From Lab to Fab: Path Forward for Multi‐Scale Design of Industrial Anion Exchange Membrane Electrolyzers

ABSTRACT

Anion exchange membrane water electrolysis (AEMWE) has emerged as a pivotal pathway bridging laboratory‐scale research to large‐scale hydrogen production. Nevertheless, its commercialization is constrained by multi‐scale failure mechanisms that operate across scales ranging from atoms to entire systems, including catalyst dissolution and reconstruction, degradation of the three‐phase interface with membrane electrode assemblies, corrosion of bipolar plates, and uneven stack assembly. This review systematically investigates the failure behaviors of AEM electrolyzers across multiple scales and introduces a collaborative design strategy spanning from the atomic to the system level. By integrating innovative membrane‐electrode architectures, biomimetic flow‐field designs, and advanced intelligent control systems, we establish a full‐chain optimization scheme spanning materials, devices, and systems that simultaneously improves current density, durability, and dynamic response. Emphasizing the critical roles of in situ characterization and artificial intelligence in elucidating failure mechanisms and enabling predictive control. This review also provides a systematic multi‐scale design blueprint and a technical pathway for transitioning AEMWE from laboratory‐scale prototypes (“Lab”) to gigawatt‐scale fabrication facilities (“Fab”). Ultimately, it aims to facilitate the adoption of AEMWE as an efficient and reliable industrial solution for the green hydrogen economy.

Multi‐scale design links materials, devices, and control to accelerate the scale‐up of durable, high‐performance industrial AEMWE from “Lab” to “Fab”.

1. Introduction

The global transition toward a sustainable and decarbonized energy system has unequivocally established hydrogen as a cornerstone of future energy landscapes, particularly for hard‐to‐abate sectors such as heavy industry, chemical manufacturing, and long‐haul transportation [1, 2, 3]. Within this context, “green hydrogen” (produced exclusively via water electrolysis powered by renewable electricity sources like solar, wind, and hydropower) stands out as the most promising vector for achieving deep decarbonization [4, 5]. Unlike conventional hydrogen production methods, notably steam methane reforming which emits significant CO₂, green hydrogen offers a carbon‐neutral pathway, contingent upon the sustainability of the electricity input [6]. Therefore, the scalability, efficiency, and economic viability of water electrolysis technologies are paramount to the realization of a robust and truly sustainable hydrogen economy [7, 8]. The escalating commitments from governments and industries worldwide, as reflected in ambitious targets set by the U.S. Department of Energy and the European Clean Hydrogen Partnership, underscore the urgency of advancing electrolyzer technology from laboratory‐scale prototypes to gigawatt‐scale manufacturing [9].

Anion exchange membrane water electrolysis (AEMWE) has emerged as a highly promising technology that potentially combines the best attributes of the two established alternatives: the use of low‐cost, non‐precious metal catalysts from traditional alkaline water electrolysis (AWE), along with the high current density, compact system design, and rapid response capabilities from proton exchange membrane water electrolysis (PEMWE) [3, 16]. Table 1 presents a comparison of three water electrolysis technologies, highlighting the potential advantages of AEM electrolyzers. By facilitating the conduction of OH⁻ through a solid polymer membrane, AEMWE systems can operate with reduced alkalinity compared to AWE, mitigating corrosion issues while still enabling the use of earth‐abundant electrocatalysts for both the hydrogen evolution reaction (HER) and the critical oxygen evolution reaction (OER) [17]. In the commercialization of AEMWE technology, core challenges span both micro and macro‐scales. Issues spanning multiple scales remain to be systematically resolved, including atomic‐level intrinsic catalyst activity and stability, the efficient construction of microscale three‐phase interfaces within membrane electrode assemblies, and durability under industrial‐scale high current densities. At the macro‐scale, significant bottlenecks persist in electrolyzer stack scale‐up, material compatibility, and intelligent coupling control between systems and fluctuating renewable energy sources. To effectively bridge the gap between “Lab” and “Fab”, this review advocates for a comprehensive, multi‐scale design approach for AEM electrolyzers, as illustrated in Figure 1. This figure presents a synergistic development pathway that encompasses fundamental research, technological advancement, and industrialization, underscoring the necessity of integrating efforts from nanoscale catalyst engineering to macroscale system integration. The core argument is that to unlock the full potential of AEM electrolysis, particularly in addressing the rate‐limiting alkaline OER with its complex 4e⁻ transfer kinetics, coordinated innovations across various scales are required. These span atomic‐level catalyst design, micro‐scale optimization of triple‐phase boundaries, meso‐scale electrode and membrane engineering, to macro‐scale electrolyzer stack and system control [1, 18, 19].

TABLE 1.

Comparison of AEMWE with other water electrolysis technologies.

Technology
Current status [7, 8, 9]	TRL 9 (mature commercial technology and widely deployed)	TRL 8–9 (commercial commercialization and rapid deployment)	TRL 6–7 (demonstrated)
Capital cost (CAPEX) (stack level, 2020–2023) [10, 11, 12]	$200–$400/kW (low stack cost due to non‐noble catalysts)	$200–$400/kW (low stack cost due to non‐noble catalysts)	$300–$600/kW (projected) (potentially low cost via non‐noble catalysts and cheap BPPs)
Efficiency [13, 14, 15]	59%∼70%	65%∼82%	52%∼67%
Advantages [13, 14, 15]	Low cost per slot; long lifespan; perfect industrial chain; adapt to large‐scale projects	High equipment cost; fast response; high pressure resistance; adapt to fluctuating power	Low cost + fast response; pressure resistance; non‐precious metal catalyst; localization rate > 95%
Disadvantages [13, 14, 15]	Inefficient; danger of mixing H2+O2; poor dynamic responsiveness	Rely on Ir/Pt catalysts; sensitive to water quality; high cost	Anion exchange membrane; lifespan insufficient
Industrialization [11, 12]	Dominant market share (∼60%–70% of global installed capacity); standard for large‐scale and centralized projects; mature global supply chain.	Growing commercial share (∼20%–30% of global projects); rapidly scaling up in MW/GW‐scale wind/solar‐to‐H2 plants	Early commercialization/niche markets; small‐scale commercial stacks (typically <50 kW) available; transitioning from lab‐scale to multi‐mw demonstration projects.

FIGURE 1.

Multi‐scale enhancement of alkaline OER via atomic‐scale catalyst design, meso‐scale electrode optimization, and macro‐scale system control.

Existing reviews have extensively summarized advances in AEMs, ionomers, electrocatalysts, membrane‐electrode assemblies, and system performance. However, the durability limitations encountered during AEMWE scale‐up are rarely governed by a single material or component. As the system evolves from catalyst materials to electrodes, MEAs, single cells, stacks, and integrated systems, degradation becomes increasingly nonlinear due to the coupling of interfacial transport, local chemical environments, mechanical stress, water/gas management, flow‐field distribution, and dynamic operating conditions. Consequently, a component‐by‐component perspective is insufficient for identifying the fundamental bottlenecks that limit industrial implementation. To address this challenge, this review establishes a degradation‐mechanism‐guided, cross‐scale design framework for AEMWE scale‐up. Distinct from existing reviews that primarily focus on advances at individual scales, we emphasize how degradation mechanisms originate, propagate, and interact across materials, interfaces, devices, and systems, ultimately governing electrolyzer durability and scale‐up performance. Furthermore, we highlight the emerging role of advanced operando and in situ diagnostic techniques in establishing mechanistic links between material‐level degradation and performance decay at the electrode, cell, stack, and system levels. By integrating degradation mechanisms, multi‐scale diagnostics, electrode structures, electrolyzer design, operational management, and intelligent control into a unified framework. This review conducts a holistic analysis of technical issues across the entire production chain and establish a closed loop that bridges basic laboratory research and practical deployment.

2. Material Frontier and Integration Gap

At the catalyst level, researchers have conducted extensive forward‐looking explorations to advance the industrialization of AEM electrolysis (Table 2).

TABLE 2.

The key performance indicators of the catalyst.

Anodes	Cathodes	Membranes	Performance	Durability (h)	Temp.	Electrolytes	Refs.
Na‐HE LDH	Pt/C	FAA‐3‐50	1.6 V @ 1 A cm−2	2000 (0.5 A cm−2)	80°C	30 wt% KOH	[20]
CAPist‐L1	Ni4Mo/MoO2	PAP‐TP‐85	1.8 V @ 2.7 A cm−2	1500 (1 A cm−2)	60°C	1 M KOH	[22]
MOF@POM	Pt/C	Sustainion X37‐50 Grade T	1.78 V @ 3 A cm−2	5140 (2 A cm−2)	25°C	1 M KOH	[23]
FeNiHOF	R‐Ni	PPS diaphragm	1.81 V @ 1 A cm−2	500 (1 A cm−2)	25°C	6 M KOH	[24]
CoCe–DHBDC	Pt/C	Fumasep FAA‐3‐50	1.81 V @ 1 A cm−2	1000 (10 000 A m–2)	25°C	6 M KOH	[25]
NiFeCo LDH/NiS/NF	Pt/C	KMem AM01‐50	1.63 V @ 1 A cm−2	1000 (1 A cm−2)	60°C	1 M KOH	[26]

Currently, nickel‐iron‐based and cobalt‐based catalysts represent the forefront of research due to their abundance, tunable structures, and exceptional catalytic performance. Cobalt‐based systems are recognized for their stable reduction‐oxidation properties, whereas nickel‐iron‐based catalysts frequently achieve a lower oxygen evolution overpotential by optimizing the adsorption of oxygen intermediates. For example, Wang et al. successfully addressed the critical industrial bottlenecks of high‐current‐density durability and gas‐liquid management by doping sodium into high‐entropy layered double hydroxides (Na‐HE LDH) (Figure 2a). This strategy constructs oxygen non‐bonding states and in situ oxygen vacancies, enabling a stable lattice oxygen mechanism. Consequently, in a practical AEMWE (30 wt% KOH, 60°C), the Pt/C||Na‐HE LDH system delivered a low cell voltage of 1.55 V at 500 mA cm⁻² and operated stably for 2000 h (Figure 2b,c). Critically, this exceptional durability is enabled by the high‐entropy configuration, which thermodynamically suppresses transition metal (especially Fe) leaching to just 1.1%. Additionally, the highly hydrophilic surface of the nanosheets accelerates bubble detachment, effectively mitigating mass‐transport polarization under intense gas evolution [20]. However, from an industrial scale‐up perspective, the observed in situ surface reconstruction, forming a 20 nm amorphous layer‐may introduce mechanical interfacial stress within the membrane electrode assembly (MEA) during dynamic load cycling [21]. Future stack‐level developments must critically evaluate how such reconstructed interfaces tolerate the physical strain of rapid power fluctuations. However, outstanding intrinsic activity serves as merely the starting point for commercialization. Under industrial‐grade current densities (≥1 A cm⁻²), violent gas‐liquid scouring often triggers mechanical delamination between the catalyst layer (CL) and the porous transport layer (PTL), leading to catastrophic MEA interfacial instability. Addressing this challenge, Li et al. developed an in situ seed‐assisted heterogeneous nucleation strategy to construct a tightly anchored, dense interlayer on the nickel substrate (Figure 2d), nearly doubling the mechanical adhesion force (65.4 mN). This integrated electrode architecture enabled a record‐high AEMWE performance of 7350 mA cm⁻² at 2.0 V, with outstanding durability for 1500 h at 1 A cm⁻² under 80°C (Figure 2e). Beyond mechanical reinforcement, the hierarchical hydrangea‐like morphology optimizes gas‐liquid management by accelerating oxygen bubble detachment at a microscopic scale (bubble size ∼39 µm), mitigating mass‐transport polarization. Crucially, scaling up the electrode from 1 to 25 cm² yielded negligible voltage variation, demonstrating excellent stack‐level uniformity. This work underscores that transitioning from standalone catalyst synthesis to holistic, mechanical‐and‐fluidic‐integrated electrode engineering is the key to surviving real‐world industrial operations [22]. For large‐scale applications, future catalyst design must transition from structural tuning to addressing the multivariable challenges of high‐current‐density durability and dynamic operation compatibility. Yue et al. pioneered a MOF@POM superstructure (Figure 2f), achieving an outstanding AEMWE performance of 3 A cm⁻² at 1.78 V (80°C) and a remarkable lifespan exceeding 5000 h at 2 A cm⁻² (Figure 2g). Crucially, this work transcends conventional stability strategies by establishing a fast electron‐transfer channel (W‐Ni‐Co‐Fe) that prevents active site overoxidation and subsequent dissolution under fluctuating inputs. Furthermore, the intermediate Ni layer acts as an “elastic deformation buffer”, mitigating localized mechanical stress and structural reconstruction shocks during rapid start‐stop cycles. This multi‐functional modular design directly addresses the critical bottleneck of MEA interfacial failure under transient industrial loads, showcasing how atomic‐scale mechanical buffers and electronic highways can synergistically protect the catalyst‐PTL interface under extreme, dynamic operating conditions [23].

