device fabrication, Jiang et al. started with the synthesis of nanocrystalline p-type/intrinsic/n-type multilayered Si membranes, followed by microfabrication to achieve mesh-like geometry. Upon attaching the Si mesh onto a porous PDMS substrate, a soft–hard composite was made for neuromodulation. The device fabrication follows a deterministic assembly, i.e., HH > S₂H, S₂S₂ > S₂H; S₂ and H are PDMS and Si membranes, respectively.

7.7.2. Cardiac Interfaces.

Tian and Lieber developed 3D macroporous flexible nanoelectronics as bioactive synthetic scaffolds to electrically probe the biophysical and biochemical microenvironments of engineered cardiac tissues in 3D.²¹² The microporous nanoelectronics were fabricated either through deterministic assembly (SS > SH, HH > SH; where S and H denote the SU-8 and electronic components, respectively) via conventional lithography, or guided organization(HH > SH > SS, where S and H denote the SU-8 and the multilayered metals, respectively) via residual stress. The nanoelectronic scaffolds exhibited integrated sensory capabilities, enabling real-time monitoring of pH changes, electrical activity, as well as tissue dynamics in response to drugs. These “cyborg” cardiac tissues may be useful for in vitro drug screening. Moreover, 3D mapping and regulation of real-time action potential propagation in nanoelectronics-innervated tissues was achieved, and the dynamics and characteristics of action potential conduction in a transient arrhythmia disease model was also probed using this model.⁸⁴ In addition, these multifunctional electronics can be applied as cardiac patches to online monitor and regulate cardiac function.³⁰⁰ In addition to electrical recording, the integration of electroactive polymer enables on-demand electrical stimulation and drug release.

In a different approach, Liu and co-workers implanted prefabricated stretchable bioelectronics into developing cardiac tissues for tissue-wide electrophysiological mapping throughout organogenesis (Figure 19). Uniquely, the biomechanical forces produced during organogenesis drove transformation of the 2D stretchable devices into a tissue-integrated 3D layout, following SS > SH > HH where S is the growing cardiac organoid and H is the stretchable electronics (Figure 19b). The authors used this technique to chronically and systematically study the evolution, propagation, and synchronization of the bursting dynamics in human cardiac organoids during development (Figure 19c).

Figure 19. — Organogenesis can guide an organization of 3D mesh electronics. (a) Microscope images showing different types of deformations in the material throughout organogenesis. (b) Directed organization, following SS > SH > HH. Organoid development provided the driving force for the 2D to 3D device transformation. (c) Seamless integration enabled continuous chronic recording and analysis of spike dynamics revealed a change from an initially slow waveform to fast depolarization over the course of organogenesis. Reproduced with permission from ref 225. Copyright 2019 ACS.

Besides electrical sensing, several soft–hard composites have been developed for cardiac pacing or stimulation. For example, Parameswaran et al. assembled Si nanowires over SU-8-based polymer mesh (deterministic assembly, S₁S₁ > S₁H, HH > S₁H; S₁ and H denote SU-8 and Si nanowires, respectively) for optically triggered cardiac training and pacing (signal transduction, HH → HS₂ → S₂S₂; S₂ and H denote cardiomyocytes and Si nanowires, respectively).¹⁸⁸ The Si nanowires produced photoelectrochemical output upon laser scanning, whereas the SU-8 mesh supported the Si nanowires and contributed to device adhesion over the heart surface.

Future soft–hard composites for multifunctional cardiac interfaces may achieve both stimulation and recording of the electrical signals for heart disease therapies, enable energy harvesting from the heart’s beating motion,³⁰¹ possess tissue-like mechanical properties, and even display adaptability and trainability.

7.8. Tissue-Like Living Hybrid System

Biological components are adaptable and can produce signaling processes or molecules that are easily recognizable by cells and tissues. Bioelectronics are designed to interface with cells and tissues for electronic sensing or control of their electrical activities. While most efforts have focused on achieving bioelectronics that are nonliving or produced by physical methods, recent progress has involved the so-called “living bioelectronics”. In living bioelectronics, the cells and tissues are seamlessly integrated with the electronic materials, for example, through cellular internalization-enabled integration²²⁶ or cell-driven growth of the electronic materials²²⁹ (Table 2).

Zimmerman et al. showed that Si nanowires could be internalized by many mammalian cells such as endothelial cells, smooth muscles, and many cancer cell lines.²²⁶ With biophysical studies at the single-cell and ensemble levels, and with several drug assays, they found that the Si nanowire internalization was mediated by phagocytosis (Figures 20a,b). This composite formation follows SH > HH and SH > SS in the soft–hard framework, as the internalization (i.e., cell-Si nanowire interaction, SH) drives the translocation of nanowires from the extracellular to intracellular spaces (Figure 20c). Initially, the internalized Si nanowires were used to measure intracellular force dynamics. Jiang et al. later showed that the Si nanowires, when internalized by satellite glial cells (Figure 20d), could trigger calcium flux from the intracellular space of the glia upon optical excitation.⁸⁵ More importantly, the calcium signal from the glia elicited neuronal responses in a neuron-glia coculture (Figure 20e). In this example, the photothermal effect of the Si nanowires may have contributed to the observed calcium flux initiation, as the transient heating would cause poration of calcium storage organelles such as the endoplasmic reticulum and mitochondria. It is also possible that the transient heating could produce reactive oxygen species (ROS) that stress the glial cells and elevate their intracellular calcium concentration. As minimal cytotoxicity was observed in this intracellularly integrated bioelectrical control, Rotenberg et al. expanded this modulation method to the cardiovascular system with a myofibroblast/Si nanowire composite.^227,228 The cellular composite was made via phagocytosis of Si nanowires into the myofibroblasts (Figure 20f). Upon lasing, calcium signals were produced in the myofibroblast, which were transmitted to the adjacent cardiomyocytes. With controlled frequency of the light pulses, Rotenberg et al. achieved cardiac training and pacing^227,228 (Figure 20f).

