MICROWAVE BIOSENSORS for SINGLE-CELL DIELECTRIC CHARACTERIZATION: a REVIEW

Abstract

The dielectric property of biological cells exhibits a rich frequency-dependent behavior that is related to the cellular structure, function and molecular makeup. Assessing the dielectric properties of single cells offers a label-free and non-invasive method for disease diagnosis, cell identification, and monitoring of cellular structure and function. This article reviews the fundamentals and applications of microwave biosensors for single-cell analysis, with a focus on how electromagnetic interactions across the α-, β- and γ-dispersion regimes can be leveraged to extract cellular parameters such as membrane capacitance, cytoplasmic conductivity, and intracellular hydration. We begin by discussing the physical principles of dielectric dispersion and the equivalent circuit models in biological cells that underpin microwave-based sensing. We then examine major classes of sensing platforms, including transmission line sensors, capacitive electrode arrays, and resonant structures, highlighting their design strategies, strength and weakness, and applications in single-cell sensing. The role of dielectrophoresis (DEP) as both a manipulation tool and a sensing mechanism is also discussed, particularly in the context of hybrid systems that combine low-frequency trapping with high-frequency dielectric readout. Together, these technologies represent a convergence of microwave engineering, biophysics, and microsystem design to enable high-resolution, real-time interrogation of single cells. After describing the theoretical foundations with recent experimental advances, we provide a perspective on the design of next-generation dielectric biosensors tailored to the demands of single-cell diagnostics, drug screening, and functional phenotyping.

I. INTRODUCTION

Electrical methods have long served as powerful tools for probing the structure and function of biological systems. From early electrophysiological experiments to modern microchip-based impedance sensors, the ability to interrogate cells through their electrical properties has enabled numerous advances in physiology, clinical diagnostics, and biotechnology. Central to this field is the understanding that biological materials interact with electric fields in a frequency-dependent manner, a principle formalized through the foundational work of Schwan and others in the mid-20th century [1]. This behavior is governed by a combination of cellular structures—membranes, cytoplasm, and organelles—that give rise to distinct dispersion phenomena across the electromagnetic spectrum [2], [3].

At low frequencies (typically below 10 kHz), biological responses are dominated by the motion of ions at the cell–electrolyte interface, leading to what is known as α-dispersion. This regime primarily reflects counterion diffusion and is sensitive to the properties of the suspending medium and the outer membrane surface of cells. As the frequency increases into the β-dispersion range (~10 kHz – 100 MHz), Maxwell–Wagner interfacial polarization develops across the cell membrane, arising from charging of the cell membrane capacitance. While the dielectric properties of the lipid membrane dominates in this range, cytoplasmic contribution could be observed at higher frequencies of the β-dispersion range [4], [5].

At even higher frequencies, in the δ- and γ-dispersion ranges (~100 MHz – 100 GHz), the electric field penetrates the cytoplasm and interacts with intracellular water, macromolecules, and organelles, revealing dielectric relaxation processes within the cell interior. These high-frequency interactions provide rich information about the biochemical and structural organization inside the cell [6], [7], [8]. These high-frequency interactions offer a label-free, non-invasive, and real-time means to assess intracellular compartments at subcellular resolution. Recent developments in sensor engineering, microfabrication, and instrumentation have facilitated the design of miniaturized, integrated systems capable of performing broadband or resonant dielectric measurements at the single-cell level [9]. Such systems have demonstrated utility across a wide range of applications, from cell viability screening and differentiation analysis to intracellular hydration monitoring and drug response assessment [9], [10].

Single cell dielectric analysis is attracting both scientific and practical interest. Conventional dielectric measurements based on tissues or a group of cells average the cell responses and obscure the intrinsic heterogeneity in the cell population that often drives biological function and disease progression. In cancer, for example, rare drug-resistant cells can dictate treatment outcomes; in stem cell biology, subtle dielectric differences reveal lineage commitment long before conventional biomarkers appear [11]. Single cell sensing enables the detection of these unique subpopulations, providing insights that bulk assays cannot resolve. By isolating the dielectric fingerprint of individual cells, researchers can capture the dynamic interplay between membrane integrity, cytoplasmic composition, and organelle states with unprecedented resolution. This capability is particularly valuable for personalized medicine, where understanding patient-specific variability at the cellular level can inform diagnostics, therapeutic monitoring, and drug development [12].

This review aims to provide an overview of techniques for single-cell dielectric analysis, focusing on the microwave frequency range. While microwave biosensing has been the topic of several recent reviews [13], [1], [14], this paper intends to focus on the single-cell aspect that have seen a fast development in the past decade. We begin by outlining the physical foundations of dielectric dispersion in biological matter, introducing the theoretical models that describe how electric fields interact with cells across different frequency ranges. These concepts form the basis for interpreting electrical signatures in terms of cellular structure and composition. We then survey the sensor technologies that leverage these dielectric principles, highlighting the diversity of approaches currently used in the field. This includes broadband and resonant structures, planar and volumetric sensor geometries, and both propagating and localized field configurations. Volumetric geometries such as substrate-integrated waveguides and folded waveguide resonators confine electromagnetic energy within three-dimensional cavities, enabling high-Q operation and deep field penetration for single-cell dielectric sensing. These will be discussed in the resonant structures section. Next, we explore enabling techniques such as dielectrophoresis (DEP), which play a crucial role in positioning and manipulating cells within the sensing environments. While rooted in classical electrokinetic, DEP remain essential for modern dielectric analysis and are increasingly integrated with high-frequency measurement systems. We also discuss the practical challenges that arise when applying microwave techniques to single-cell analysis. These include the weak and transient nature of cellular signals, the need for precise positioning and stability of individual cells within localized electromagnetic fields, and the critical role of calibration and de-embedding strategies to separate minute cellular responses from parasitics and environmental effects. Complementary approaches such as optical tracking, microfluidic focusing, or DEP-assisted manipulation are highlighted as essential to ensure reliable detection and reproducibility. Finally, in the Conclusion, we emphasize how advances in resonator design, multimodal integration, and machine learning are transforming microwave biosensors into intelligent platforms capable of extracting rich dielectric fingerprints. These developments underscore both the challenges and the promise of translating single-cell dielectric analysis into practical diagnostic and therapeutic tools.