FIGURE 2.

Na‐HE LDH. (a) Schematic diagram. (b) AEMWE performance at 60°C in 30 wt% KOH. (c) Durability test and Fe dissolution amount. Reprinted with permission from Ref. [20]. Copyright 2025, Springer Nature. CAPist‐L1. (d) SEM image of the transition layer. (e) AEMWE performance. Reprinted with permission from Ref. [22]. Copyright 2024, Springer Nature. MOF@POM. (f) Structural schematic diagram. (g) AEM performance and durability. Reprinted with permission from Ref. [23]. Copyright 2025, American Association for the Advancement of Science. CoCe‐DHBDC. (h) Durability test. (i) Self‐made alkaline industrial test system. Reprinted with permission from Ref. [25]. Copyright 2025, Springer Nature. NiFeCo LDH/NiS/NF. (j) ADT test. (k) On‐site photo of the industrial 15 × 15 cm² system. (l) Durability test. Reprinted with permission from Ref. [26]. Copyright 2025, Elsevier.

In summary, current research on non‐precious metal catalysts for AEM is progressing beyond simple activity optimization and entering an era that emphasizes mechanical stability, interface compatibility, and system integration capabilities. Researchers are not only focusing on material design, but also increasingly adopt harsh operating conditions, such as simulating industrial start‐stop cycles and reverse currents, to evaluate catalyst degradation. Chen et al. verified the industrial application prospects of the catalyst through 20 ON/OFF cycles and 500 h of durability tests. This test directly simulated the corrosive reverse currents and transient electrochemical stresses experienced during industrial shutdowns, proving that the charge‐flexible carboxyl ligands in the organic framework can effectively suppress iron leaching and peroxidation under fluctuating renewable energy inputs [24]. To bridge the gap between laboratory‐scale molecular design and industrial‐grade application, Guo et al. developed scalable CoCe‐DHBDC electrodes (210 × 210 mm²) for integration into a kilowatt‐scale alkaline electrolyzer (Figure 2h,i). At an industrial current density of 4500 A m⁻², the system achieved an exceptionally low energy consumption of 4.11 kWh Nm⁻³ H₂ and maintained robust operational stability for over 5000 h. Crucially, to simulate the rapid power fluctuations associated with renewable electricity inputs, the electrodes underwent accelerated degradation testing (ADT), successfully withstanding 1225 dynamic start‐stop cycles with virtually no performance loss. This superb durability is molecularly rooted in the strong 3d‐4f electronic coupling between Co and Ce, which compresses the Co‐O bond and mitigates detrimental transition metal dissolution under high anodic polarization. By subjecting the large‐scale MOF electrode to such rigorous system‐level evaluations, this study exemplifies the modern transition from pursuing laboratory‐level intrinsic kinetics to securing long‐term operational resilience under volatile industrial stress profiles [25]. To validate its industrial viability, Liu et al. constructed NiFeCo LDH/NiS heterojunction catalysts on nickel foam substrates, achieving excellent results in AEMWE (Figure 2j). To further assess its industrial potential, they scaled up the NiFeCo LDH/NiS/NF electrode to a geometric area of 15 × 15 cm² and integrated it into a 2.5 kW AEMWE stack. Operating in 1.0 M KOH at 60°C, the stack delivered an industrial‐grade current density of 1 A cm⁻² at an exceptionally low cell voltage of only 1.77 V, demonstrating outstanding operational durability for over 1000 h (Figure 2k,l) [26]. To further bridge the gap between lab‐scale catalyst design and commercial‐scale device applications, demonstrating the scalability of electrode fabrication and long‐term durability in multi‐cell stacks is of paramount importance. Addressing this, Xu et al. successfully scaled up the fabrication of a D_yO_x/NiCo₂S₄ nanosheet catalyst on large‐scale nickel mesh substrates 78.5 cm² per cell) and integrated them as anodes into a kilowatt‐scale industrial stack consisting of 17 series‐connected cells (total active area of 1334 cm²). Operating at 80°C, this industrialized stack delivered a current density of 0.5 A cm⁻² at a low cell voltage of 1.97 V with an energy conversion efficiency of 74%. Most impressively, the stack maintained stable operation at 39.25 A for over 5000 h, achieving a cumulative hydrogen output of 1400 Nm³. This successful stack‐level implementation demonstrates that modulating interfacial hydrogen bond networks to accelerate reactant transport is a highly viable strategy for practical, large‐scale green hydrogen production [27]. Nevertheless, when subjected to multiple coupling stresses during the integration process, these systems tend to degrade rapidly due to complex and interacting factors. Overcoming this bottleneck requires not only systematically revealing multi‐scale failure mechanisms from atomic‐scale catalyst degradation to macroscopic system failures, but also using this in‐depth understanding to guide collaborative design across scales and among multiple components.

Therefore, the following elucidates the decay mechanism through multi‐scale failure analysis. Guided by this analysis, a cross‐scale collaborative design strategy spanning from atoms to systems is proposed, thereby paving the scientific and engineering path toward the industrialization of AEM electrolysis technology.

3. Multi‐Scale Failure Mode Analysis

Figure 3 illustrates the multi‐scale architecture of an AEMWE system, encompassing catalyst materials, catalyst‐coated membranes (CCM), MEA design, stack fabrication, and full‐system integration. A typical phenomenon in such systems is the nonlinear acceleration of degradation, for instance, as the active electrode area or the number of cells increases, the system lifetime declines more rapidly. This behavior arises from cascading failure mechanisms across different scales, driven by factors such as cyclic temperature and humidity variations [28, 29], high‐potential operation (accelerating catalyst dissolution and carbon corrosion) [30, 31, 32], chemical degradation of AEMs [33, 34], dynamic load fluctuations, stack‐to‐stack inconsistencies due to uneven clamping or flow distribution, liquid water management failures (flooding or dry‐out), carbon corrosion in PTLs, and coldstart stresses [35, 36, 37].

FIGURE 3.

Multi‐scale architecture and nonlinear degradation behavior of alkaline AEMWEs.

As the core unit of industrial AEM electrolyzers, the reliability and durability of key components including the membrane electrode assembly (MEA), bipolar plates (BPPs), and their assembly integrity within the stack directly determine the electrolyzer service life, efficiency retention, and industrial application potential [38, 39]. To achieve the industrial target of 50 000 h of stable operation for the MEA component and over 10 000 h of the full system by 2030 (as shown in the inset of Figure 3), a multi‐scale failure mode analysis is required. This analysis must encompass degradation phenomena ranging from the nanoscale such as catalyst particle dissolution to the macro‐scale such as performance decay caused by uneven fluid distribution within the stack. Such an approach is crucial for identifying industrialization bottlenecks and guiding the design of robust systems [37, 40]. It should be noted that systematic degradation studies covering catalyst, MEA, stack, and system scales remain relatively limited for AEMWE compared with PEM water electrolyzers and fuel‐cell technologies. Therefore, some degradation phenomena discussed in this section are supported by evidence from PEMWE and fuel‐cell systems (Figures 4, 5, 6, 7). These examples are included not as direct equivalents of AEMWE degradation, but as representative case studies illustrating multiscale failure‐analysis methodologies and degradation pathways that may also emerge in AEMWE owing to the similarities in membrane‐electrode architectures, porous transport structures, bipolar plates, flow fields, and stack configurations. Throughout this section, special attention is given to distinguishing experimentally verified AEMWE degradation mechanisms from transferable insights derived from related electrochemical energy‐conversion technologies.

FIGURE 4.

(a–c) Three common deactivation mechanisms of catalysts. (d) OH blocks the active site. Reprinted with permission from Ref. [46]. Copyright 2024, American Chemical Society. (e) Ionomer degradation blocks and poisons the active metal Pt. Reprinted with permission from Ref. [48]. Copyright 2025, Springer Nature. (f) Deformation of the catalyst. Reprinted with permission from Ref. [50]. Copyright 2022, John Wiley and Sons. (g) Phase transition of the catalyst, and the α‐LDH phase is unstable. Reprinted with permission from Ref. [52]. Copyright 2025, American Chemical Society. (h) Dissolution and deposition of iron in Fe–MOH. Reprinted with permission from Ref. [55]. Copyright 2020, Springer Nature. (i) Precipitation of Mo in the form of MoO₄ ²⁻. Reprinted with permission from Ref. [57]. Copyright 2022, John Wiley and Sons. (j) Dissolution rate of Cu in the liquid electrolyte and MEA environment. Reprinted with permission from Ref. [58]. Copyright 2021, Springer Nature.

FIGURE 5.

(a) The failure mechanisms of MEAs (three synergistic components of MEAs: (b) membrane; (c) catalyst layer; (d) GDL).

FIGURE 6.

(a) The failure mechanisms of BPPs. (b) Cross sectional scheme of a PEM electrolyzer anode. The dashed circle indicates the area of contact between the BPP and the current collector (thin mesh) and the OER catalyst layer. This region is the most prone to degradation due to the acidity of the environment under high potentials. Reprinted with permission from Ref. [84]. Copyright 2016, Elsevier. (c) The gold‐coated metal bipolar plates used for fuel cells in the 3‐kilowatt stack failed. Reprinted with permission from Ref. [85]. Copyright 2024, Elsevier. (d) Comparison of ICR values for different coating materials. Reprinted with permission from Ref. [89, 90]. Copyright 2017, Elsevier.

FIGURE 7.