Figure 20. — Silicon nanowires can be spontaneously internalized by different types of cells, enabling optical modulation of cells. (a) Confocal microscope image of nanowires (blue, scattering channel) internalized by HUVEC cells. The confocal sections confirm nanostructure internalization. (b) The plot of area overlap between the nanowires and cell bodies and confluence over time. Overlap fraction larger than confluence suggests that nanowires are actively taken up by the cells. (c) soft–hard interaction framework follows SH > SS and SH > HH. (d) DIC microscopy image of nanowires internalized by glial cells. (black arrow–nanowire, red arrow–glial cell, blue arrow–neuron) Confocal microscope fluorescence time series shows calcium wave propagation after stimulation of an internalized nanowire using a laser. (e) Transients of calcium dynamics in the cellular assembly after stimulation. (f) Schematic of myofibroblast-cardiomyocytes assembly, which can be used to study intercellular signaling and perform pacing of muscle cells. Panels a and b were reproduced with permission from ref 226. Copyright 2016 AAAS. Panels d and e reproduced with permission from ref 85. Copyright 2018 Springer Nature. Panel f reproduced with permission from ref 228. Copyright 2019 PNAS.

The above examples of living hybrid systems integrate cells with semiconductor materials through phagocytosis, and are potentially limited by the lack of cellular specificity. A more advanced method was recently demonstrated by Liu et al.,²²⁹ where living neurons were genetically modified to produce electrically functional semiconducting and insulating polymers over plasma membranes (Figure 21a). The authors used adeno-associated virus (AAV) vectors and expressed peroxidase enzyme on the extracellular surface of plasma membranes. In the presence of the polymer precursors (such as that for PANI conducting polymers), peroxidases catalyzed the polymerization at the cell surfaces and produced polymer networks. In this example of the formation of a living composite, the soft–hard framework follows SS > SH > HH, as peroxidase production from the targeted cells (i.e., SS) and plasma membrane-associated polymerization (i.e., SH) are the key drivers behind the formation of the cellular composite (Figure 21b). The authors used various electrophysiological and behavior studies and demonstrated that the genetically targeted assembly of polymers could remodel the cell membranes’ electrical properties and modulate the biological activities even in freely moving animals. This method has opened up many new opportunities in electroceuticals where the electronic materials or devices can be produced de novo from target cell populations. This method can seamlessly integrate cell-type-specific functional polymers into tissues or organs, which could help tissue regeneration if injury or disease occurs. In particular, the conductive polymers produced in this way can apply exogenous electrical fields for enhanced nerve regrowth or directed cell migration.

Figure 21. — Genetic encoding of the catalytic enzyme allows the synthesis of conductive polymers directly onto the cell membranes. (a) Genetic targeting allows modifying only a specific subset of neurons with the conductive polymer. Depending on the polymer conductivity, these materials can enhance or inhibit neuronal activity by modifying their membranes’ capacitance. Reproduced with permission from ref 229. Copyright 2020 AAAS. (b) Directed organization following SS > SH> HH. Genetically modified neurons can guide the growth of the conducting polymers. In this case, the modulus of the conducting polymers are orders of magnitude larger than that of cell membranes. This process can be homologous to biomineralization, although no inorganic materials are formed.

7.9. Tissue-Like Robotics

The human body is highly efficient and capable of performing precise and delicate movements. We have long been trying to replicate human biology to create autonomous forms that match or exceed human capabilities, whether carrying heavy loads or creating small inscriptions. Machines have entered the conversation for such a task, with computers and artificial intelligence and robots dominating technological advancements. Factories and other large industrial environments often utilize robots to increase labor efficiency while lowering costs. Now, scientists have set their sights on developing robots that can achieve more deft and irregular movements that would allow robotics to enter fields that require more intimate interactions with humans, such as biomedical settings.

As robotics rapidly advances, increasing attention has been directed toward the development of soft robotics. Typical robots are composed of hard, unyielding material that allows them to carry out preplanned tasks precisely. However, while rigid-bodied robots have excelled in a factory and industrial settings, they lack the capabilities to interface with softer living organisms in unstructured environments²⁹² safely. Soft robotics utilizes bioinspired soft technologies to allow for morphologically adaptive interactions with different tissue-based systems. Such robots may surpass natural organisms in performing multifunctional and versatile tasks, thereby opening the door for numerous applications. The healthcare field,³⁰² in particular, is a virtual gold mine for soft robotics opportunities, motivating the desire for high biocompatibility and precision in these robots.