Together, these developments represent a convergence of electrical engineering, biophysics, and microtechnology, offering unprecedented access to the dielectric state of individual cells. By capturing dielectric fingerprints that reflect both membrane-level and intracellular dynamics, microwave biosensors are poised to play a central role in the future of single-cell diagnostics and functional phenotyping.

II. FOUNDATIONS AND PRINCIPLES

The interaction between biological matter and electromagnetic fields is fundamentally frequency dependent, a concept first established by Schwan [1]. This frequency dependence gives rise to characteristic relaxations and resonances in a material’s complex permittivity, manifested as dispersions in both permittivity and conductivity. FIGURE 1(a). is a schematic of the dependence of permittivity and conductivity of biological matter on the frequency: the relative permittivity ε′ indicates the sample’s ability to store electric energy, closely tied to its structural polarizability, while the specific conductivity σ indicate the material’s ability to conduct electric current, which is determined by the carrier mobility and transport mechanisms. Multiple dispersions are observed in FIGURE 1(a). Each dispersion corresponds to specific polarization mechanisms within biological structures, from ionic movement at low frequencies and dipolar polarization in intermediate frequencies to molecular rotation and vibration at high frequencies. The dispersion principles in biological samples have been explained in a few previous review articles, and we focus the explanation of dispersion on the single cell level in the text below [15], [16].

FIGURE 1.

Frequency-dependent dielectric dispersions in biological cells and the equivalent circuit model. (a) The relative permittivity (ε′, blue) and specific conductivity (σ, pink) as functions of frequency across a wide spectral range. Three primary dispersions are observed: α-dispersion (~10 Hz - 10 kHz) arises from ionic polarization at the cell–electrolyte interface; β-dispersion (~10 kHz – 100 MHz) originates from interfacial polarization across the cell membrane (Maxwell–Wagner effect); and γ-dispersion (~100 MHz – 100 GHz+) is attributed to the dipolar relaxation of water and organic molecules inside and around the cells. Schematics of the field interaction with single cells illustrate how electric fields couple with cellular components at different frequency regimes, transitioning from whole-cell responses at low frequencies to intracellular and molecular responses at higher frequencies. The inset depicts the molecular origin of γ-dispersion, highlighting rotation of water and biomolecules [18], [19]. While the existence of the δ-dispersion remains somewhat debated and not depicted, recent advances in broadband dielectric spectroscopy have provided growing evidence supporting its presence between the β- and γ-dispersion regimes. (b) Equivalent electrical circuit model of a single biological cell situated between parallel electrodes.

For single cell analysis, the foundational approach to understanding the cell-EM wave interaction requires an inspection of both the polarization mechanisms and the circuit model of a cell. The cell can be modeled as an equivalent electrical circuit composed of three main compartments: the extracellular medium, cell membrane, and cytoplasm. FIGURE 1(b) shows the equivalent circuit model of a single cell with the order of magnitude of the capacitive and resistive elements [17]. In this model, the cell membrane can be modeled as a capacitor and a resistor in parallel due to the insulating lipid bilayer, while the cytoplasm and extracellular environment introduce frequency-dependent resistive and dielectric elements tied to their ionic and dielectric molecular compositions. The membrane acts as a high-impedance barrier at low frequencies, while the cytoplasm increasingly contributes at higher frequencies. Quantitatively, the complex impedance seen by an external field is governed by the series and parallel arrangement of the resistive and capacitive components of the cell, and the expression of the impedance has been derived in the literature [17].

At low frequencies (< 10 kHz), ions in the solution are mobile in response to the field. The insulating cell membrane restricts ion motion to the extracellular space, leading to charge accumulation on the outside of the membrane and creating strong interfacial polarization [1], [20]. This frequency range corresponds to α dispersion, characterized by a large ε′ due to interfacial charge accumulation at the cell membrane and electrode interfaces. The imaginary part of permittivity, ε″, remains low in this range because the membrane conductance is minimal, leading to limited dielectric loss. The specific conductivity (σ) is also low, reflecting restricted ionic transport across the membrane. For instance, at 10 kHz, the membrane reactance $X_{mem}=\frac{1}{2\pi f{C}_{mem}}$ is on the order of 10MΩ, and the cytoplasmic reactance X_cyt is on the order of 1MΩ. Both of these capacitive paths are shunted by relative smaller resistive elements. The high resistance of the membrane dominates the total impedance of the cellular equivalent circuit. The membrane behaves like a high-impedance barrier, effectively shielding the cell’s interior from the external field. This explains why low frequency dielectrophoresis (DEP) and impedance measurements of single cells are primarily sensitive to membrane properties rather than intracellular features. The field interacts with the cell membrane and associated ions. Applications leveraging this frequency range typically exploit the membrane’s electrical properties to infer cell size and integrity. One classic example is the Coulter counter, which measures changes in electrical current as individual cells pass through a narrow aperture. This technique remains a clinical standard for automated blood cell counting [21]. Simple in principle, such α-dispersion–based measurements have become fundamental in clinical hematology, cell viability assessment, and high-throughput biomedical diagnostics.

As the frequency increases beyond the α-dispersion range and enters the β-dispersion regime (typically 10 kHz to 100 MHz), the capacitive reactance of the membrane X_mem decreases, reaching values comparable to or lower than the membrane and cytoplasmic resistance. For example, at 100 MHz, the membrane and cytoplasmic reactance XX_mem and XX_cyt are on the order of 1000 Ω and 100 Ω respectively, shunting the membrane and cytoplasmic resistance. As a result, the cell membrane capacitance dominates the total cellular impedance. Physically, the membrane capacitance dominance is a result of Maxwell–Wagner interfacial polarization. At high frequencies of the β-dispersion regime, a gradual shift of the dominant impedance pathway from the insulating membrane to the dispersive cytoplasm enables EM field to penetrate the cytoplasm. In this range, the total impedance of the cell is contributed by both the membrane capacitance and the cytoplasmic capacitance, and the electric field begins to interact with the cell interior rather than being blocked by the membrane interface. As a result, dielectric measurements reflect the physical and electrical properties of the entire cell, including size, shape, membrane composition, and intracellular conductivity. The β-dispersion regime is ideal for applications like impedance-based flow cytometry [5], [22], where changes in the cell are detected in real time as cells respond to environmental conditions, mechanical stress, or intrinsic biological processes such as cell cycle progression, osmotic shifts, or apoptosis. However, composition of the cytoplasm is not fully accessible, as the cytoplasm contributes only partially to the measured signal [23], [24], [25].