(a) The failure mechanisms of stack assembly. (b) Surface pressure and deformation size distribution. Reprinted with permission from Ref. [43]. Copyright 2025, Elsevier. (c) Local overheating caused by current crowding, temperature gradient. Reprinted with permission from Ref. [94]. Copyright 2024, Elsevier. (d) Uneven fluid distribution (H₂ mole fraction and partial pressure distribution). Reprinted with permission from Ref. [96]. Copyright 2024, Multidisciplinary Digital Publishing Institute.

3.1. Degradation Mechanisms of OER Catalysts

The failure mechanisms of electrocatalysts are of paramount importance for elucidating structure‐activity relationships, optimizing material design, and enhancing durability. The performance degradation of electrocatalysts primarily arises from three factors: blockage of active sites, morphology or phase changes, and dissolution or mechanical detachment of active species (Figure 4a–c).

3.1.1. Active Sites Blockage

One core physicochemical process driving material failure is the irreversible adsorption and accumulation of reaction intermediates or poison molecules on active sites, leading to physical isolation or chemical passivation of those sites. During the electrocatalytic reaction interfacial mass exchange, carbon‐containing substances physically deposit on the catalyst. This carbon deposition blocks pores of various sizes, hindering access of active sites to the actual reaction interface, thereby impairing catalytic function that effectively renders the active sites unavailable. Alternatively, carbon deposition may directly obstruct the active centers, compromise the catalyst primary catalytic region and lead to catalyst deactivation [41, 42]. Amorphous carbon formation in certain chemical reactions triggers the sintering and fragmentation of nickel particles. This process initiates a self‐accelerating deactivation cycle that ultimately raises the reactor voltage [43]. Furthermore, CO can induce carbon deposition, and the rapid deactivation of PtIn/Al₂O₃ catalysts is closely associated with in situ generated CO. CO strongly absorbs to Pt active sites, particularly isolated Pt atoms, obstructing these sites and promoting the sintering of Pt nanoparticles. This, in turn, expedites carbon deposition and other undesired side reactions, leading to the rapid deactivation of the catalyst [44]. Notably, carbon deposition can be reversed via gasification regeneration, whereas chemisorption‐induced poisoning typically results in irreversible deactivation. For example, alkali metal poisoning occurs in some reactions, and studies have shown that K adsorbs near the Mn top and bridging oxygen sites of Mn/TiO₂ catalysts, resulting in a decrease in the specific surface area of the catalyst, a reduction in the proportion of surface Mn and chemisorbed oxygen, and a decline in the number of surface acid sites [45]. Transition metals have become the first‐choice catalysts for industrial alkaline electrolysis due to their high cost‐effectiveness. However, their strong binding energy with adsorbed hydroxyl groups often leads to blocking of active centers, significantly affecting catalytic performance [46] (Figure 4d). Sulfur‐containing species are also common catalyst poisons, for instance, H₂S can deactivate Cu/ZnO/Al₂O₃ (CZA) catalyst by initiating the dynamic migration of ZnO species and altering the structure of active sites [47]. In addition, certain ionomers (e.g., containing SO³⁻ groups) can undergo severe degradation and poison active metals, such as by suppressing Pt surface activity [48] (Figure 4e). Notably, complex synergistic effects can arise between different poisons. In‐depth study of CrMn_1.5O₄ catalysts has revealed a “co‐poisoning” effect of K and SO₂ [49]: when both coexist, the interaction between SO₂ and K will inhibit electron transfer and reduce the energy barrier for dehydrogenation, thereby mitigating the toxicity of K alone to some extent. This suggests that the coexistence of different poisons may produce antagonistic effects, offering a new perspective for designing anti‐poisoning catalysts. Overall, these two primary mechanisms (physical coverage or chemical occupation of active sites) directly contribute to the decline in the catalyst efficiency.

3.1.2. Morphology or Phase Changes

Under severe acidic/alkaline conditions or high current densities, catalysts often undergo dynamic structural changes that leads to deactivation through reconstruction, phase transitions, and morphological evolution. These processes ultimately reduce the number of accessible active sites or diminish intrinsic activity. For example, Figure 4f shows that an electrocatalyst deforms during flow cell testing, with its initial morphology transforming into a roughened, dendritic structure, which adversely affects both activity and durability [50]. Under high temperature operating conditions, supported noble metal catalysts tend to lose their activity significantly over time. This deactivation is generally attributed to the sintering of nanoparticles, specifically, the growth of larger particles at the expense of smaller ones [51]. In many cases, pre‐catalysts usually undergo phase transition under reaction conditions to form highly active heterostructures, however, the long‐term stability of these metastable structures remains a concern. As illustrated in Figure 4g, a catalyst may spontaneously reconstruct from an amorphous oxide in its initial state to a distorted α‐LDH phase. Since the α‐LDH phase is unstable under electrochemical conditions, it continues to change into the more stable γ‐LDH phase. Such long‐term evolution, along with catalyst dissolution under industrially relevant current densities, warrants careful attention [52]. Even after an active phase is formed, irreversible degradation can occur under sustained oxidizing potentials and repeated exposure to reaction intermediates. This degradation may involve a transition from a crystalline phase to a more disordered amorphous state, or the segregation of active components, both of which lead to a decline in catalytic activity [53].

3.1.3. Dissolution/Mechanical Detachment

During the OER in water electrolysis, catalysts may generate unstable intermediates or compounds as a result of oxidation. Additionally, catalyst dissolution can occur in aggressive acidic or alkaline environments under elevated temperatures and applied voltages. Furthermore, mechanical stress concentration arising from bubble formation at the three‐phase boundary can lead to delamination of the catalysts layer [54, 55]. Markovic et al. demonstrated that the dissolution and redeposition of iron in Fe‐MOH can generate dynamically stable iron active centers at the interface. Apart from regulating the iron concentration in the electrolyte, robust interactions between iron and the matrix play a crucial role in governing the average number of iron active centers at the solid/liquid interface [56](Figure 4h). Zhang et al. investigated the evolution of Mo within a Ni₄Mo alloy and observed its dissolution as MoO₄ ²⁻, followed by re‐adsorption and subsequent polymerization into MoO₄ during the reaction [57] (Figure 4i). Zhong et al. explored the redistribution behavior in nanoporous materials after dealloying in liquid electrolytes compared to MEA environments, noting that the diffusion of dissolved copper is significantly slower in the latter, leading to decelerated dealloying kinetics [58] (Figure 4j). Waterhouse et al. provided valuable insights into the degradation mechanism of NiFe‐LDH catalysts during OER processes and proposed a direct synthetic method for a more durable cationic vacancy‐engineered NiFe‐LDH catalyst. This work established a foundation for the future advancement of highly stable and efficient OER electrocatalysts for practical applications in water electrolysis [59].

3.2. Membrane Electrode Assembly Degradation

The MEA serves as the “heart” of AEM electrolyzers, facilitating the anodic OER and cathodic HER while enabling multi‐phase mass transport of ions, electrons, gaseous reactants/products, and liquid water [60, 61]. As illustrated in Figure 5a, the MEA comprises three synergistic components: a 10–100 µm AEM, 50–150 µm CLs at the anode and cathode, and 200–400 µm PTLs [62, 63]. The degradation mechanisms of these components span chemical, mechanical, and electrochemical pathways that interact across multiple scales.

The AEM (Figure 5b), which serves as a critical barrier for OH⁻ transport and gas separation, undergoes chemical degradation via the breakdown of quaternary ammonium functional groups (e.g., HC‐N(CH₃)₃ ⁺), leading to membrane thinning and reduced ionic conductivity [64]. Concurrently, mechanical degradation manifests as cracking and pinholes, driven by stress concentration at flow channel edges (due to PTL compression) and cyclic swelling/shrinking during humidity variations. These structural defects increase gas crossover, elevating explosion risks and promoting radical generation that further accelerates degradation [65]. The CL (Figure 5c), a porous matrix composed of catalyst nanoparticles (e.g., Pt‐based for HER and NiFe‐based for OER), carbon supports and anion exchange ionomers, suffers multi‐modal degradation [32]. Catalysts particles (typically 2–5 nm) undergo dissolution, depletion, segregation, reconstruction, and aggregation under electrochemical stress. For instance, Pt oxidation to soluble species or Fe leaching from NiFe‐LDHs reduces the electrochemical active surface area (ECSA), while Ostwald ripening and aggregation lead to the formation of larger particles (>10 nm) [66, 67]. Carbon supports undergo corrosion via the reaction (C + 2H₂O → CO₂ + 4H⁺ + 4e⁻) under high anodic potentials (>1 V vs. RHE), where corrosion kinetics become significantly accelerated. This degradation behavior triggers the collapse of triple‐phase boundaries (TPBs) and a notable reduction in CL porosity after long‐term operation, as verified by in situ x‐ray computed tomography (XCT) characterization [68]. The application of in situ XCT is equally vital for AEMWEs to capture the spatial‐temporal degradation of alkaline CLs, particularly the mass transport losses caused by catalyst‐ionomer detachment and local structural collapse. Quantitative 3D reconstructions from XCT can establish a direct link between the physical deterioration of the AEMWE electrode porous structure and the degradation of its gas‐liquid transport pathways during long‐term electrolysis. Anion exchange ionomers, which play a pivotal role in OH⁻ transport, are susceptible to degradation through radical attack on their ether linkages and quaternary ammonium groups. Beyond their chemical stability, anion exchange ionomers critically regulate the local reaction microenvironment around non‐PGM catalysts in AEMWE catalyst layers. Unlike liquid alkaline electrolytes, the ionomer phase defines confined OH⁻ ‐transport pathways, local water activity, and catalyst accessibility within the membrane‐electrode interface. Therefore, localized ionomer dry‐out, excessive swelling, or non‐uniform ionomer distribution can disrupt continuous OH⁻ conduction and generate spatially heterogeneous current density. In dry or poorly hydrated regions, increased ion‐transport resistance leads to local overpotential accumulation, which may accelerate the reconstruction, oxidation‐state fluctuation, and dissolution of NiFe or Co‐based OER catalysts. Conversely, excessive ionomer coverage can block active sites and hinder gas removal, promoting local bubble accumulation and mass‐transfer polarization. These molecular and interfacial‐level processes can further propagate to the mesoscale by destabilizing the triple‐phase boundary, reducing catalyst utilization, altering catalyst‐layer porosity, and weakening mechanical adhesion between the catalyst layer and porous transport layer. Thus, AEM/ionomer degradation is not an isolated polymer‐stability issue, but a key trigger that couples catalyst phase evolution, interfacial transport failure, and MEA‐level performance decay [14, 69]. Additionally, dissolved metal ions (e.g., Fe²⁺ and Cr³⁺) leached from corroded PTLs can form inactive complexes with ionomers, leading to a marked increase in ion transport resistance [70]. Furthermore, gas bubbles can block active sites, exacerbating kinetic losses [71]. In the AEMWE, the PTL acts as a bridge between the CL and the BPP (Figure 5c). However, bubbles produced by the electrode may obstruct the PTL, covering active sites and potentially harming the interface structure. This obstruction can lead to PTL degradation through material‐specific mechanical fatigue and chemical corrosion, ultimately resulting in electrode deactivation and reduced operational lifespan [72]. Nickel‐based PTLs (e.g., felt and knitted mesh) offer high conductivity but suffer Ni oxidation and dissolution. Inconel 601 exhibits good thermal stability yet limited conductivity. Stainless steel (SS) is cost‐effective but leaches Fe and Cr ions that can contaminate MEAs. Mechanically, nickel foam undergoes plastic deformation (15%–25% porosity loss), whereas nickel knitted mesh retains better elastic recovery [73]. Chemically, oxidative corrosion increases both transport resistance and interfacial contact resistance (ICR)‐SS PTLs exhibit 65–75 mΩ cm² ICR, though stacked configurations can reduce to <10 mΩ cm² [74]. Mitigation requires corrosion‐resistant coatings and elastic structural designs to balance conductivity, durability, and mass transport properties.