Soft robotics design revolves around three primary components: an actuator, a sensor, and a control system. Unlike conventional hard robots, soft-bodied robots do not use a motor but instead rely upon a device, termed an actuator, that allows for the robot to move and interact with the surrounding environment. Sensors take in external stimuli, such as pressure or temperature, and communicate that information to the actuator. The control system works to enhance and direct the activity of the robot through the information taken in by the sensor. These systems replace the rigid modular systems that perform and determine movement in hard robotics to allow for irregular and delicate movements.³⁰³

In designing these key pieces, material selection becomes extremely important. Soft and compliant materials with elastic moduli similar to that of tissue in biological systems (i.e., tissue-like materials) are chosen as the primary components of the robot to work in conjunction with harder components (Table 2). In choosing these tissue-like systems, researchers can develop robots that can safely interact with humans and other living systems, such as in surgical procedures, drug delivery, or even child supervision. Typical soft material components that could yield the tissue-like behaviors include, but are not limited to, hydrogels, silicone-based elastomers, urethanes, hydraulic fluids, and gases. Many of these soft materials are elastic or viscoelastic with nearly infinite degrees of freedom, allowing for energy dissipation and stable motion under dynamic loading forces.³⁰⁴

Soft robotic actuators have been developed to move in response to various external stimuli, including magnetic, thermal, electrical, chemical, and light stimuli. The material composition can be anything from shape memory alloys to papers or fluids. The desired application often determines the material composition, with biomedical applications requiring materials activated in the near-infrared region (NIR) and environmental ones preferring sunlight-stimulated materials.³⁰⁵ Responsive hydrogels³⁰⁶ have gained great attention due to their adaptive responses to various stimuli in the environment, including pH, temperature, light, and glucose. These hydrogel materials have the potential to be used as bioelectronics platforms for sensing physiological changes in the environment. An example of this is the phototactic self-sustained hydrogel oscillator²³⁰ demonstrated by Zhao et al. Visible light directs and fuels the Au nanoparticle-embedded PNIPAAm (AuNP/PNIPAAm) hydrogel pillar by converting absorbed light to heat, and causing different portions of the hydrogel to expand or contract, resulting in oscillatory locomotion (Figure 22). In this example, the Au nanoparticles are the hard materials, and they produce a rapid and robust photothermal effect through plasmonic heating. The soft PNIPAAm hydrogels are thermally responsive and produce strong shape changes upon heating and cooling (Figure 22c,d). Given that robotic actuation follows a clear temporal sequence, the soft–hard framework would follow HH → SH → SS, where H and S are Au nanoparticles and the hydrogel and the heat-induced shape changes via the Au/PNIPAAm interfaces (Figure 22b).

Figure 22. — Phototactic gels allow the creation of oscillators that can be used for light-driven motion. (a) Schematic of the laser-driven oscillating pillar made of the phototactic gel. (b) soft–hard interaction framework. The signal transduction follows HH > SH > SS. (c) Mechanism of oscillating motion. Self-shadowing of the pillar’s inner surface creates out-of-equilibrium condition and negative loop driving the reciprocal motion. (d) Tip displacement and local temperature in the hinge during motion. The 90° shift between the curves is in agreement with the proposed motion mechanism. Reproduced with permission from ref 230. Copyright 2019 AAAS.

Magnetically responsive shape-programmable materials have also recently gained interest due to their ability to form small complex shapes that can be tuned over time via shifts in the direction, magnitude, and spatial gradients of the applied magnetics field.³⁰⁷ One of the primary issues that arise in designing magnetic soft robots lies in the large rigid nature of the embedded magnets necessary to generate deflection.²³¹ This requirement impedes any attempts at miniaturization and hinders the robot’s movement, thereby limiting the potential applications for magnetic soft robots. However, as fabrication technology has improved, scientists have developed magnetic soft robot designs^231,307,308 that circumvent these previous issues. Kim et al. reported a ferromagnetic soft continuum robot²³¹ for biomedical applications composed of polymer matrices embedded with magnetic microparticles (also following HH → SH → SS). The robot possessed self-lubrication, submillimeter scale, omnidirectional steering, and navigating capabilities that allowed it to move throughout complex environments.

Similar to the construction of living bioelectronics, magnetic particles can integrate with cells intracellularly. Yasa et al. showed that magnetically steerable helical microswimmers can be internalized by mouse macrophages and primary splenocytes.²³² They found that the macrophages and splenocytes interacted differently with microswimmers with different helix turn numbers (i.e., SH is highly dependent on the material design). This study suggests a new design perspective for biohybrid robotic implementation (following SH > HH, and SH > SS).

Sensors work to take in information on the environment surrounding the robot, whether that be light, pressure, or heat. Such technology is necessary to generate responsive and more bio-like soft robots. Silica-based distributed fiber-optic (DFOS) systems have been long used for their abilities to sense strain, pressure, temperature, and other external stimuli.³⁰⁹ However, classic DFOS systems are not compatible with soft robotics due to their inability to deal with large strains present in the soft materials. Bai et al. demonstrated a “stretchable” DFOS system³⁰⁹ that can sense locations, magnitudes, and modes of mechanical deformation through frustrated total internal reflection and absorption. They integrated the stretchable sensor into a soft glove to illustrate the capabilities and applications of such material in soft robotics. You et al. produced a deformable ionic receptor³¹⁰ that can take in and cleanly distinguish thermal and mechanical signals without interference. The receptor works through ion relaxation dynamics by regulating charge relaxation time to measure absolute temperature and normalized capacitance as a measure of mechanical strain. This sensor can then be integrated into a synthetic electronic-skin (e-skin)³¹¹ system for more biorealistic soft robotics.