As the frequency rises into the high MHz and microwave (GHz) regime, γ-dispersion becomes prominent. In the single-shell circuit model, the membrane’s capacitive reactance X_mem decreases with frequency, while the cytoplasmic resistance R_cyt remains relatively constant. For example, at 1 GHz, with C_mem ≅ 1 pF and C_cyt ≅ 10 pF, X_mem and X_cyt are on the order of 10–100 Ω. Although X_mem does not vanish, the relatively low magnitude of the cell impedance allows minute changes in the cytoplasm to be measured more sensitively. The primary dispersion in this frequency range is attributed to the rotational relaxation of water molecules, which peaks near 18–20 GHz [3], [26]. The pronounced dielectric loss reflects the rapid reorientation of small, free dipolar molecules—primarily water—at high frequencies, a defining feature of the γ-dispersion. In this regime, the real part of the permittivity ε′ becomes sensitive to subcellular structures, including water structuring, macromolecular crowding, and organelle organization. The imaginary part ε″, representing dielectric loss, captures energy dissipation from molecular friction and dipolar relaxation—especially from water, the most abundant intracellular component. Simultaneously, the specific conductivity σ, shown in magenta in FIGURE 1(a), reflects dipolar and molecular relaxation rather than charge transport at higher frequencies, and increases with frequency. This frequency-dependent conductivity is particularly influenced by the dipolar relaxation of water molecules and other polar cytoplasmic constituents, becoming a dominant factor in determining dielectric loss in the γ-dispersion regime. Together, these parameters reveal how electromagnetic fields probe not only membrane integrity but also the physical and biochemical organization inside the cell. The key components influencing this response are thus the cytosolic matrix, proteins, and other hydrated macromolecules [6], [27]. The unique ability of γ-dispersion to probe intracellular dielectric properties in a label-free and real-time manner has led to a new class of biosensing applications. Microwave resonator sensors and near-field antennas have been used to detect subtle changes in cellular hydration, protein folding, and macromolecular interactions, enabling the classification of cells based on physiological or pathological states without chemical labeling. Shifts in the resonance frequency can distinguish between normal and cancerous cells due to differences in cytoplasmic conductivity and dielectric relaxation [28]. In flow-based formats, microwave cytometry enables high-throughput single-cell analysis [23], with the potential to identify apoptotic or drug-perturbed cells based on intracellular water mobility and permittivity changes [29], [30]. These features position γ-dispersion-based methods as powerful tools for noninvasive cellular phenotyping, drug response monitoring, and the real-time study of biophysical states inside living cells.

Between the β and γ dispersions lies a subtle domain: the δ-dispersion (100 MHz–5 GHz). Though often masked by the dominant relaxation of water, this regime is believed to arise from the motion of bound water molecules and the relaxation of polar biomolecular side chains. These components — hydration layers around proteins, nucleic acids, and membrane-associated macromolecules — introduce a weak but detectable dielectric signature, especially in sensors designed for high sensitivity. This dispersion is particularly relevant for biosensing applications that target protein conformational changes and biomolecular interactions [31].

Practically, many microwave biosensors use vector network analyzer (VNA) measurements, which quantify how an electromagnetic signal is reflected or transmitted when it encounters a biological sample. These systems typically measure scattering parameters, such as S₁₁ (reflection) and S₂₁ (transmission), which describe how the presence of a single cell perturbs the local electromagnetic field. These parameters are commonly mapped to complex permittivity through calibration procedures and circuit modeling. By extracting these permittivity values over a wide frequency spectrum, detailed dielectric fingerprints of individual cells are generated, capturing their internal composition, hydration state, molecular density, and functional phenotype [29], [32], [30].

Historically, low-frequency techniques in the kHz and low MHz range were the dominant choice for single-cell electrical measurements. Devices operating in the α- and β-dispersion ranges, such as Coulter counting and impedance-based flow cytometry, require relatively low-cost electronics, straightforward electrode configurations and robust signal-to-noise ratio, making them ideal for clinical and industrial adoption. These methods excel at detecting membrane-level changes and gross morphological features, such as cell size and membrane integrity, which are often sufficient for basic cell classification tasks. However, their ability to probe the interior of the cell is fundamentally limited by the high capacitive impedance of the membrane at low frequencies, and their accuracy is often compromised by electrode polarization artifacts that distort the measured signal near the electrode-electrolyte interface. In contrast, microwave sensing distinguishes itself by directly probing the dielectric response of intracellular water and macromolecules, granting access to subcellular features that low-frequency fields cannot reach. Because the membrane impedance becomes negligible at gigahertz frequencies, microwave fields can penetrate deeply into the cytoplasm, allowing the detection of dynamic changes in hydration, protein folding, and macromolecular crowding. Moreover, microwave measurements are less susceptible to electrode polarization effects, resulting in more reliable and stable dielectric reading. Although microwave radiation has a free-space wavelength on the order of centimeters, biosensing platforms have achieved microscale and even nanoscale resolution through near-field localization [33]. Structures such as coplanar waveguides, split-ring resonators, and microstrip sensors confine the electromagnetic field to small volumes, often within microfluidic channels or capacitive gaps, where the evanescent or resonant fields interact directly with single cells or subcellular structures. This field confinement, rather than the free-space wavelength, defines the effective spatial resolution of the measurement. Consequently, microwave biosensing enables sensitive, label-free, non-invasive, and real-time analysis of intracellular dielectric properties of single cells, complementing and extending the capabilities of conventional optical microscope-based single-cell imaging platforms.