These multi‐scale degradations are inherently interconnected: AEM chemical degradation and membrane pinholes can promote radical formation and gas crossover, ionomer dry‐out or redistribution can induce local ion‐transport failure and catalyst reconstruction, and corrosion products from PTLs or BPPs can contaminate both the CL and the AEM [1, 75]. Therefore, addressing MEA durability requires integrated strategies that simultaneously optimize polymer chemistry, ionomer‐catalyst compatibility, catalyst‐layer hydration, mechanical integrity, and uniform mass transport. Such cross‐scale understanding is essential for linking molecular‐level AEM/ionomer degradation to electrode‐scale structural failure and device‐level performance decay in industrial AEM electrolyzers [64, 76, 77].

3.3. Bipolar Plates Corrosion

In AEMWEs, the bipolar plate (BPP) serves as a core multifunctional component, performing several critical roles (Figure 6a). Primarily, it ensures the uniform distribution of reactants (water and ions) and efficient removal of products (hydrogen and oxygen) through its surface flow field design [78]. Second, it acts as an electrically conductive element, facilitating electron transport between adjacent single cells to form a series electrical circuit. Furthermore, the BPP physically separates evolved hydrogen and oxygen to prevent gas crossover while simultaneously conducting the heat generated during electrochemical reactions [79]. This multifunctionality implies that the performance of the BPP directly impacts the overall efficiency, safety, and operational lifetime of the electrolyzers.

In an AEM electrolyzer, the BPP operates under extremely harsh conditions. Electrochemically, the anode side is exposed to high potentials exceeding 1.8 V while immersed in a hot, concentrated alkaline solution, creating an electrochemical‐chemical dual assault [80]. Chemically, even when pure water is fed, a localized strongly alkaline environment (pH > 14) forms at the AEM surface due to OH⁻ serving as the primary charge carrier. Physically, the BPP endures frequent thermal cycling and pressure fluctuations, especially under intermittent operation when coupled with renewable energy sources. Moreover, atomic hydrogen generated at the cathode side can permeate into the metal lattice, inducing hydrogen embrittlement and further compromising the material's mechanical integrity [81].

Consequently, the corrosion and subsequent failure of BPPs in AEM electrolyzers manifest across three interrelated dimensions: coating failure, substrate failure, and functional failure. These degradation mechanisms are illustrated in Figure 6, which details the failure pathways for each mode. Addressing this complex, multi‐scale degradation challenge necessitates coordinated efforts across materials science, electrochemistry, and engineering design. Coating failure encompasses pore‐induced galvanic corrosion, uniform transpassive dissolution, and spallation [82, 83]. Pore‐induced galvanic corrosion arises from porous coatings, which establish micro‐galvanic cells between exposed substrate and intact coating areas (Figure 6b) [84]. Transpassive dissolution is driven by the high‐potential environment, while spallation is often triggered by a mismatch in the coefficient of thermal expansion between the coating and substrate. For instance, as shown in Figure 6c, significant agglomeration and spallation of an Au coating on a 316L SS BPP were observed during New European Driving Cycle (NEDC) durability testing [85]. This exposure of the underlying Ti interlayer and 316 L substrate initiated micro‐galvanic corrosion, accelerating oxide formation, and leading to transpassive dissolution of the oxide film under the high potential of 1.6 V. Consequently, the ICR sharply increased from an initial 5.4 mΩ cm² to 137.8 mΩ cm². Substrate failure includes pitting corrosion (initiated by chloride ions penetrating the passive film), intergranular corrosion, elemental leaching, and selective dissolution [86]. In AEMWEs, while the alkaline working medium mitigates some acid‐induced pitting, the thermodynamic instability of protective coatings and substrates under high operating potentials and dynamic loads still triggers similar transpassive dissolution and interface degradation, making these failure modes equally critical for AEM BPP design. These substrate degradation pathways ultimately lead to functional failure. For example, the formation of passive films and coating spallation can elevate the ICR from the U.S. Department of Energy (DOE) target of < 10 mΩ cm² to over 100 mΩ cm². Dissolved metal ions (e.g., Fe and Cr) can poison the catalyst layer, reducing the HER activity of Pt/C by 25%–40%, and degrade the anion exchange membrane, decreasing OH⁻ conductivity by 30%–50%. Corrosion by‐products (e.g., iron oxides) can clog flow channels, causing reactant starvation and localized performance loss within the electrolyzer stack. Functional failure is characterized by the progressive degradation of component performance [87, 88]. As shown in Figure 6d, Lædre et al. investigated the corrosion resistance and pre‐/post‐corrosion ICR changes of materials like titanium, molybdenum, 254 SMO (a high‐molybdenum austenitic stainless steel), tungsten, 316 L stainless steel, 304 L stainless steel, niobium, and tantalum under high potential and low pH conditions [89, 90]. With the exception of tungsten, all materials exhibited an increase in ICR, representing a classic manifestation of functional failure. Despite the differences in operating pH, the fundamental challenge of thermodynamic oxidation of metallic substrates under high‐potential operation is shared, meaning that the candidate materials screened in PEMWE study face analogous surface passivation and functional failure risks in AEM electrolyzers. This correlation between electrochemical corrosion and interfacial resistance degradation serves as a critical baseline for AEMWE BPP development.

3.4. Stack Assembly Failure Modes

Figure 7a illustrates the macro‐assembly of an AEM electrolyzer stack and highlights key failure modes originating from its assembly and multiphysics interactions. Multiscale degradation in such systems often initiates from structural and operational nonuniformities, ultimately impairing overall efficiency and durability.

Structural and mechanical failures frequently result from improper clamping pressure. Excessive pressure compresses the PTL, reducing porosity by 20%–30% and thererby impeding mass transfer. In contrast, insufficient pressure elevates the contact resistance between the BPP and the PTL [37, 91]. Gaskets (e.g., EPDM) degrade under cyclic thermal/humid conditions, losing elasticity and causing electrolyte leakage (>0.1 mL h⁻¹) that dilutes reactants and corrodes BPPs. Moreover, MEA‐BPP misalignment creates edge gaps, inducing localized high current density. Figure 7b outlines a coupled simulation approach designed to diagnose such assembly‐induced failures. By integrating a volume‐of‐fluid (VOF) model with a conventional multi‐physics model, the oxygen evolution rate obtained from the multi‐physics solution is used as the inlet condition for the VOF simulation [43]. The resulting bubble coverage (β) at the anode‐channel‐PTL interface determines the bubble overpotential (η _bubble), which is then fed back to correct the electrochemical model, while the active surface area is scaled by (1−β)a ². This coupled analysis reveals that pressure fluctuations (±0.5 MPa) during assembly can reduce voltage uniformity by up to 30%, with misaligned stacks exhibiting 15%–25% greater voltage variation than aligned ones, directly linking assembly tolerances to performance degradation [92, 93]. Edge effects and nonuniform current distribution are further aggravated by uneven gas‐liquid transport [43]. As shown in Figure 7c, the gaseous volume fractions of H₂ and O₂ are highest at the cell outlet and peak at the membrane‐PTL interface (the reaction zone), while lower gas fractions appear at the outer edges of the cell [94]. Notably, the H₂ volume fraction is roughly twice that of O₂, preserving the 2:1 stoichiometry of water splitting. Such heterogeneous gas distribution, when combined with channel blockage (from BPP corrosion products) or mismatched flow geometry, can cause local electrolyte starvation or flooding. In turn, this promotes current crowding (edge current densities 2–3 times the core value), creating hotspots >30°C above the core temperature and accelerating radical‐induced AEM degradation and PTL oxidation [95]. Figure 7d quantifies the gasdistribution dependence on operating conditions, displaying the gaseous volume fraction for KOH concentrations of 0.01–1 M at 2 V (left) and for cell voltages of 1.4–2 V in 1 M KOH (right) [96]. These profiles confirm that under high current densities (>1 A cm⁻²), H₂ purity can drop from 99.9% to below 96.5%, raising safety risks and accelerating MEA degradation [97, 98]. The data further validate that uneven fluid distribution (e.g., at flow rates <10 mL min⁻¹) exacerbates cell‐to‐cell voltage variation, nearly doubling the degradation rate compared with optimized wet‐cathode operation. When fluid and current distribution are uneven, cell‐to‐cell voltage variation can be exacerbated, accelerating degradation rates by nearly twofold under unfavorable operating conditions, such as dual electrolyte feeding compared to dry cathode configurations [99]. In AEMWE operation, the simultaneous evolution of gas bubbles (H₂/O₂) and liquid electrolyte feed mimics these exact transport imbalances, where localized gas‐trapping and liquid starvation similarly trigger localized current crowding and accelerated chemical degradation of the AEM. This correlation between fluidic maldistribution and accelerated membrane decay is highly instructive for AEMWE systems, which share analogous flow field and MEA architectures.

Stack‐level failures are often inherently interrelated with component‐level degradation. For instance, BPP corrosion can lead to increased ICR and contamination of adjacent components, which in turn accelerates MEA degradation. Similarly, MEA failure may cause local overheating or gas crossover, adversely affecting neighboring cells and propagating failure throughout the stack. To effectively mitigate multi‐scale failure modes and ensure the reliable, long‐term operation of industrial AEM electrolyzers, a systems‐level approach is paramount. Such an approach must integrate robust design principles, material compatibility, and controlled operating conditions [100].

The analysis of failure mechanisms reveals that the durability bottleneck of AEM electrolyzers arises from a complex failure network characterized by multi‐scale, multi‐physics coupling. At the atomic‐nanoscale, OER catalysts deactivate because active sites become blocked, structures reconstruct, and elements dissolve. At the micro‐scale, MEA suffer from ionomer degradation, carbon support corrosion, and instability of the three‐phase interface. At the device macro‐scale, BPPs undergo electrochemical corrosion and coating delamination under high potential, causing a sharp increase in contact resistance and metal ion contamination. At the system‐integration level, stacks suffer chain reactions triggered by uneven assembly stress, imbalanced gas‐liquid distribution, and seal aging.

Crucially, under realistic dynamic operating conditions, the loss of pore structure within the CL which directly triggers a reduction in ECSA, catalyst detachment, and agglomeration. This acts as the primary and most immediate initiator of performance attenuation. This structural collapse is fundamentally driven by the corrosion of carbon supports (or alternative substrate materials) under high potentials and suboptimal hydrothermal management, highlighting a critical dependence on system‐level control precision [85, 101]. Consequently, a multi‐scale regulatory strategy is essential: at the material level, the engineering of robust framework substrates with high structural stability is required to maintain pore integrity; at the system level, the control precision of operational parameters, such as potential and humidity, must be significantly enhanced [102]. While current worldwide industrial efforts exhibit a notable deficit in sophisticated system‐level control, there remains profound research and development potential in the fundamental optimization of catalysts and MEAs.