As technology continues to advance in soft robotics, efforts toward more faithful biomimicry begin to emerge. With actuators and sensors producing more life-like movement and responsiveness, scientists have devoted time to developing soft robotics that addresses an array of different natural processes through the endowment of tissue-like materials with unique properties. Pikul et al. published work delineating a synthetic 3D textural camouflage design on stretchable surfaces.³¹² Inspired by the ability of cephalopods’ musculature to change the organism’s skin texture, this elastomeric structure can complete programmable transformations of 2D surfaces into 3D shapes depending on the spatial distribution of the mechanically heterogeneous components. Mishra et al. developed a hydrogel-based actuator³¹³ to maintain stable temperatures through autonomic perspiration, thereby endowing soft robotics with thermal homeostasis. Pena-Francesch et al. reported synthetic soft materials with self-healing capabilities³¹⁴ for both micro- and macro-scale mechanical damage. Such innovations will continue to emerge as tissue-like soft robotics progresses further toward and even surpasses conventional biological functionality.

8. OUTLOOK

Chemical processes at soft–hard interfaces are ubiquitous in both naturally occurring and synthetic systems. Despite the diversity of these interfaces, they share common principles (Figure 7) and can be understood in a coherent framework. Future chemical studies at soft–hard interfaces can benefit from interdisciplinary studies and advanced in situ characterization tools. Directions that may bring immediate impacts are suggested as follows:

8.1. Nature-Inspired Synthetic Interfaces

Naturally occurring soft–hard interfaces have evolved to display high performance, adaptability, and durability. They represent numerous sources of inspiration for synthetic soft–hard interface designs.

Nature displays remarkable gradient strategies for solving mechanical mismatch issues at heterogeneous tissue interfaces or transition zones. These chemical strategies involve graded mineralization¹⁶ (Figure 23a, 1), diffusion of coacervates for uneven cross-linking and hardening,^101,102 formation of interpenetrated and spatially varying (bio)polymer networks,¹⁰² or molecular linkers that display load-bearing moieties and asymmetric chemical bonding sites to both soft and hard components.⁹⁸ These strategies could be used in the synthesis of coating materials for stiff biomaterial implants, such as osseo-integrated prosthetic limbs.

Naturally occurring soft–hard hybrids demonstrate elegant solutions for mass transport (Figure 23a, 2) necessary for (bio)chemical processes in functional tissues or organs. For example, many natural materials, hard or soft, are porous or display loose interiors with dense surfaces to balance strength and flexibility.³ Alternatively, molecules and ions are efficiently delivered to the target domains through vascular networks.^3,315 These strategies could be considered when designing chemical reactions at synthetic soft–hard interfaces (e.g., tissue-bioelectronics interfaces).

In contrast to most synthetic methods, biologically derived interfaces are processed with natural molecules⁷ and ions under physiological conditions (i.e., at ambient or body temperature, at close to atmospheric pressure, in a salty solution, at close to neutral pH). If the biochemical pathways for these natural soft–hard interfaces are resolved, greener⁷ and more robust chemical synthesis (Figure 23a, 3) may be developed for future materials or devices where soft–hard interfaces are desirable.

Finally, naturally occurring soft–hard interfaces usually have unique homeostatic functions (Figure 23a, 4) and can precisely control the interfacial microenvironment. This control is maintained by autoregulatory feedback loops that are distributed hierarchically at multiple spatiotemporal scales. However, most synthetic soft–hard interfaces are unable to self-sense or self-modulate their behaviors. Future studies should incorporate these homeostatic principles, identify the essential physical and chemical elements, and seek new customizable and scalable materials synthesis that would enable autonomous interfaces.

8.2. Emergent Behavior in Synergistic Interfacial Mismatches

Soft materials commonly present motile, nonlinear, and emergent properties, which are sensitive to subtle external perturbations such as reaction-diffusion processes and dynamic defect flows.^1,5,29,30,57 The exclusive behaviors of soft materials are acquired at far-from-equilibrium conditions and later preserved by energy dissipation.⁵⁷ In particular, biological tissues and synthetic soft materials are adaptable,^{6,161,163,166-168,316} self-healing and regenerative,^{12,38,39,69,161} and symmetry-breaking.⁵⁷ In contrast, hard materials display constant and well-defined behaviors, and most of their properties are generated at or close to equilibrium conditions. These differences, in addition to the mismatches in mass transport and mechanical and electrical properties, represent several unique and exploitable design opportunities for future chemical studies.