III. SENSOR TYPES AND APPLICATIONS

The development of electrical and microwave-based sensors has revolutionized the field of single-cell analysis by enabling label-free, real-time detection of a wide range of cellular properties. A diverse array of sensing architectures has emerged to meet the spatial, temporal, and sensitivity demands of single-cell characterization. Broadly, these include transmission line sensors, which detect changes in wave propagation; capacitive electrode-based sensors, which focus on localized field interactions; and resonant structures, which enhance sensitivity through energy confinement at specific frequencies. In parallel, dielectrophoresis (DEP) has played a critical role in positioning and manipulating single cells within sensing regions and is increasingly integrated into modern sensor platforms. This section presents an in-depth review of these sensor types, highlighting their working principles, design strategies, and application domains in single-cell dielectric sensing.

A. TRANSMISSION LINE-BASED MICROWAVE SENSORS: MICROSTRIP AND COPLANAR WAVEGUIDE DESIGNS

Transmission line sensors form a foundational class of planar microwave devices widely used for high-sensitivity, label-free biosensing, particularly in the context of single-cell analysis [34], [35], [36]. The presence of individual cells or biomolecular targets in the sensing region perturbs the effective permittivity of the medium surrounding the transmission line, thereby altering propagation characteristics such as insertion loss and phase velocity. When incorporated into resonant configurations (e.g., stub-loaded or SRR-integrated lines), these perturbations also shift the resonant frequency, providing an additional sensitive metric for dielectric detection. Among the various transmission line configurations, microstrip lines and coplanar waveguides (CPWs) are the most employed due to their ease of fabrication, planar structure, and seamless integration with microfluidic channels [34], [36].

1). MICROSTRIP

Microstrip sensors consist of a conductive strip patterned on a dielectric substrate with a ground plane located on the opposite side, forming a planar transmission line that supports guided microwave propagation (FIGURE 2). The electromagnetic field in a microstrip is distributed between the dielectric substrate and the air above it, with a significant portion extending into the region above the substrate [37]. This fringing field distribution makes microstrip structures responsive to dielectric changes near the sensor surface, such as those introduced by a single biological cell. To enhance sensitivity and spatial resolution, microstrip designs often incorporate specialized features such as interdigital capacitors, split-ring resonators (SRRs), and stub-loaded configurations. For example, Dubuc et al. [34] demonstrated that integrating grounded interdigitated capacitors at the ends of microstrip stubs produced compact quarter-wavelength (λ/4) resonators capable of detecting small shifts in permittivity with high precision, enabling accurate quantification of molecular concentrations and potential applicability to single-cell dielectric monitoring.

FIGURE 2.

Schematic comparison of electric field distribution in two common planar transmission line structures used for biosensing: microstrip (left) and coplanar waveguide (CPW, right). In microstrip lines, the signal conductor is positioned above a dielectric substrate with a ground plane on the bottom, resulting in fields that extend primarily above the substrate. In CPW structures, the signal line is flanked by two ground lines on the same plane, confining the electric field more tightly near the sensor surface and enhancing interaction with nearby biological samples [38].

Microstrip-based sensors offer several advantages in the context of single-cell analysis: they are compact, cost-effective, compatible with standard printed circuit board (PCB) fabrication, and easily integrated into lab-on-a-chip platforms. Their operation at microwave frequencies allows sufficient penetration depth (roughly 10 to 300 μm) to interact with cellular contents, enabling characterization of both membrane and cytoplasmic dielectric properties [36]. However, they are also susceptible to environmental cross-sensitivities, such as temperature, humidity, or ionic strength, and exhibit limited field penetration depth compared to enclosed waveguide structures, which can limit performance in complex biological environment.

2). COPLANAR WAVEGUIDE

CPW structures consist of a central signal conductor flanked by two ground conductors on the same dielectric plane, resulting in strong electromagnetic field confinement near the sensor surface. This geometry enhances interaction with surface-bound analytes or cells suspended in microfluidic channels, making CPWs especially well-suited for detecting dielectric perturbations at the single-cell level. Compared to microstrip lines, CPWs exhibit lower radiation losses and reduced signal dispersion at millimeter-wave frequencies, which supports more precise permittivity measurements and higher signal fidelity [24].

However, when used as pure transmission line sensors, the open-field configurations of CPW distribute electromagnetic energy over broad regions, which limits field confinement and makes it challenging to resolve the small-scale dielectric contrasts of individual cells [39], [40]. To overcome the challenge, advanced adaptations have been developed. Guard electrodes (FIGURE 3), for example, have been integrated into CPW geometries to suppress fringing fields and improve the uniformity of the electric field distribution across the sensing region, improving reliability in permittivity extraction from individual cells [41]. Moreover, CPW platforms that incorporate interdigitated electrodes and split-ring resonators (SRRs) have demonstrated highly sensitive broadband detection capabilities ranging from 40 MHz to 40 GHz, enabling label-free monitoring of subtle dielectric changes in individual yeast, fibroblast, and bacterial cells [27]. Bow-tie-shaped CPW resonators have also been developed to enhance sensitivity and field localization, supporting applications such as single-cell trapping, flow-rate quantification, and nanopore-based virus sensing [42]. In these examples, CPWs are used as feedlines, while the field confinements are achieved through the other structures such as resonators. Resonator-based single cell sensors are discussed in the text below.

FIGURE 3.

(a) Basic low frequency implementation of guard electrodes with independent but synchronized voltage sources. (b) Exploded view of the basic sensor design, featuring two gold layers structured on glass substrates (not shown) and a microfluidic channel within an SU8 layer. The figures are adapted from [41].

CPW sensors in biosensing applications face several challenges. Their open structure makes them sensitive to the surrounding environment, leading to variability in baseline signals when fluidic conditions change. In addition, parasitic coupling and crosstalk can arise in densely integrated lab-on-chip layouts, reducing measurement fidelity. These effects are particularly relevant when characterizing single cells, where the dielectric perturbations are subtle and easily masked by background noise. Recent studies have shown that careful electrode symmetry, guard structures, and optimized resonator geometries can partially mitigate these issues, but they remain active areas of research rather than complete solutions [43], [41]. Overall, CPW and microstrip sensors form a complementary foundation for next-generation dielectric biosensing platforms. Their planar form factor, compatibility with microfabrication, and integration with microfluidic systems make them indispensable for developing compact, scalable, and high-sensitivity devices for single-cell diagnostics [36].