These cross‐scale failure modes interact synergistically through coupled mass transport, charge transfer, and mechanical stress, forming a positive‐feedback loop that accelerates performance degradation from materials to devices and ultimately to system‐level operation. While some of the degradation phenomena discussed above have been more extensively investigated in PEM electrolyzers and fuel‐cell systems than in AEMWE, the underlying failure‐analysis methodologies and multiscale degradation frameworks provide valuable insights into the durability limitations of industrial AEMWE systems. As AEMWE technology progresses toward larger‐scale deployment, translating these mechanistic understandings into AEM‐specific degradation models will be essential for elucidating failure propagation across scales and guiding rational system design. Therefore, bridging the gap from “Lab” to “Fab” requires moving beyond isolated component optimization toward a holistic strategy based on cross‐scale collaborative design and integrated optimization. Such an approach is critical for mitigating the initiation, propagation, and coupling of degradation processes across multiple length scales. In the following sections, we systematically discuss cross‐scale design strategies aimed at achieving both high current density and long‐term durability in industrially relevant AEMWE systems.

4. Multi‐Scale Performance Optimization: From Electrode Fabrication to System Integration

Based on the analysis of cascading failure mechanisms in AEM electrolyzers from the atomic‐scale to the system level, it becomes evident that overcoming the positive feedback loop of “performance degradation accelerating failure” necessitates moving beyond the isolated optimization of individual components. Instead, a full‐chain, cross‐scale collaborative design approach spanning materials, devices, and systems is essential. From an industrial perspective, the optimization strategies discussed in this section are organized not merely according to component hierarchy, but around the key bottlenecks that currently limit large‐scale AEMWE deployment, including high‐current‐density durability, MEA interfacial stability, gas‐liquid management, stack uniformity, component compatibility, and dynamic operational robustness. Drawing on the mechanistic insight outlined above, this chapter systematically presents a set of multiscale performance‐optimization strategies. At the core of these strategies lies innovation in MEA architecture, aimed at fundamentally optimizing the reaction interface and transport pathways. At the macroscale, bionic and structured flow‐field design ensures uniform gas‐liquid distribution and efficient mass and heat transfer. Advanced multivariable collaborative control strategies serve as the system “central nervous system”, enabling intelligent adaptation to complex operating conditions and actively suppressing degradation mechanisms. These three elements, which are innovative MEA architecture, advanced flow‐field design, and intelligent control, synergistically integrated to form a comprehensive framework spanning electrode fabrication to system operation. Together, they constitute an integrated pathway toward the development of industrial‐grade AEM electrolysis systems capable of achieving high current density, extended operational lifespan, and robust performance under dynamic conditions.

4.1. MEA Architecture Innovation

As the core of electrochemical energy conversion devices, the architecture of membrane electrodes directly determines the efficiency of reaction sites, mass transfer paths, and conductive networks. Tracing its developmental trajectory, the evolution of membrane electrode architecture has undergone a transformative progression, from conventional discrete assembly, through structurally optimized design, to fully integrated configurations [103, 104, 105].

4.1.1. First Generation: Powder Catalysts and Coating Technology

Early membrane electrodes were predominantly fabricated using powder catalysts. This approach fundamentally relies on dispersing high‐specific‐surface‐area catalyst nanoparticles (e.g., Pt/C) into catalyst inks containing solvents and ionomers (Figure 8a), followed by coating techniques (e.g., slot‐die coating, spray coating, and blade coating) to deposit the ink onto proton exchange membranes (PEM) or PTL (Figure 8b–d) [106]. The innovation in this route has centered on optimizing ink formulations and coating processes. At the laboratory level, researchers regulate the formation of the three‐phase boundary (TPB) and proton conduction pathways by adjusting the ionomer‐to‐carbon ratio and solvent system [107, 108]. At the industrial level, key engineering challenges include maintaining slurry stability, ensuring coating uniformity over large areas, maximizing catalyst utilization, and achieving production throughput [109]. Nevertheless, this inherently “stacked” architecture inevitably introduces several limitations: high contact resistance between catalyst particles, significant mass transfer resistance arising from uneven ionomer distribution, and susceptibility to catalyst delamination during long‐term operation.

FIGURE 8.

(a) Preparation process of catalyst ink. (b–d) Coating methods. Reprinted with permission from Ref. [103]. Copyright 2024, Springer Nature. (e) Self‐supported catalyst. Reprinted with permission from Ref. [107]. Copyright 2017, John Wiley and Sons. (f–h) Integrated electrode and SEM images. Reprinted with permission from Ref. [114]. Copyright 2024, Royal Society of Chemistry.

4.1.2. Second Generation: Self‐Supported Catalyst Electrodes

To overcome the inherent limitations of powder‐based catalysts, self‐supported catalyst electrodes have emerged as a promising alternative. These electrodes move beyond conventional powder supports by directly constructing porous catalytic architectures integrated with three‐dimensional (3D) continuous conductive networks. They are broadly classified into two categories: homogeneous nanoelectrodes and heterogeneous nanoelectrodes (Figure 8e) [110]. For instance, a self‐supported porous NiFe alloy electrode Featuring three‐dimensionally connected microcapillary channels has been fabricated via tape casting technology. This structure synergistically optimizes both mass transport pathways and mechanical robustness. Through crystallographic engineering, a self‐healing oxyhydroxide layer of ∼15 nm was precisely formed on the electrode surface, establishing a dynamic equilibrium between Fe dissolution and substrate replenishment from the underlying substrate. This enables stable operation for over 1100 h under industrial current density and harsh operating conditions [111]. Choi et al. developed a superhydrophilic NiFe/ATNT electrode by depositing porous NiFe nanoparticles on annealed TiO₂ nanotubes. The resulting 3D hierarchical porous structure significantly enhances the surface roughness and wettability, promoting rapid desorption of oxygen bubbles, effectively minimizing dead zones, and improving both OER activity and long‐term electrolyzer stability [112]. Jiang et al. designed an anisotropic grooved micro‐nano structured porous electrode. The gradient grooves generate asymmetric Laplace pressure, which in combination with buoyancy, directionally guides the rapid bubble transport and desorption along the grooves. Coupled with low‐adhesion nanosheet arrays, this design reduces bubble size and substantially enhances the efficiency of both OER and HER, as well as overall water splitting performance [113]. Self‐supported electrodes markedly improve mechanical stability, electrical conductivity, and interfacial resistance compared to conventional powder‐based electrodes. Nevertheless, their preparation processes are often complex, and the interfacial bonding with the membrane still requires further optimization.

4.1.3. Third Generation: Novel Electrodes‐Integration of Structure and Function

The state‐of‐the‐art has now evolved toward monolithic electrodes, representing a fundamental paradigm shift in architectural design philosophy. Rather than treating the CL, PTL, and even the PEM as discrete components to be assembled later, this approach achieves multiscale functional synergy and integration at the material synthesis stage through advanced manufacturing techniques such as in situ synthesis, 3D printing, templating methods, or chemical vapor deposition [114]. For example, Hu et al. systematically optimized the ion conduction, interfacial contact, and mechanical stability by constructing an anode architecture comprising a 3D powder catalyst layer synergized with dual ionomers (AEI/CEI), integrated with a highly conductive b‐PTP membrane. This electrode structure enabled an AEMWE to operate stably for over 800 h at ultra‐high current density of 10 A cm⁻² with a voltage of only 2.3 V, demonstrating a practical and robust electrolyzer structure design strategy [115]. Wang et al. developed a typical innovative structure of the third‐generation integrated electrode. The core of this method lies in the direct construction of 3D ordered and interlocked catalytic nanoarrays on the anion exchange membrane through a swelling‐assisted transfer strategy (Figure 8f–h). This approach abandons the traditional high‐temperature and high‐pressure thermal transfer method. Through direct membrane deposition, it achieves a perfect combination of the self‐supported catalyst layer and the membrane under mild conditions, forming vertically oriented through‐pores and directionally arranged ionomer transport channels. This integrated structure eliminates the interlayer interface, enabling efficient and synergistic transport of electrons, ions, and mass. Ultimately, under pure water electrolysis conditions, it achieves a high performance of 3.61 A cm⁻² @2.0 V and stable operation for 700 h, marking a paradigm shift in the membrane electrode architecture from “coating‐assembly” to “in situ growth‐integration” [116, 117].

The core advantage of the monolithic electrode lies in its “structure is function” characteristic: by eliminating interlayer interfaces at the design source, they enable efficient and synergistic transport of charges (electrons and ions), species (reactants and products), and thermal energy. This not only significantly enhances catalyst utilization and device power density but also establishes a robust foundation for long‐term stability under extreme operating conditions. This architectural innovation marks a paradigm shift in membrane electrode technology from the traditional “coating‐assembly” manufacturing paradigm to a bio‐inspired “design‐growth” paradigm.

4.2. Industrial Scale‐Up Manufacturing

4.2.1. Roll‐to‐Roll (R2R) Manufacturing of Powder Catalysts

To transition from lab‐scale materials to commercial‐scale AEMWE production, the electrode fabrication process must shift from batch‐wise, low‐throughput techniques (e.g., manual coating, ultrasonic spraying, and electrostatic spraying) to highly automated, continuous R2R manufacturing. While lab‐scale methods prioritize catalyst utilization and immediate cell testing, continuous R2R processing prioritizes high‐speed throughput, batch‐to‐batch repeatability, and precise large‐area uniformity of the catalyst layer. In slot‐die R2R coating, the wet coating thickness (T) is governed by the fundamental coating hydrodynamic equation [118, 119]:

$$T=\frac{Q}{BV}$$

where Q represents the volumetric slurry flow rate, B is the coating width, and V is the line speed of the moving web substrate.

Consequently, achieving a highly uniform catalyst loading across large‐area membranes or gas diffusion electrodes depends heavily on suppressing the fluctuations of slurry flow rate (Q), stabilizing the web line speed (V), and maintaining micrometer‐level mechanical alignment tolerances.

4.2.1.1. Mitigation of Slurry Flow Rate (Q) Fluctuations

The stability of the volumetric flow rate (Q) is primarily governed by slurry rheological properties, feeding system fluctuations, and the slot‐die head design. First, to mitigate variations in slurry rheology (e.g., viscosity, temperature, and particle aggregation) during continuous operation, auxiliary units such as continuous agitation, double‐jacketed temperature control, and vacuum degassing are integrated. Second, feeding pulsations are minimized through high‐precision progressive cavity pumps, ensuring continuous fluid transport with a dosing accuracy of ±2%. Finally, the internal flow channel of the slot‐die head is custom‐machined based on the slurry rheological profile, thereby minimizing discharge non‐uniformity across the entire coating width (typically <2%) [118].

4.2.1.2. Stabilization of Web Line Speed (V)

Any minor velocity perturbation in substrate transport directly compromises coating uniformity in the machine direction. To suppress these fluctuations, the coating backup roll is driven by closed‐loop servo motors. Advanced velocity feedback controls restrict the web line speed fluctuation to a critical threshold of less than 0.3% [118].