The contrasting behaviors and properties of soft and hard components can be integrated for synergistic outputs (Figure 23b, left). In this way, the information-generating or information-processing capacity can be drastically expanded as both nonequilibrium/emergent (in the soft phase) and equilibrium/deterministic (in the hard phase) pathways can be adopted and seamlessly integrated (Figure 23b, left). For example, a soft–hard composite made of ferromagnetic nanowires with nematic order and a light-responsive polymer network can respond to both the optical and magnetic fields in a synergistic manner. This has yielded rapid walking of a robot made from this composite in water and delivery of cargo by rolling and light-induced geometry variation.³¹⁷

Soft and hard materials may display mismatched responses upon external stimulation, yielding energy dissipation domains⁶² or temporarily imbalanced potential energy (V) levels across soft–hard interfaces. This would produce a gradient of properties and a driving force (i.e., following F = −dV/dr, r is the distance) of transition, as long as there are finite interactions between the soft and hard components at the interfaces. The hard component sets unique boundary conditions for any behaviors and properties from the soft component as the former has, for example, a much smaller chemical diffusivity and much larger modulus. The gradient of properties or force at the interface can serve as a localized zone (Figure 23b, right) to trigger directional chemical diffusion or bias reaction pathways based on stimuli-responsive chemical groups.^{6,161,166,167,316} For example, the force produced by a temporary difference in electric potential across the soft–hard interface (e.g., through the photovoltaic effect of a cell-interfacing semiconductor¹⁵) can affect ions or molecules and their transition states that display polarity.³¹⁸ One can also appreciate this possibility when considering ion channels and plasma membranes. The ~5 nm thick plasma membrane separates intra- and extra-cellular domains, and it establishes an electrical field on the order of ~10⁷ V/m (ref 36). The ion channels and many other membrane proteins can sense this large electrical field,⁹¹ display conformational changes, and trigger cellular signaling. While fields, gradients, and forces occur in many other interfacial domains, such as the electrical double layer at the solid/liquid interface or the charge depletion region of a p-type/n-type silicon junction,¹⁵ these interfaces usually have limited room for exploring new chemical processes, as opposed to the multiple exploratory domains at soft–hard interfaces (Figure 5).

From an applications point of view, the mismatches at soft–hard interfaces can be leveraged for sensing and self-regulation, such as radiation or damage sensing (e.g., with a mechanophore³¹⁹ at the interface) and self-healing electronics (e.g., with mechanically induced free radical generation for polymerization¹⁹⁶).

8.3. Tissue-Like System for Human-Machine Interfaces and Neuromorphic Computing

8.3.1. Human-Machine Interface.

Human-machine interfaces (HMIs) form direct connections between the human body and external electronic machines. According to the direction of information flow, HMIs can be divided into two types: the input type HMI in which artificial signals are transmitted into the central nervous system,³²⁰ and the output type HMI in which signals generated in the human body are transmitted to external devices.³²¹ While both types of HMIs contribute greatly to the biomedical field, the output type HMIs, in particular, support restoration of mobility in paralyzed and amputated patients by bypassing lost or nonfunctional nerves and rerouting signals from the sensory-motor area in the cerebral cortex to manipulate prosthetic limbs. Thus, the key functions of output HMIs are to establish steady communication between the brain and signal-collecting electrodes, and between the HMIs and the electronic devices.³²² For example, Bao and co-workers demonstrated an artificial afferent nerve, which collects tactile information (1 to 80 kilopascals) from integrated pressure sensors. The artificial afferent nerve can convert the tactile information into a neuron-like action potential (0 to 100 Hz) achieved by ring oscillators. A series of ring oscillators can be used to construct a synaptic transistor that reflects complicated neural communication behavior, which is potentially useful for neurorobotics and neuroprosthetics.³²³ Rogers and co-workers developed a highly integrated millimeter-scale pressure sensor. The three-dimensional design of the small-scale pressure sensor integrates a hard metal strain gauge on a soft, thin polyimide substrate with a battery-free and wireless platform. The unique hard–soft integration is similar to biological tissue and can be imperceptibly laminated onto skin-prosthesis interfaces.³²⁴ Unfortunately, many currently developed HMIs display mechanical and biological mismatch with the human brain,³²⁵ which restricts signal transmission efficiency and long-term performance. Noninvasive approaches are one potential solution; however, the relatively low spatial resolution and low signal-to-noise ratio of noninvasive approaches are not suitable for practical control.³²⁶ Thus, the development of tissue-like, minimally invasive HMIs that can integrate with compliant brain tissue is greatly desired.