B. CAPACITIVE ELECTRODE-BASED SENSORS

Single-cell detection demands highly localized and sensitive electric field interactions to resolve subtle dielectric variations between individual cells.

Capacitive electrode-based sensors address these limitations by providing stronger field confinement and localized interrogation of cellular targets. It works by detecting changes in capacitance between electrodes when an object with a different dielectric constant or conductivity alters the surrounding electric field. Among them, ground-signal-ground (GSG) capacitive electrodes generate non-propagating, tightly confined fringing fields between adjacent electrodes. These localized fields interact directly with nearby cells, making them highly sensitive to small dielectric perturbations rather than global transmission characteristics. Similarly, interdigitated electrodes (IDEs) extend the active sensing region by creating overlapping fringing fields across multiple electrode fingers, which strongly interact with cells flowing through a microfluidic channel. GSG arrays tend to provide a more uniform sensing region with reduced baseline drift, while IDEs are particularly effective for real-time monitoring of cells in transit (FIGURE 4).

FIGURE 4.

(a) Schematic illustration of an Ground-Signal-Ground capacitive sensor [49]. (b) Diagram depicting the operational concept of the coplanar capacitive sensor: sensor examining a solid non-conductive sample [50].

Further refinements include the use of guard electrodes, which suppress edge-related fringing fields and improve measurement uniformity. Chien et al. [44] showed that guarded electrode configurations significantly increased the precision of permittivity extraction, enabling not only detection but also discrimination among cells based on viability or pathological state. Similarly, Lambert et al. [45] demonstrated that the tightly confined fields of capacitive sensors can differentiate between healthy and cancerous glioblastoma cells. More recently, Arzhang et al. and Kovacs et al. [46], [47] advanced coplanar electrode array integration into microwave cytometry platforms, achieving high sensitivity and label-free detection across diverse single-cell types.

Compared to transmission line sensors, capacitive electrode-based architectures enable strong field localization, high sensitivity, and straightforward integration with microfluidics, which are essential for subcellular dielectric resolution. However, their complex electrode geometries require precise fabrication, and their small active areas may limit throughput unless coupled with high-throughput microfluidic automation. Transmission line sensors, on the other hand, remain easier to fabricate, support broader frequency operation, and can be scaled into arrays, though their inherent lack of field confinement necessitates resonators or field-enhancing structures for single-cell applications. For a more comprehensive discussion of capacitive electrode-based sensors and their applications in biosensing, readers are referred to recent review articles [48].

C. RESONANT STRUCTURES FOR SINGLE-CELL DETECTION

Microwave resonators have emerged as powerful tools for label-free, non-invasive, real-time single-cell sensing, offering a compelling alternative to traditional broadband dielectric spectroscopy. While broadband sensors sweep across a wide frequency range to infer cellular properties, resonant structures operate at discrete frequencies, where electromagnetic energy is stored and strongly confined. This confinement dramatically enhances sensitivity to local dielectric changes, allowing even attofarad-scale capacitance shifts caused by the presence, size, or internal composition of a single biological cell to be detected with high precision [51], [52].

The unique advantage of resonators in single-cell biosensing lies in their ability to detect minute perturbations in local permittivity caused by cellular attributes such as membrane capacitance, cytoplasmic conductivity, and morphological variations [53]. The high detection sensitivity of resonant sensors stems from their high-quality factor (Q) and strong spatial field confinement, which significantly amplifies the interaction between the electromagnetic field and individual biological cells, especially when integrated with microfluidic platforms. However, resonator-based sensing also comes with notable limitations. These devices are inherently sensitive to environmental fluctuations such as temperature drift or dielectric background noise, which can confound measurements. Additionally, because resonators operate at discrete frequencies, they may be ill-suited for exploratory analysis where the target’s dielectric properties are poorly characterized, and a broadband response is needed. This narrowband nature also limits their utility in multi-analyte detection or in applications that benefit from full-spectrum data, such as data-driven or machine learning-based biosensing. As such, while resonant techniques offer superior sensitivity for targeted and well-understood applications, they may be less versatile compared to broadband or label-free approaches.

Among various resonant sensor architectures, split ring resonators (SRRs) and their complementary counterparts, complementary split ring resonators (CSRRs), are the most widely adopted structures in single-cell microwave biosensing (FIGURE 5). Their compact size, strong near-field localization, and high dielectric sensitivity make them particularly effective for resolving the minute electromagnetic disturbances caused by individual biological cells. This enables applications such as differentiating viable vs. non-viable cells through membrane integrity, monitoring electroporation by detecting dielectric disruption in real time [54], tracking drug-induced physiological changes during cytotoxicity screening [6], profiling dielectric heterogeneity in cancerous vs. healthy cells [55], monitoring cell differentiation and metabolic activity in stem cells [56] etc.

FIGURE 5.

Topology and circuit model of (a) split ring resonator (SRR) and (b) complementary split ring resonator (CSRR). The figures are adapted from [57].

SRRs are planar metamaterial-inspired structures composed of one or more concentric metallic loops, each containing a narrow slit or gap. These slits introduce capacitive discontinuities, while the rings serve as inductive elements, collectively forming an LC resonator that exhibits a sharp, well-defined resonance frequency. When a single cell enters the high-field region near the SRR gap, it perturbs the local permittivity, causing a shift in both the resonance frequency and amplitude [58], [55]. This interaction enables SRRs to detect changes in cellular parameters such as membrane capacitance, cytoplasmic conductivity, and intracellular water content, making them suitable for label-free classification of viable vs. non-viable cells, cancer diagnostics, or monitoring dynamic events like electroporation and apoptosis in real time.

Despite their effectiveness, SRRs are inherently polarization-sensitive and operate over a narrow bandwidth, which can be limiting in continuous-flow or high-throughput scenarios where cells with varying dielectric properties pass through the sensing region. In such dynamic conditions, a broader bandwidth is often needed to capture a wider range of cellular responses in real time. To address these challenges, CSRRs have been introduced. These are typically etched into the ground plane beneath a microstrip or coplanar waveguide and are excited by the electric field component of the microwave signal. This orientation makes CSRRs more amenable to planar integration with microfluidic systems, enhances compatibility with standard PCB processes, and reduces radiation losses. CSRRs retain the high sensitivity of SRRs while offering greater robustness in device packaging and layout [59].