4.2.1.3. Equipment Fabrication and Assembly Precision

The mechanical alignment and manufacturing tolerances of the physical tooling establish the baseline for coating thickness control. Specifically, the straightness of the slot‐die lip and the cylindricity tolerance of the precision‐ground backup roll are both strictly controlled within ≤ 2 µm, with robust structural fixtures ensuring strict parallelism between the die lip and the roll axis. Furthermore, gap positioning accuracy is maintained within a tight micrometer‐level range across the entire width via a closed‐loop translation system. This system integrates high‐precision servo motors, ultra‐precise lead screws, and optical grating scales featuring a 0.1 µm resolution [120].

4.2.1.4. Real Time Detection and Monitoring of Coating Uniformity

To achieve active quality control during continuous mass production, online real time nondestructive metrology is integrated into the coating line. Utilize online optical (spectral) and x‐ray fluorescence/transmission techniques [121, 122, 123], dynamically measure and feedback the catalyst layer thickness and loading (mg cm⁻²) in real time. Scalable R2R manufacturing of catalyst‐coated membranes, achieved via slot‐die coating with a 500‐fold throughput increase, relies heavily on ink formulation optimization (Figure 9a,b). To guarantee coating quality during high‐speed production, in situ x‐ray monitoring serves as a critical diagnostic tool. This real time technique characterizes the dynamic dispersion state of IrO₂ inks, particle agglomeration, and structural evolution of the catalyst layers during drying. Integrating x‐ray diagnostics into scalable processes ensures precise control over film uniformity and interfacial adhesion, thereby yielding high‐performance electrolyzers that match lab‐scale spray‐coated counterparts while significantly lowering industrial manufacturing costs [123]. This continuous monitoring enables instantaneous quality tracking and ensures high large‐area uniformity of the scaled‐up AEMWE electrodes.

FIGURE 9.

Industrial scale‐up manufacturing. (a) Schematic of the process for R2R direct catalyst layer coating onto membrane. (b) Direct coated IrO₂ electrodes on Nafion 117 membrane. Reprinted with permission from Ref. [123]. Copyright 2020, Elsevier. (c) Schematic show of the R2R‐FJH strategy. (d) Various electrodes prepared by R2R‐FJH. (e) Various electrodes prepared by R2R FJH method. Optical photograph of the prepared meter‐scale MoNiFe‐LDH onto NF electrode. Reprinted with permission from Ref. [126]. Copyright 2025, American Chemical Society. (f) Schematic synthesis of metal@oxide heterostructured catalysts by the roll‐to‐roll method. (g) A digital photo of a large‐scale electrode (10 cm  ×  100 cm) manufactured. Reprinted with permission from Ref. [127]. Copyright 2025, Springer Nature.

4.2.2. Industrial Scalability and Manufacturing of Self‐Supported Catalysts

The transition of 3D self‐supported electrodes, such as nickel foam‐supported catalysts, from laboratory‐scale proof‐of‐concept to industrial‐scale deployment (>100 m² per MW stack) requires a paradigm shift in manufacturing methodologies. Traditional batch processes, such as static autoclave hydrothermal synthesis, are severely restricted by heat/mass transfer limitations and poor batch‐to‐batch consistency over large geometric areas. To overcome these barriers, R2R processing combined with non‐equilibrium thermal technologies, such as roll‐to‐roll flash Joule heating (R2R‐FJH), offers a promising route for continuous, high‐throughput manufacturing (Figure 9c) [124, 125]. By passing a liquid precursor‐impregnated nickel foam conveyor through localized electrical rollers, solvent evaporation and ultra‐fast catalyst nucleation/stabilization can be achieved within seconds. To validate the scalability of the R2R‐FJH strategy for industrial applications, meter‐scale electrodes (50 mm wide) decorated with diverse catalysts, including MoNiFe‐LDH and commercial benchmarks (IrO₂ and Pt/C), were successfully prepared (Figure 9d,e). The uniform deposition and desired morphologies achieved on these large‐area substrates highlight the capability of the R2R‐FJH process to transition from laboratory‐scale synthesis to continuous, high‐throughput manufacturing [126]. Shi et al. used roll‐to‐roll carbothermal shock (R2R‐CTS) strategy driven by induced Joule heating for the scalable fabrication of multielement heterostructured electrocatalysts (Figure 9f). By drawing precursor‐loaded carbon cloth through graphite rollers at a continuous speed of 7 m min⁻¹, large‐scale electrodes (10 × 100 cm²) were synthesized in one step with an exceptional productivity of 116.69 cm2 s⁻¹ (Figure 9g). This high‐throughput method ensures structural uniformity and consistent catalytic performance across large areas, demonstrating the viability of the R2R‐CTS process for industrial‐grade mass production [127]. Concurrently, large‐scale aqueous chemical/vapor‐phase corrosion and continuous electrodeposition lines present highly cost‐effective alternatives for scaling up transition‐metal (oxy) hydroxides to meter‐scale dimensions [128]. By integrating these continuous manufacturing technologies, the industrial cost of green hydrogen production can be significantly driven down, paving the way for the widespread deployment of high‐current‐density electrolyzers.

4.3. Flow Field Optimization

The flow field functions as the “circulation system” of an electrolyzers, its design critically governs the efficiency of mass, charge, and heat transfer, ultimately determining both activity and durability of the electrolyzers. It serves not only as the channel for reactant delivery and product removal, but also the structural framework that dictates local distributions of temperature, pressure, gas‐liquid two‐phase flow, and current density within the electrolyzer [129]. A well‐designed flow field ensures that the reactant (water) is delivered uniformly and adequately to the catalyst surface, while simultaneously facilitating the efficient removal of gaseous products (such as O₂ and H₂) from the electrode interface. If the gaseous products are retained and accumulate as bubbles, they will cover the active sites, increasing mass transport resistance and overpotential. This leads to higher energy consumption and reduced overall efficiency. Moreover, localized hot spots may form under such conditions, accelerating the degradation of both the catalyst and the membrane.

Traditional flow field designs, such as parallel and serpentine configurations (Figure 10a–c), each present distinct advantages and limitations. The parallel flow field offers a low‐pressure drop, but is prone to non‐uniform flow distribution, leading to localized “short‐circuit” effects. In contrast, the serpentine flow field enhances forced convection and facilitates gas removal, yet it imposes a relatively high‐pressure drop, thereby increasing parasitic pump power consumption [130]. In recent years, advances in manufacturing technologies and multiphysics simulations have spurred the development of numerous innovative flow field designs. Shen et al. proposed a hybrid flow field called “Parpentine” (Figure 10d). Guided by computational fluid dynamics (CFD) simulations, this design achieves an optimized balance among electrolyte distribution, bubble removal rate, and system pressure drop. It significantly mitigates the high‐pressure drop inherent to serpentine flow fields while alleviating the bubble blockage commonly observed in parallel designs. Experimental validation in an AEM electrolyzer demonstrated a hydrogen production efficiency increase of up to 34.8% [131]. In parallel, extensive research has focused on biomimetic flow fields, which draw inspiration from transport structures optimized through millions of years of biological evolution (such as leaf venation, pulmonary bronchi, and capillary networks). These designs offer a new paradigm for enhancing electrolyzer performance by achieving an optimal trade‐off between mass transfer and energy consumption. Fractal flow fields, for instance, employ hierarchically branched channels to distribute reactants uniformly across the electrode surface with minimal pressure drop, effectively eliminating stagnant zones. Rao et al. applied Murray law to strategically design the dimensions of multi‐level branching channels. By incorporating blocking structures into the primary flow channels, they successfully enhanced reactant distribution uniformity, improved water drainage capacity, and reduced system pressure drop, thereby boosting mass transport efficiency and overall output performance [132]. Huang et al. introduced obstacles of various geometric shapes (such as circular, diamond, and triangular) into the leaf‐vein‐inspired main flow channel (Figure 10e). These obstacles promote forced diversion, enhancing electrolyte penetration into branching channels. Among the geometries tested, circular obstacles proved most effective due to their streamlined profile, which minimizes flow dead zones and achieves the most uniform distribution of active species and the highest system efficiency [133]. Inspired by the branching architecture of the human superior mesenteric artery, Lei et al. designed a biomimetic flow field incorporating refined, densely distributed channels (Figure 10f). This configuration significantly improves reactant uniformity while maintaining an extremely low system pressure drop, and also offers superior thermal management and reduced parasitic power consumption [134]. Li et al. proposed a fish‐scale biomimetic flow field, replicating the arcuate structure and staggered arrangement of fish scales to enhance flow characteristics (Figure 10g). Compared to conventional parallel and pin‐type flow fields, this design reduces flow resistance, promotes uniform oxygen distribution, and improves the liquid water management, resulting in simultaneous gains in power density and effective mass transfer coefficient [135]. Drawing inspiration from the undulating fin structure of cuttlefish, Liu et al. introduced “exchange channels” between adjacent wavy flow channels (Figure 10h). This innovation addresses the high pressure drop and uneven reactant distribution typical of wavy flow fields, significantly improving gas distribution uniformity and water management while reducing parasitic power, thereby substantially increasing net power density [136]. Wang et al. took inspiration from aircraft wing profiles, constructing cross‐flow channels using strategically arranged airfoil‐shaped pins. This design markedly reduces the flow resistance and pressure drop while enhancing gas transport to the diffusion layer, synergistically improving net power density [137]. Sun et al. developed a composite bionic flow field based on leaf‐vein architecture (Figure 10i). First, mimicking the pinnate venation of plant leaves, they constructed a flow field with a central inlet and symmetrically distributed branch channels extending to the four corners. This configuration shortens the reactant transport paths, significantly reducing flow resistance and parasitic power. Subsequently, biomimetic streamlined blocks inspired by water droplets and bird shapes were introduced into the optimally branched flow field, transforming the mass transport mechanism from diffusion‐dominated to convection‐dominated, thereby enhancing the oxygen transport and distribution uniformity. Ultimately, this composite flow field achieved an 8.475% increase in maximum power density compared to conventional serpentine designs, while avoiding eddy current formation and excessive parasitic power, and improving long‐term operational stability [138]. To provide a clearer quantitative comparison of these representative flow field strategies, Table 3 summarizes their main design principles, performance improvements, and relevance to AEMWE scale‐up.

FIGURE 10.

Traditional flow fields: (a) Parallel flow channels, (b) stepped flow channels, and (c) serpentine flow channels. Reprinted with permission from Ref. [130]. Copyright 2023, Multidisciplinary Digital Publishing Institute. (d) Improvements to traditional flow fields. Reprinted with permission from Ref. [131]. Copyright 2025, Elsevier. Bionic flow fields: (e) leaf‐vein‐bionic main flow channel. Reprinted with permission from Ref. [133]. Copyright 2024, Elsevier. (f) Bionic flow field by mimicking the branching structure of the superior mesenteric artery in the human body. Reprinted with permission from Ref. [134]. Copyright 2023, Elsevier. (g) Fish scale biomimetic flow field. Reprinted with permission from Ref. [135]. Copyright 2025, Elsevier. (h) Wavy structure of the cuttlefish fin. Reprinted with permission from Ref. [136]. Copyright 2024, Elsevier. (i) Composite flow field of leaf vein flow field + bionic streamlined blocks in the shape of water droplets and birds. Reprinted with permission from Ref. [138]. Copyright 2021, Elsevier.