8.3.2. Neuromorphic Computing.

Driven by the dream of building an artificial manmade brain, the field of computer science and neurobiology have become intricately linked.³²⁷ As the science of neuromorphic computing has flourished in the past years, material scientists have been drawn to investigate technologies and materials that can ultimately apply neuromorphic computing to living organisms. Unlike conventional hard semiconductor materials, soft organic semiconductors may be used to craft neuromorphic devices for biomedical applications due to their biocompatibility, soft mechanical properties, and inherent sensitivity.³²⁸ Soft organic semiconductors have been used to craft organic neurons,³²⁹ memristive devices,³³⁰ neuromorphic electrolyte-gated transistors,³³¹ and organic synapses.³³² Salleo and co-workers reported an electrochemical neuromorphic organic device (ENODe) that demonstrates neuromorphic functionality in stretchable electronic systems. The working principle of ENODe is different from the traditional inorganic memristor. The mechanical flexibility of ENODe makes the memristor more brain-like.³³³ Santoro and co-workers reported a multifunctional biohybrid synapse, which coupled a conventional organic neuromorphic device to a PC-12 cell containing the dopaminergic presynaptic domain. They demonstrated a seamlessly integrated soft–hard interface-coupled tissue-like neuromorphic device and revealed the essential functions of dopamine during cell-to-cell transmission. On the other hand, dopamine can also manipulate synaptic plasticity. Therefore, the synaptic network produced by dopamine signaling created an unprecedented neuromorphic device.³³⁴ Biological synapses are the functional connection points between neurons, and are responsible for transmitting, storing, and processing information simultaneously through changes in synaptic weight, which is the foundation of learning and memory in the biological brain.³³⁵ Thus, synapse-like devices with seamlessly integrated soft–hard interfaces are critical to developing neuromorphic devices and essential for building up brain-like neuromorphic architectures. Current artificial neuromorphic devices include three-terminal organic synaptic transistors that consist purely of manmade organic materials³³⁶ and biohybrid synapses that combine living cells as presynaptic neurons and organic materials as postsynaptic neurons.³³⁴ These synaptic devices focus on mimicking the working behaviors of a biological synapse, such as long-term potentiation (LTP), short-term potentiation (STP), and spike-timing-dependent plasticity (STDP).³³³ However, they do not consider other biological features equally important for crafting implantable or wearable devices. In the future, emulation of the working behaviors of synapses will be achieved through the development of tissue-like neuromorphic computing devices with sophisticated soft–hard components.

8.4. Living Components

The field of living bioelectronics or robotics will continue to expand, especially given the need for cellular specificity, biocompatibility, and extended lifetime in the next generation of devices. Future living bioelectronics will strive for truly seamless integration of biological circuits. Recent progress in fabricating purely biological “electrodes” has suggested that this seamless integration is feasible. For example, implantable “living electrodes” were achieved by growing living cortical neurons in biocompatible hydrogel cylinders, where the axonal tracts were extended along the cylinder axis (Figures 24a, 24b). The “living electrodes” were highly biocompatible, could survive for a long time in the rat brain, and were capable of optical sensing and modulation of brain activity.³³⁷ While no electronics components were included in this first study, the barriers are low to integrate these “living electrodes” with electronic circuits for closed-loop and highly efficient neural interfacing.

Figure 24. — New strategies for tissue engineering would enable future biointegrated electronics and robotics. (a) Hydrogel-directed axonal growth was used to generate living electrodes. Optogenetically active neurons were grown in a cylinder. After implantation, rapid axonal overgrowth allowed integration of the probe with the tissue. Partial overgrowth of host axons with the probe allows for the recording of neuronal activity. (b) Schematic of two probes of various lengths which can be implanted to distinct depth allowing stimulation of different parts of the brain using LED arrays. (c) High throughput mechano-pharmacological screen was used to produce mesenchymal stem-cell-derived endothelial cells, which promote vascularization and perfusion in the tissue after transplantation. Both chemical and mechanical stimuli were applied to generate appropriate cell phenotype. Panels a and b were reproduced with permission from ref 337. Copyright 2021 AAAS. Panel c reproduced with permission from ref 338. Copyright 2021 Springer Nature.

Many living subcellular components are conductive, which could be explored for future bioelectronics and robotics. For example, with electrical stimulation from electrodes, Geobacter sulfurreducens biofilms can produce cytochrome OmcZ nanowires. These nanowires have 1,000-fold higher conductivity and 3-fold higher stiffness than the naturally occurring cytochrome OmcS nanowires.³³⁹ In this case, through signal transduction HH → SH → SS, Geobacter sulfurreducens produces living components that can be potentially integrated into a bioelectronics circuit for either sensing or modulation applications.

Future bioelectronics or robotics designs should also consider the incorporation of biological vasculature. This could potentially enhance biointegration and cell viability near the device surfaces. In tissue engineering and regenerative medicine, several methods have been proposed for enhanced vascular regeneration. For example, Baker and coauthors recently found that biomechanical conditioning, together with pharmacological treatment, can synergistically enrich the vascular regenerative potential of mesenchymal stem cells (Figure 24c). The proposed mechanism for the enhanced blood vessel growth involved elevated angiogenic paracrine signaling and an increase in the endothelia–pericytes population.³⁴⁰

9. CONCLUSION

This review proposed a soft–hard interaction framework to classify diverse structures and dynamic processes involved in soft–hard composites (Figure 7 and Video 1). A deeper and more coherent understanding of the interfacial processes at soft–hard interfaces will yield numerous opportunities for diverse research fields.

Given that soft–hard interfaces⁴² are functional across a wide range of length and time scales, future efforts should include formulating a theoretical framework or a dimensionless description that covers the mechanical, electrical, and chemical processes in the majority of soft–hard interfaces. Additionally, a ‘total synthesis framework³⁵ could be developed for coupling the soft–hard components into functional structures. Such a framework would explore chemoselective reactions, substituent effects, regiospecificity, and many other rational designs. Moreover, since soft–hard interfaces are usually embedded, new tools that can either directly visualize the interfaces nondestructively or provide 3D chemical or structural reconstruction of the interfaces are highly desirable. Correlative microscopy or spectroscopy may also contribute to a future understanding of the structures and dynamics of both the soft and hard phases near the interfaces.