Further evolution of this topology has led to the development of complementary spiral resonators (CSRs), which utilize tightly wound spiral-shaped slits to boost both capacitive loading and inductance. These features result in lower resonant frequencies and enhanced field confinement, enabling ultra-sensitive detection of small dielectric shifts associated with single-cell events such as osmotic swelling, metabolic shifts, or early-stage apoptosis—without requiring biochemical labels.

Beyond the widely used resonators described above, several other microwave resonator architectures have either been applied to single cell sensing or demonstrate strong potential for adaptation in this domain (FIGURE 6). These include stepped impedance resonators (SIRs), coupled transmission-line resonators, interdigitated capacitor-coupled resonators, substrate-integrated waveguide (SIW) resonators, folded waveguide structures, and symmetry-enhanced metamaterial geometries such as mirror symmetric dodecagon resonators. Each of these designs offers distinct advantages in terms of field confinement, integration capability, multiband operation, and fabrication scalability, factors that influence their suitability for specific single-cell applications, including electroporation tracking, cell cycle profiling, differentiation monitoring, and high-throughput cell classification. TABLE 1 presents a comparative summary of the key resonator types in the literature, highlighting their electromagnetic characteristics, design complexity, fabrication compatibility, and applications in single-cell dielectric sensing.

FIGURE 6.

Examples of microwave resonator architectures for single cell analysis. (a) Stepped impedance resonator consisting of two microstrip. (b) The coupled transmission lines resonator and the equivalent LC circuit. (c) Interdigitated comb capacitor-coupled resonator and the equivalent LC circuit. (d) Geometry of the substrate integrated waveguide cavity resonator with a sensing aperture. (e) Folded-waveguide quarter-wavelength resonator. (f) Front view of the metamaterial patch mirror symmetric resonator unit cell total patch layout and enlarged view of a segment [60], [51], [61], [62], [63], [64].

TABLE 1.

Comparative summary of resonator-based and resonator-integrated microwave sensor architectures for single-cell dielectric sensing

Stepped Impedance Resonator (SIR)	ltemating high/low lance sections	−5 GHz	ct, high-Q, and tunable	e ΔS21 magnitude scales with cell size and membrane capacitance	Multiplexed cell sensing platforms	Glioblastom a cells	Sensitive to fabrication errors	[44], [46], [47]
Transmission Line Resonator	microstrip lines with mutual coupling	−2 GHz	detection, easy integration with microfluidics	response and frequency indicate cytoplasmic conductivity	dielectric cytometry and viability profiling	fibroblast	alignment critical, sensitive to geometry
Comb Capacitor-Coupled Resonator	wavelength strips coupled via interdigitated capacitor		ity, tunable geometry	responds to cell size and membrane capacitance	single-cell sensing in microchannels	a cells, yeast cells	resonances, fabrication precision needed
Split Ring Resonator (SRR) / CSRR / CSR	entric rings with slits (SRR); ground-plane etching for CSRR	−10 GHz	h field localization, suitable for label-free sensing	resonant frequency shift is linked to dielectric constant. The ΔS21 magnitude is icative of viability	Single-cell viability assays and dielectric subpopulation analysis	Jurkat T-cells, U87 glioblastom a cells	Narrow bandwidth, polarization sensitivity (SRR)	[39], [40], [41], [68]
Integrated Waveguide (SIW) Resonator	rectangular waveguide with via sidewalls			shift is (Δf) proportional to intracellular permittivity	mapping of single cells	cells	tuning tradeoff, fabrication complexity	[40]
Resonator	paths for compact size	GHz	broad frequency range	magnitude changes during electroporation events	tracking	cells	fabrication, sensitive to roughness
Mirror Symmetric Dodecagon Resonator	ymmetric dodecagon unit cells with SRRs/CSRRs	−12 GHz	ltiband, polarization-independent, compact	e multiband AS21 response and frequency shift encode size and intracellular heterogeneity	Multi-parametric single-cell sensing	Mammalian cells, fibroblasts	Requires high symmetry; fabrication challenges	] [49], [50]

D. DEP

DEP is a long-established electrokinetic technique that enables the label-free manipulation and characterization of polarizable particles, such as biological cells, in non-uniform electric fields. Several recent reviews have described the DEP principle and applications in detail [65], [66], [2]. Briefly, when a dielectric particle is placed in a spatially varying electric field, it experiences a net force due to the induced dipole moment interacting with the electric field gradient. Traditional DEP typically operates in the kilohertz to low megahertz frequency range (~100 kHz to 10 MHz), and it has been extensively studied for biological applications since the foundational work of Ronald Pethig and others [2]. In this regime, the single-shell dielectric approximation is sufficient to describe cellular behavior, with the membrane modeled as a thin capacitive shell enclosing the conductive cytoplasm. The dielectrophoretic force acting on the cell is determined by the Clausius–Mossotti (CM) factor [2], which reflects the frequency-dependent permittivity and conductivity contrast between the cell and its surrounding medium. When the real part of the CM factor is positive, the cell is attracted to regions of high electric field intensity (positive DEP); when it is negative, the cell is repelled toward low-field regions (negative DEP). The transition point between these regimes, known as the crossover frequency (f_xo), is a critical parameter for assessing cellular dielectric properties. At traditional DEP frequencies, the first crossover frequency (f_xo1) is closely associated with membrane capacitance and cell size [39], [44].

Traditional DEP has been widely employed in single-cell analysis for its ability to probe membrane-level dielectric properties without the need for molecular labeling. It has proven particularly effective for cell sorting, viability assessment, and stem cell characterization [2]. For example, viable and non-viable cells exhibit distinct DEP responses because apoptotic or necrotic membranes lose their insulating properties, making f_xo1 a convenient viability marker. Similarly, DEP has been used to distinguish stem from differentiated cells, as membrane capacitance changes during differentiation [40]. Such label-free sensitivity to physiological state makes DEP a powerful tool for stemness profiling and cancer stem cell enrichment.