TABLE 3.

Quantitative performance improvements achieved by representative advanced flow‐field designs.

Flow‐field designs	Design objectives	Key quantitative improvement	Relevance to AEMWE
Parpentine	Balance flow distribution, bubble removal, and pressure drop	H2 production efficiency +12.4%∼34.8% [131]	Directly applicable
Leaf‐vein flow field	Improve flow uniformity	Power density +5.9% [133]	Transferable principle
Fish‐scale bionic	Improve distribution and reduce pressure loss	Power density +9.3%; Pressure drop −5.5% [135]	Transferable principle
Sinusoidal exchange‐channel	Improve reactant distribution	Net power density +10.3% [136]	Transferable principle
Airfoil cross‐flow	Enhance convection with low resistance	Net power density +10.7% [137]	Transferable principle
Composite leaf–vein bionic flow field	Enhance mass transfer	Power density +8.5% [138]	Transferable principle

4.4. Advanced Control Strategies

Advanced control strategies are pivotal for bridging the gap between lab‐scale performance and industrial application, particularly when the electrolyzer is integrated with intermittent renewable energy sources such as solar and wind power [139, 140]. Such strategies enable real time regulation of operating parameters, mitigation of dynamic disturbances, and maximization of system efficiency while prolonging component operational lifespan [141]. The core control objectives include maintaining uniform reactant distribution, stabilizing cell voltage and current density, suppressing gas crossover, and enabling rapid response to load fluctuations, all of which are schematically represented in the multi‐layered control logic framework depicted in Figure 11.

FIGURE 11.

(a) Coordinated control of contact resistance and flow rate feedback. Reprinted with permission from Ref. [92]. Copyright 2023, Elsevier. Reprinted with permission from Ref. [93]. Copyright 2025, American Chemical Society. Reprinted with permission from Ref. [144]. Copyright 2023, Elsevier. (b) Thermal management prediction model of multi‐physical field coupling. Reprinted with permission from Ref. [196]. Copyright 2024, Multidisciplinary Digital Publishing Institute. (c) Adaptive adjustment combining power fluctuation and life data. Reprinted with permission from Ref. [148]. Copyright 2025, Elsevier. (d) Leakage detection and fault‐tolerant response based on residual analysis. Reprinted with permission from Ref. [142]. Copyright 2024, Elsevier.

Figure 11 presents a hierarchical control architecture that integrates sensory data acquisition, model‐based decision‐making, and actuator response across the electrolyzer stack and associated balance‐of‐plant. This architecture functions as the central intelligence guiding the mechanical system, ensuring consistent operation within safe and optimal performance windows. At the foundation level, a distributed sensor network continuously monitors key parameters: local current density and temperature distributions across the MEA; electrolyte flow rate and concentration at both anode and cathode inlets/outlets; clamping pressure distribution across the stack (using pressure‐sensitive films or piezoresistive sensors); and gas purity (H₂/O₂) along with crossover rates [142]. These real time measurements are transmitted to a central control unit, where a hybrid model integrating empirical correlations with physics‐based simulations predicts system states and optimizes control actions. The execution layer then implements adjustments through actuators, including variable‐speed pumps for electrolyte flow regulation, proportional‐integral‐derivative (PID) controllers for stack temperature management, and torque‐adjustable clamping mechanisms for dynamic pressure control [143].

4.4.1. Multi‐Variable Coordinated Control for Robust Operation

A critical challenge in electrolyzer operation lies in the strong coupling among various operating parameters. Advanced control strategies address this complexity through multi‐variable coordinated control, which enables simultaneous optimization of multiple parameters. For instance, based on real time pressure distribution data, the system dynamically adjusts bolt torque to maintain a uniform clamping pressure within the optimal range of 1.8–2.2 MPa. This capability is fundamental to robust mechanical design, as it minimizes contact resistance (reducing ohmic losses by 15%–20%) and prevents over‐compression of PTLs (avoiding porosity reduction below 40%) [92, 93, 144]. Water flow rate regulation exerts a dual effect on stack performance, serving as both thermal management and reactant supply. At high current densities (>0.5 A cm⁻²), a moderate reduction in water flow rate promotes self‐heating of the stack, leading to a maximum cell voltage reduction of 710 mV at 1.5 A cm⁻². Conversely, under wind‐loaded conditions with an inlet water temperature of 70°C, elevating the water flow rate from 60 to 120 mL min⁻¹ directly enhances the electrochemical performance by 1.35% and significantly improves voltage uniformity [145]. Figure 11a highlights this integrated feedback loop, in which sensory data on contact resistance and electrolyte velocity are used to synchronously compute optimal torque and pump speed. These results demonstrate that coordinated pressure‐flow regulation can significantly improve both operational stability and durability under industrially relevant conditions.

4.4.2. Thermal Management Through Multi‐Physics Modeling

Effective thermal management, guided by multi‐physics modeling, is indispensable for preserving stack integrity and ensuring long‐term performance [74]. The hybrid control architecture illustrated in Figure 11b incorporates multi‐physics simulations that couple electrochemical reactions, fluid dynamics, and heat transfer. These models enable real time prediction of temperature distributions within the MEA and throughout the stack. Coupled with feedback‐control algorithms, they allow dynamic regulation of coolant flow rates and operating currents, thereby suppressing localized hotspots that accelerate membrane degradation, catalyst deactivation, and mechanical stress accumulation. Through the integration of online temperature monitoring and predictive modeling, near‐isothermal operating conditions can be maintained, thereby mitigating thermal gradients that would otherwise promote uneven degradation across the stack [96]. Recent studies have quantitatively demonstrated the effectiveness of predictive thermal‐management strategies in improving thermal stability and operational robustness under dynamic operating conditions. For example, Keller et al. developed an adaptive model‐based feedforward temperature‐control strategy for a 100 kW PEM electrolyzer operating under highly dynamic photovoltaic and wind‐power inputs. Compared with conventional PID control, the proposed strategy reduced stack‐temperature deviations from over ±10°C to within 2°C and maintained temperature oscillations around the setpoint within ±1°C, significantly improving thermal stability under transient operating conditions [146]. Although demonstrated in PEM electrolysis, the underlying control philosophy is directly transferable to AEMWE systems, where thermal non‐uniformity similarly contributes to accelerated membrane degradation, catalyst instability, and stack‐performance heterogeneity. Beyond PEM systems, similar benefits have also been observed in alkaline electrolysis. Model‐predictive thermal‐control strategies developed for alkaline electrolysis systems have demonstrated the ability to effectively suppress temperature overshoot under fluctuating operating conditions. Experimental studies showed that model predictive control eliminated observable temperature overshoot and enabled operation at higher temperature setpoints, resulting in approximately 1% efficiency improvement compared with conventional control approaches [147]. Collectively, these studies demonstrate that predictive thermal management can simultaneously enhance operational stability, suppress thermally induced degradation, and improve system efficiency, highlighting its growing importance for the scale‐up of industrial AEMWE systems.

4.4.3. Adaptive and Intelligent Control for Dynamic Operation

The inherent intermittency of renewable energy sources necessitates adaptive and intelligent control strategies that extend beyond conventional static setpoint regulation. As illustrated in Figure 11c, to address fluctuating photovoltaic power, a power‐adaptive control strategy is proposed for multi‐stack PEM electrolyzers, integrating Dung Beetle Optimization for offline allocation with real time fuzzy PID control for online branch regulation. This intelligent control framework dynamically balances system efficiency and hydrogen production, effectively mitigating adverse low‐power and full‐load operations to enhance hydrogen yield and extend equipment lifespan under highly dynamic operating conditions [148]. Material‐specific control requirements further highlight the importance of coupling operational management with component durability. Specifically, PTFE gaskets require slower torque adjustments (0.3 N m min⁻¹) to prevent stress spikes, whereas silicone gaskets tolerate faster adjustments (0.5 N m min⁻¹) but necessitate more frequent pressure monitoring. This intelligent, material‐aware control strategy reduced gasket degradation by 28% over 1000 h of operation [92]. Beyond component‐specific protection strategies, the increasing penetration of renewable energy further necessitates system‐level predictive and adaptive control frameworks capable of coordinating stack operation under highly dynamic conditions. Advanced optimization strategies have demonstrated measurable benefits in this regard. For example, Huang et al. proposed an economic model predictive control (EMPC) framework for MW‐scale alkaline electrolyzers integrated into multi‐energy systems. Compared with rule‐based control and simplified economic‐dispatch strategies, the proposed EMPC reduced operating costs by 59% and 38%, respectively, while simultaneously accounting for hydrogen, heat, and electricity dynamics [149]. These results demonstrate that predictive control can simultaneously improve operational stability, mitigate degradation, and enhance system‐level economic performance, highlighting its growing importance for renewable‐energy‐driven AEMWE systems. Artificial intelligence (AI) is increasingly being explored as a promising tool for further enhancing adaptive and predictive control strategies. For instance, based the long short‐term memory (LSTM) neural network model predicts polymer electrolyte membrane (PEM) electrolyzer hydrogen temperature using cooling water temperature, achieving a strong correlation coefficient of 0.99 and a low root‐mean‐square error (RMSE) of 0.1351. This provides highly accurate and reliable predictive data to support predictive maintenance, sensor fault tolerance, and safety warnings during dynamic system operations [150]. Furthermore, AI‐assisted approaches may leverage long‐term degradation data to estimate component remaining useful life and support predictive‐maintenance scheduling, thereby reducing unplanned downtime and improving operational reliability. Collectively, these developments illustrate the evolution of control strategies from passive regulation toward predictive, durability‐oriented, and increasingly data‐driven management, providing an important pathway for maintaining stable operation, mitigating degradation, and improving operational resilience during the scale‐up of industrial AEMWE systems.

4.4.4. Fault‐Tolerant Control and System Diagnostics

System‐level faults present significant risks to both the operational stability and safety of AEM electrolyzers. The fault diagnosis sub‐system depicted in Figure 11d employs residual analysis to detect anomalies such as membrane pinholes (indicated by increased H₂ crossover and ionic resistance) or gasket leakage, which manifests as uneven pressure distribution across the stack [142, 144]. Beyond fault mitigation, advanced diagnostic and supervisory‐control frameworks are increasingly recognized as enabling technologies for predictive maintenance. By combining online monitoring, fault diagnosis, and degradation forecasting, these approaches provide opportunities to reduce unexpected shutdowns, improve stack availability, and support long‐term operational reliability under industrial operating conditions. To meet the demands of predictive maintenance for electrolyzers under fluctuating power sources, Shen et al. employed interleaved voltage detection to eliminate sensor interference and utilized SSA‐VMD (Variable mode decomposition) to mitigate cell inconsistency. This approach amplified the diagnostic features of water shortage and short‐circuit faults by 13.04 and 4.33 times, while advancing their detection times by 30 and 107 s, respectively. Combined with the crest factor and an improved correlation coefficient method, the proposed method achieved precise localization of short‐circuit, water shortage, and low clamping pressure faults, significantly enhancing the long‐term operational reliability of the electrolyzer stack [151]. Although large‐scale implementation in AEMWE systems remains limited, similar strategies have already demonstrated substantial operational and economic benefits in related electrolysis platforms. Collectively, these advances highlight the value of deeply integrated system‐level control strategies, demonstrating that rapid fault diagnosis, predictive maintenance, and corrective control can effectively minimize performance losses, reduce unplanned downtime, and enhance the operational resilience of industrial AEMWE systems.