With the notion of SS, HH, and SH, we hope that the new interaction framework can unify seemingly random and disconnected soft–hard examples in both the naturally occurring and the synthetic systems. Looking forward, we anticipate that this framework may point to new directions for formulating new soft–hard interfaces. For example, while there is numerous material and device progress in bioelectronics and robotics, most of these systems were constructed with a deterministic assembly approach. However, given that directed organization or cooperative integration makes most biological systems, we envision a significant increase in this effort by designing and implementing the SH interactions given their relative dominance in the interaction frameworks for directed organization or cooperative integration. Chemists, biochemists, and materials scientists would play a significant role in this regard, given they would help achieve new SH interactions involving coordinate covalent bonding, electrostatic interactions between charged groups, hydrogen bonding, or van der Waals interactions, etc.

Regarding biointerfacing materials, diverse approaches to building soft–hard interfaces have generated living composites or engineered tissues with moduli ranging from a few kilopascals to tens of gigapascals, with diverse stimuli-responsive or adaptable behaviors. Improvements in soft–hard interfaces may also yield new devices for clinical applications, such as engineered cartilage or meniscus. We believe that future chemical, mechanical, electrical, and medical exploration at soft–hard interfaces will yield breakthroughs in bioengineering, materials science, regenerative medicine, artificial intelligence, and future robotics design.

Supplementary Material

Supplementary video

Download video file^{(13.7MB, mp4)}

ACKNOWLEDGMENTS

We thank Karen Watters for scientific editing of the manuscript. This work is supported by National Institutes of Health (NIH NS101488), Army Research Office (W911NF2110090), Air Force Office of Scientific Research (FA9550-20-1-0387), the US Office of Naval Research (N000141612958), and the National Science Foundation (NSF CMMI-1848613, DMR-2105321). A.P. acknowledges support from the NSF MRSEC Graduate Fellowship (NSF DMR-2011854). B.T. and X.Y. would like to dedicate this article to Dr. C. M. Lieber for his mentorship.

Biography

Yin Fang is currently an assistant professor in the School of Chemical and Biomedical Engineering at Nanyang Technological University. He received his Ph.D. in chemical engineering from the University of Florida in 2016. His Ph.D. research with Professor Peng Jiang focused on self-assembled photonic crystals, shape memory polymers, smart windows, and plasmonic sensing in photonic structures. He worked with Professor Bozhi Tian as a postdoctoral scholar in tissue-like hybrid systems and bioelectronics at the University of Chicago. His current focus is on soft materials, tissue-like electronics, shape memory materials, and soft medical robotics for therapeutic applications. Professor Fang’s accolades include the MRS graduate student award (2015) and Chinese government award for outstanding self-financed Ph.D. students (2016). In 2020, his work was selected as the top 7 stories in the chemistry department. Meanwhile, his work was also selected as “ten cool research stories” on Uchicago news at the University of Chicago.

Xiao Yang received her Ph.D. degree in Chemistry from Harvard University in 2020 under the supervision of Professor Charles M. Lieber. Currently she is a postdoctoral fellow in the laboratories of Professor Sergiu P. Paşca. and Professor Bianxiao Cui at Stanford University. She is interested in developing novel bioelectronics for neural interfaces.

Yiliang Lin received his B.S. in polymer science and engineering in 2013 from Zhejiang University in China. Later he obtained his Ph.D. in chemical engineering in 2018 from North Carolina State University in the U.S.A., under the guidance of Prof. Michael D. Dickey and Prof. Jan Genzer. He is currently a postdoctoral fellow in the lab of Prof. Bozhi Tian at the University of Chicago in the U.S.A. Yiliang’s research interests include designing and engineering soft matter or soft–hard composites for bioelectronics and hybrid living materials.

Jiuyun Shi received his B.S. in chemistry in 2018 from Zhejiang University in China. Currently, he is a graduate student at the University of Chicago supervised by Prof. Bozhi Tian. His research involves the development of novel functional hydrogels for biomedical regenerative therapy and bioelectronics.

Aleksander Prominski received his B.Sc. from the University of Warsaw, Poland, in 2016 and M.Sc. from the University of Chicago in 2017 He entered the Ph.D. program in Chemistry at the University of Chicago in 2016. His current research involves the synthesis of new nanostructured materials for application in biomodulation and the development of machine-intelligence-assisted bioelectronic systems.

Clementene Clayton is an undergraduate student at the University of Chicago in the Chemistry and Molecular Engineering Departments. Her research centers around biomimetic materials, namely those involving hydrogels and starch-based systems. Her family currently resides in Sydney, Australia, but she is originally from the California Bay Area.

Eleanor Ostroff is an undergraduate student majoring in Chemistry at the University of Chicago. In the Tian Lab, she works on nanostructured materials and their applications to biomodulation. She is interested in pursuing a career in medicine and hopes to continue researching the biomedical applications of nanomaterials as her career progresses.

Bozhi Tian received B.Sc. and M.Sc. degrees in chemistry from Fudan University Shanghai, China, and A.M. and Ph.D. degrees in physical chemistry from Harvard University in 2010. His current research focuses on the semiconductor-enabled understanding of subcellular biophysics, as well as studies of dynamics at soft–hard interfaces.

Footnotes

Supporting Information

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.chemrev.1c00365.

Video illustrating the concept of the soft–hard “interaction framework”, where both the spatial and temporal aspects are discussed (MP4)

The authors declare no competing financial interest.