It is noted that while DEP and resonator-based sensor both use frequency shift to differentiate cells, and they are sometimes integrated in the same platform, their mechanisms are distinct. DEP is based on the motion of polarizable particles induced by non-uniform electric fields, whereas resonator-based sensing detects dielectric perturbations as shifts in resonant frequency or amplitude.

Traditional DEP has inherent limitations. Because it relies on relatively low-frequency fields, its sensitivity is restricted to the membrane, with limited penetration into the cytoplasm or nucleus. DEP behavior is also strongly dependent on the suspending medium conductivity, requiring precise control over buffer composition. In heterogeneous samples, overlapping responses can complicate discrimination of closely related subpopulations. Despite these constraints, DEP’s noninvasiveness, label-free nature, and compatibility with microfluidics have secured its role as a foundational tool in single-cell characterization.

In integrated platforms operating at ultra-high frequencies (UHF, > 50 MHz) or microwave/millimeter-wave regimes, DEP manipulation remains central for trapping or positioning cells, after which high-frequency signals probe intracellular properties [67], [45], For instance, Manczak et al. exploited a CMOS quadrupole system to measure glioblastoma cell crossover frequencies at UHF, enabling discrimination of differentiated and undifferentiated states via intracellular dielectric contrasts [68], Palego et al. extended this principle with a BiCMOS microfluidic sensor where DEP trapping was combined with microwave intermodulation for label-free monitoring of electroporation events [69].

These advances highlight how conventional DEP manipulation complements high-frequency readouts. Simple broadband, passive electrode microchamber structures [10] enable dielectric discrimination with capacitance resolution on the order of 0.1 fF, limited by the sensitivity of external instruments such as the VNA. In contrast, resonant and active architectures [46] achieve oscillator-based detection at microwave and millimeter wave frequencies, where sensitivity scales with frequency. This allows attofarad-level resolution and enhanced field penetration beyond the membrane, opening access to intracellular dielectric properties.

Evolving from conventional DEP sensing in the kHz–MHz range, UHF-DEP [40] represents an emerging combination of device simplicity and depth of analysis. Operating in the tens to hundreds of MHz, UHF-DEP samples dielectric properties in a regime where contrast between cell populations remains significant but spectral overlap is less severe than in the GHz range. This enables discrimination based on subtle CM factor differences (as small as 0.01) that are difficult to resolve using broadband S-parameter methods. By combining DEP-based cell manipulation with UHF or resonant readouts, modem single-cell platforms are increasingly able to capture both membrane-level and intracellular information, bridging the gap between traditional electrokinetic methods and advanced dielectric spectroscopy [54], [65].

IV. PRACTICAL CHALLENGES IN SINGLE-CELL MICROWAVE MEASUREMENT

Single-cell microwave spectroscopy is a rapidly advancing field, but several unique challenges limit its broader applications. The weak signal produced by one cell, combined with the complexities of cell localization, demands careful attention to detection, positioning, stability, and calibration.

A. Detecting and Confirming the Presence of a Single Cell

Microwave biosensors typically register a cell as a small resonance shift or a small variation in scattering parameters. The weak signals from single cells are easily confounded by noise, bubbles, or baseline signal drift. To overcome this challenge, most demonstrations pair microwave sensing with complementary methods to confirm that a single cell is indeed being interrogated. Optical tracking is the most common approach [46], while impedance cytometry at lower frequencies has also been combined with microwave readout [22]. More advanced schemes integrate LED detectors or imaging flow cytometry to time-stamp the cells when they cross the sensing region [47].

B. Positioning Cells within the Sensing Region

Accurate positioning is critical for single cell analysis because the cells should reside in the highly localized electromagnetic field. Electrode design plays a central role in cell positioning. Broadband architectures enabling both microwave sensing and DEP manipulation in the same platform [23] remains the most established method, offering subcellular-precision placement over resonator splits or coplanar capacitor gaps. Importantly, DEP not only enables trapping but also controlled de-trapping, a flexibility that stationary mechanical traps typically lack [45], [2], [70]. Specific electrode designs that combine high-frequency microwave sensing with localized DEP actuation have also been demonstrated. For example, systems that integrate microwave interferometric sensors with DEP electrodes enable simultaneous dielectric characterization and precise cell manipulation within the sensing [71]. Recent advances in ultra-high-frequency (UHF) DEP extend the operating range into the hundreds of MHz and beyond, improving selectivity for intracellular properties while maintaining precise cell control. In addition to DEP-based cell positioning, acoustic or optical tweezers have been demonstrated, which avoid the conductivity constraints of DEP [29]. Specifically, optical tweezers have been integrated into microfluidic platforms to trap and position individual cells with nanometer-scale precision. For instance, Enger et al. combined optical tweezers with microfluidics to manipulate and sort single cells within a microchannel system [70].

Another common cell positioning strategy is to employ microfluidics. The design of the microfluidic channel itself strongly influences positioning accuracy. Tapered or constricted geometries or sheath-flow focusing can align cells precisely within electrode gaps, improving detection sensitivity and throughput [36]. Such strategies are widely used in impedance-based cytometry systems [72]. Microchannel features like pillar arrays, multilayer layouts, or constriction zones also help define single-cell paths and reduce positional variance in sensing regions [73]. Passive microwell traps are useful for long-term monitoring [56]. The different cell position methods come with trade-offs between precision, throughput, and compatibility with biological buffers [74].

C. Maintaining Positional Stability

Even when successfully placed, a single cell rarely sits perfectly still. Brownian motion, shear flow, and buoyancy all contribute to small positional shifts. Because microwave fields decay rapidly away from the sensing region, these small displacements can broaden resonances, increase signal variation and degrade reproducibility. Guarded electrode designs help reduce off-axis cell motion [41]. Low-shear channel geometries and viscoelastic focusing also help to damp random motion [73].

D. Buffer Composition and Environmental Effects

The surrounding medium strongly influences both the cell trapping efficiency in an electrical field and microwave response. High conductivity suppresses DEP forces and adds dielectric loss, while very low conductivity can compromise cell viability and function [68]. Environmental variables such as temperature, evaporation, and bubbles also perturb the baseline. These environmental factors, often overlooked in bulk assays, become the dominant error sources when the target is a single cell. Thermal stabilization, humidity control, and integrated bubble traps have all been reported as practical solutions to overcome the challenges of environmental effects [54].

E. Calibration, De-embedding, and Processing Methods

Perhaps the most technically demanding step in single cell microwave sensing is ensuring that the tiny cellular perturbation is not buried beneath parasitics. Attofarad-level capacitance shifts are easily masked by bond-wire inductances or fluidic interfaces [52]. On-chip open/short/thru standards in liquid are frequently used to de-embed these contributions, and two-buffer calibration helps subtract the medium background [17]. Spatially localized calibration, as developed in near-field microwave microscopy [75], offers one route to distinguish weak cellular signals from background. Single-cell work also raises new demands in broadband calibration across MHz–GHz ranges, where membrane polarization dominates at low frequencies and water relaxation dominates above 20 GHz [13]. Approaches such as broadband impedance spectroscopy [14] and time-domain dielectric spectroscopy [76] aim to improve calibration over these wide spans.

The transient nature of single-cell events in electrical cytometry introduces additional complexities. A cell may perturb the sensing region for only milliseconds, requiring real-time acquisition and fast fitting to equivalent-circuit models. More recently, machine learning has been explored as a processing tool to denoise transient responses and extract reproducible dielectric fingerprints [13].

In short, calibration and de-embedding for single-cell dielectric spectroscopy are not scaled-down versions of bulk methods. They require liquid-compatible standards, broadband correction, and transient-capable processing strategies to ensure that the weak cellular signature can be extracted with confidence.

V. CONCLUSION AND OUTLOOK

Microwave biosensors have emerged as a powerful and versatile platform for label-free, real-time analysis of single-cell dielectric properties. By probing frequency-dependent interactions between electromagnetic fields and cellular components, these systems offer insights into both membrane and intracellular features that are inaccessible to conventional low-frequency techniques. The ability to capture α-, β-, δ-, and γ-dispersion responses enables comprehensive characterization of the cellular structure, hydration state, viability, and functional phenotype. With well-established theoretical foundations of dielectric dispersion and the equivalent circuit models underpinning electrical cell sensing, various microwave biosensors, including transmission-line-based structures, capacitive electrodes, and high-Q resonators, have been successfully implemented. Each approach offers unique advantages: transmission lines support broadband profiling, capacitive sensors provide high spatial resolution, and resonators deliver exceptional sensitivity to minute permittivity shifts. When integrated with microfluidic platforms and dielectrophoretic manipulation, these sensor architectures enable high-throughput, label-free single-cell analysis with subcellular precision.

Despite fast progress, several challenges remain. Resonator-based sensors, while sensitive, are limited by their narrowband nature and environmental conditions, such as temperature or humidity, which can impact their accuracy and reliability. Capacitive sensors often demand complex fabrication, and broadband systems face trade-offs between spectral resolution and signal stability. Moreover, translating raw dielectric data into actionable biological insights requires continued development of computational tools, including machine learning frameworks capable of handling multidimensional permittivity signatures.

Microwave biosensing is moving beyond traditional single-parameter measurements toward richer, integrative platforms capable of extracting multifaceted biological information at the single-cell level. A key direction for the field is the convergence of microwave sensing with other physical modalities. Recent innovations have demonstrated that combining dielectric measurements with optical or electrochemical readouts can significantly enhance cellular profiling. For example, hybrid systems that fuse microwave impedance cytometry with fluorescence or Raman spectroscopy have enabled simultaneous detection of physical, molecular, and functional markers from individual cells [77]. These multimodal systems not only offer cross-validation of results but also expand the interpretability of dielectric changes by resolving ambiguities related to cell size, morphology, or medium conductivity.

In parallel, the field is also witnessing substantial progress in miniaturization and scalability. Advances in microfabrication and CMOS-compatible design have led to the development of compact, chip-scale microwave biosensors that support massively parallel measurements. These platforms, often integrated with microfluidic components, allow for high-throughput screening and precise spatial control of single cells in real time [78]. By reducing device footprint and improving multiplexing capabilities, such systems are laying the groundwork for portable, point-of-care diagnostic tools and large-scale biological assays, bridging the gap between benchtop research and clinical application.

Finally, as microwave sensing technologies generate increasingly complex datasets, data-driven methodologies are becoming indispensable for extracting actionable insights [23]. The nonlinear and high-dimensional nature of microwave signal responses, such as scattering parameters and impedance spectra, lends itself well to machine learning approaches. Emerging studies have employed deep learning models to capture subtle variations in dielectric signatures that correlate with subcellular structures and physiological states [79]. Meanwhile, conventional classifiers like support vector machines and random forests have shown high accuracy in tasks such as apoptosis detection and cell-type classification [80]. These computational frameworks not only improve classification performance but also offer the potential for predictive modeling—enabling the detection of early disease states or therapeutic responses from previously indiscernible dielectric patterns.

Collectively, these advances are transforming microwave biosensors from passive measurement devices into intelligent, multifunctional platforms. As the field continues to mature, the integration of multimodal sensing, chip-scale engineering, and artificial intelligence will likely redefine the scope of single-cell analysis, enabling real-time, non-invasive access to cellular phenotypes and functions with unprecedented resolution and throughput.

TABLE 2.

Summary of DEP-based single-cell manipulation and sensing across frequency regimes

Traditional DEP (< 1 MHz)	10 Hz – 1 MH	Cell and particle trapping, sorting, and sensing through polarization contrast and differential DEP forces	Particle position shift, crossover frequency shift, impedance or current variation, and capacitance change due to cell displacement	Membrane capacitance and surface charge accumulation at the cell–medium interface; capacitance contrast among different cell types	[2], [39]
High-frequency / microwave-assisted DEP	1 MHz (DEP 15 GHz (carrier)	Non-linear DEP producing intermodulation between low- and high-frequency fields	Intermodulation amplitude of S-parameters	Non-linear polarization of cytoplasm and membrane interface	[70]
		MHz; sensing via	effective permittivity change	permittivity and intracellular water
	100 – 300 MH (UHF-DEP)	Combined sorting and sensing (no trapping) based on DEP-force-driven outlet paths	ΔS-parameters / permittivity contrast	Cytoplasmic conductivity and membrane polarization crossover	[45]