Contributor Information

Yin Fang, The James Franck Institute, University of Chicago, Chicago, Illinois 60637, United States; Present Address: School of Chemical and Biomedical Engineering, Nanyang Technological University, 70 Nanyang Drive, Singapore 637457, Singapore.

Xiao Yang, Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States.

Yiliang Lin, The James Franck Institute, University of Chicago, Chicago, Illinois 60637, United States; Department of Chemistry and The Institute for Biophysical Dynamics, University of Chicago, Chicago, Illinois 60637, United States.

Jiuyun Shi, The James Franck Institute, University of Chicago, Chicago, Illinois 60637, United States; Department of Chemistry and The Institute for Biophysical Dynamics, University of Chicago, Chicago, Illinois 60637, United State.

Aleksander Prominski, The James Franck Institute, University of Chicago, Chicago, Illinois 60637, United States; Department of Chemistry and The Institute for Biophysical Dynamics, University of Chicago, Chicago, Illinois 60637, United States.

Clementene Clayton, Department of Chemistry, University of Chicago, Chicago, Illinois 60637, United States.

Ellie Ostroff, Department of Chemistry, University of Chicago, Chicago, Illinois 60637, United States.

Bozhi Tian, The James Franck Institute, University of Chicago, Chicago, Illinois 60637, United States; Department of Chemistry and The Institute for Biophysical Dynamics, University of Chicago, Chicago, Illinois 60637, United State.

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PERMALINK

Dissecting Biological and Synthetic Soft–Hard Interfaces for Tissue-Like Systems

Yin Fang

Xiao Yang

Yiliang Lin

Jiuyun Shi

Aleksander Prominski

Clementene Clayton

Ellie Ostroff

Bozhi Tian

Abstract

Graphical Abstract

1. INTRODUCTION

1.1. Underexplored Dynamics at Soft–Hard Interfaces

Figure 1.

1.2. Definition of “Soft” and “Hard” and Its Relevance to Materials Chemistry

Figure 2.

1.3. Soft–Hard Interfaces Are Common to Both Living and Nonliving Systems

Figure 3.

1.4. Harnessing Soft–Hard Interactions to Improve Composite Performance or Signal Transduction

1.5. Aims of This Review

2. LENGTH AND TIME SCALES

Figure 4.

3. UNIQUE (BIO)CHEMISTRY AT SOFT–HARD INTERFACES

3.1. Utilizing the Confined Space to Mitigate Interfacial Mismatches

3.2. Utilizing Buffer Zones to Mitigate Interfacial Mismatches

3.2.1. Additive Strategy—Linkers or Deposits at the Interface.

3.2.2. Spatial Strategy—Graded Regime.

3.2.3. Temporal Strategy—Hardening the Composite over Time.

3.3. Leveraging Interfacial Mismatches for New Capabilities

3.4. Rationale for Exploring New Chemistry at Soft–Hard Interfaces

Figure 5.

4. TOOLS TO STUDY INTERFACIAL PROCESSES

Figure 6.

Table 1.

4.1. Challenges in Characterizing Soft–Hard Interfaces

4.2. Studying the Dynamics of Interfacial Chemical Processes

5. FRAMEWORK TO CLASSIFY SOFT-HARD INTERFACES

Figure 7.

5.1. Spatial Aspect of the Interaction Framework

5.1.1. Deterministic Assembly.

5.1.1.1. Relative Dominance: SS > SH and HH > SH.

5.1.2. Cooperative Integration.

5.1.2.1. Relative Dominance: SH > SS and SH > HH.

5.1.3. Directed Organization–Initiated from the Soft Phase.

5.1.3.1. Relative Dominance: SS > SH > HH.

5.1.4. Directed Organization–Initiated from the Hard Phase.

5.1.4.1. Relative Dominance: HH > SH > SS.

5.2. Temporal Aspect of the Interaction Framework

5.2.1. Signal Transduction–Initiated from the Soft Phase.

5.2.1.1. Temporal Sequence: SS → SH → HH.

5.2.2. Signal Transduction–Initiated from the Hard Phase.

5.2.2.1. Temporal Sequence: HH → SH → SS.

6. PROOF FOR THE INTERACTION FRAMEWORK

6.1. Deterministic Assembly

6.2. Cooperative Integration

Figure 8.

6.2.1. Naturally Occurring Process.

6.2.2. Synthetic Process.

6.2.3. Takeaway Message.

6.3. Directed Organization

Figure 9.

Figure 10.

6.3.1. Naturally Occurring Process.

6.3.2. Synthetic Process.

6.3.3. Takeaway Message.

6.4. Signal Transduction

Figure 11.

6.4.1. Naturally Occurring Process.

6.4.2. Synthetic Process.

6.4.3. Takeaway Message.

7. DISSECTION OF THE SOFT–HARD INTERFACES IN TISSUE-LIKE SYSTEMS

7.1. Biomechanical Aspects of Biological Tissue

7.1.1. Mechanical Components for Building Living Soft–Hard Interfaces.

7.1.2. Unique Behaviors in Biological Systems.

7.2. Need for Advanced Tissue-Like Materials

Figure 12.

7.3. Tissue-Like Deformable Electronics

Table 2.

7.3.1. Stretchable Electronics through Geometric Engineering.