Advanced multivariable control strategies further endow the system with the capability to adapt to dynamic operating conditions, suppress degradation mechanisms, and enhance operational resilience. As summarized in Table 4, representative intelligent‐control strategies have demonstrated measurable benefits, including improved thermal stability, reduced voltage fluctuations, lower operating costs, enhanced fault‐diagnosis capability, and extended component lifetime. These quantitative improvements highlight the critical role of predictive, adaptive, and fault‐tolerant control in enabling reliable large‐scale electrolyzer operation.

TABLE 4.

Quantitative performance improvements enabled by representative intelligent‐control strategies.

Strategy	Control objectives	Quantitative improvement	Refs.
Adaptive water‐flow regulation	Coupled thermal management and stack‐uniformity control	Cell voltage ↓710 mV at 1.5 A cm−2; stack performance ↑1.35%; voltage uniformity improved	[145]
Adaptive feedforward temperature control	Thermal stability under fluctuating renewable‐power input	Temperature deviation reduced from >±10°C to <2°C; temperature oscillation maintained within ±1°C	[146]
Economic model predictive control (EMPC)	Multi‐energy system optimization and operating‐cost reduction	Operating cost ↓59% vs. rule‐based control; ↓38% vs. economic‐dispatch strategy	[149]
Fault‐tolerant diagnosis and corrective control	Fault diagnosis and precise localization	Water shortage and short‐circuit faults improve 13.04 and 4.33 times	[151]

In summary, the multi‐scale performance optimization strategies delineated in this chapter: ranging from nanostructured electrode engineering to intelligent control of macroscopic systems, constitute a tightly integrated, closed‐loop co‐design framework. Innovative membrane‐electrode architectures establish stable, efficient reaction interfaces. Biomimetic and structured flow field designs ensure uniform gas‐liquid mass transport and effective thermal management at the system level. These three dimensions do not advance in isolation, rather they are unified through a shared understanding of failure mechanisms, enabling targeted integration and iterative feedback optimization across materials, devices, and systems. This holistic systems‐engineering approach, integrating materials design, structure innovation, and intelligent control, provides a clear and technical viable pathway toward industrial‐grade AEM electrolyzers capable of achieving high current density, long lifespan, and robust performance.

5. In Situ Monitoring

Promoting the advancement of in situ characterization techniques toward cross‐scale and multi‐parameter integration has emerged as a critical scientific approach to overcoming industrial bottlenecks, which aid in elucidating the relationships between the structure, reactivity, and stability of AEMWE systems [152]. Traditional characterization methods capture only static snapshots of catalysts pre‐and post‐reaction, making it challenging to understand dynamic failure. Thus, in situ characterization techniques are essential for real time observation of the evolving electronic structure, local coordination, and morphology of active sites during reactions. This requires advanced methods: in situ SEM [153, 154, 155, 156, 157, 158], TEM [159, 160, 161, 162], AFM [163, 164, 165], and XCT [166, 167, 168] track changes in catalyst surface structure, particle size, and pore structure; in situ XRD [169, 170, 171, 172], XPS [173, 174, 175], FT‐IR [176, 177, 178, 179], and XAS (including XANES and EXAFS) [180, 181, 182], precisely analyze the valence state, coordination environment, and adsorption behavior of key intermediates; in situ DEMS [183, 184, 185] monitors real time gas or liquid products (Figure 12).

FIGURE 12.

Schematic diagram of various in situ characterization techniques for observing and studying the real time dynamics of catalysts. in situ SEM. Reprinted with permission from Ref. [153]. Copyright 2013, Royal Society of Chemistry. TEM. Reprinted with permission from Ref. [159]. Copyright 2024, Springer Nature. AFM. Reprinted with permission from Ref. [162]. Copyright 2024, American Chemical Society. XCT. Reprinted with permission from Ref. [164]. Copyright 2026, Elsevier, in situ XRD. Reprinted with permission from Ref. [167]. Copyright 2020, Multidisciplinary Digital Publishing Institute. XPS. Reprinted with permission from Ref. [172]. Copyright 2024, John Wiley and Sons. FT‐IR. Reprinted with permission from Ref. [174]. Copyright 2021, Springer. XAS. Reprinted with permission from Ref. [178]. Copyright 2026, John Wiley and Sons. in situ Raman. Reprinted with permission from Ref. [181]. Copyright 2024, American Chemical Society.

From the perspective of catalyst morphology evolution, Tong et al. directly observed the reversible encapsulation and de‐encapsulation processes of Pt nanoparticles by the TiO₂ support under redox conditions using in situ TEM, which confirmed the dynamic response mechanism of classical and non—classical metal‐support interactions [186]. Ma et al. integrated electrochemical impedance spectroscopy (EIS) into environmental transmission electron microscopy and conducted in situ tests on the battery under H₂/H₂O atmosphere and high temperature, verifying that reliable electrolyte ion transport and electrode reaction kinetics data can be obtained even in nanoscale devices. This coupling nanoscale EIS with atomic‐scale structural tracking is highly relevant to AEMWEs, where catalysts (e.g., Ni‐based oxyhydroxides) undergo intense electrochemical reconstruction and phase transitions under alkaline OER/HER conditions. Applying this in situ impedance‐microscopy platform to AEMWEs would allow researchers to directly correlate the dynamic migration of active metals and support interactions with the real time evolution of interfacial charge transfer resistance (Rct). By employing in situ TEM, they revealed the dynamic evolution of catalysts under reaction atmospheres at the atomic scale, such as single‐atom migration, alloy element segregation, and metal‐support interactions, providing direct evidence for understanding structural evolution [187]. Akbashev et al. focused on the electrode/electrolyte interface using in situ atomic force microscopy. By quantifying the correlation between surface corrosion kinetics and potential, they elucidated the decoupling mechanism of perovskite A‐site leaching and lattice collapse and determined the degradation pathway of the highly active electrocatalyst SrIrO₃ during the OER [188]. X‐ray microscopy imaging elevates the perspective to the level of real devices, monitoring the thinning of the AEM membrane electrode and catalyst shedding during operation in a 3D and non‐destructive manner, effectively decoupling chemical degradation and radiation damage [65]. The cross‐scale correlation from atomic migration, surface dissolution to macroscopic failure together constitutes the core closed‐loop for decoupling the degradation mechanism of complex electrochemical systems.

From the perspective of catalyst crystal and electronic structures, Linke et al. used in situ XRD to track the irreversible collapse of the long‐range ordered structure of Ni‐MOF‐74 during the OER, confirming that its crystalline framework gradually disintegrated and transformed into amorphous species during the cyclic voltammetry process [189]. Xu et al. used in situ XAS to reveal the degradation mechanism of RuO₂ in real electrolysis devices, finding that the significant decrease in the bridging oxygen coordination number was directly related to the reduction of the Ru valence state, and confirming the structural collapse path triggered by lattice oxygen deficiency [190]. Jiang et al. captured the adaptive distortion of PtCo bimetallic sites under working conditions through operando XAS, revealing the evolution of the coordination environment from Pt₁Co₅ to Pt₁Co₆ and its regulatory effect on the interfacial water structure [191]. Li et al. found in the operando XAS study of AEM fuel cells that Mn₃O₄ exhibited completely different valence states and coordination states under in situ half‐cell and device operating conditions. In the latter, the oxidation state of Mn increased to above 3+ and the octahedral sites were dominant, thus explaining the difference between RDE test and single‐cell performance [192]. Jaehyun Kim et al. used in situ XAS to demonstrate that subsurface W single atoms, acting as electronic modulators rather than direct active sites, trigger synergistic electron redistribution at neighboring Ni‐O‐Fe edge sites, thereby lowering the reaction barrier and promoting transition to the active γ‐phase [193]. These studies together construct a complete cognitive chain from crystal framework collapse to local electronic state reconstruction, providing direct atomic‐scale evidence for precise stabilization strategies (such as Ir doping to anchor bridging oxygen and pre‐catalyst activation path selection). Based on clarifying the structural evolution of the catalysts, in situ FTIR/Raman spectroscopy further identified reaction intermediates and revealed the dynamic changes in the catalytic reaction pathways. Ji et al. directly captured the *O‐O* bridging intermediate using in situ infrared spectroscopy, confirming that Mn doping induced the transition of the reaction pathway from the adsorbate evolution mechanism to the oxide pathway mechanism [194]. Na et al. monitored the accumulation of *OH intermediates at a lower potential via infrared spectroscopy, verifying that FeOOH modification accelerated the reconstruction of Ni active sites and the affinity for hydroxyl groups [195]. Li et al. identified the *O₂ ⁻ species through in situ Raman, directly confirming the activation of the LOM [196]. Maxwell et al. detected carboxylic acids and aromatic functional groups using this technique, revealing the chain scission and dissolution of ionomers under oxidative conditions [197].

Together, these studies reveal how catalyst structures dynamically evolve under operating conditions, spanning crystal framework collapse, electronic‐state reconstruction, and reaction‐intermediate formation. Such insights provide a fundamental basis for understanding catalyst degradation pathways and developing targeted stabilization strategies. However, catalyst degradation represents only one aspect of performance decay in practical AEMWE systems. As electrolyzers scale from materials to electrodes, MEA, and full devices, degradation increasingly arises from the complex coupling of interfacial transport, component interactions, and system‐level operating conditions. Therefore, advancing operando diagnostics beyond active materials toward electrode, MEA, and device‐level characterization has become essential for establishing multiscale degradation mechanisms and guiding the rational scale‐up of AEMWE technologies.

To address these challenges, a new generation of operando diagnostic tools has been developed for electrode, MEA, and device‐level characterization. Reference‐electrode‐integrated electrolyzers enable the independent monitoring of anode and cathode potentials during operation, allowing quantitative decoupling of voltage losses associated with catalyst layers, membranes, and transport processes (Figure 13a) [198]. By integrating a reference electrode into a zero‐gap AEMWE, the individual overpotential contributions from the anode, cathode, and ohmic resistance can be resolved under realistic operating conditions, providing a direct diagnostic platform for identifying performance‐limiting components. Similarly, internal voltage‐sensing strategies based on sense wires and membrane‐coupled probes have been developed to track local potential distributions and resistance evolution in real time, enabling direct observation of catalyst‐layer utilization, interfacial degradation, porous transport layer resistance, and local voltage‐loss distributions during operation (Figure 13b) [199, 200]. More recently, component‐resolved diagnostic frameworks combining EIS with distribution‐of‐relaxation‐times analysis have enabled the deconvolution of electrode‐specific degradation pathways in operating AEMWE systems, providing quantitative insights into charge‐transfer, ion‐transport, and mass‐transport losses. Furthermore, recent work has successfully extended this diagnostic to stack‐level analysis. Sampathkumar et al. applied DRT to a 1 kW, 5‐cell non‐PGM AEMWE stack, successfully deconvolving gas/water diffusion and HER/OER charge‐transfer losses. This demonstrates the viability of DRT in evaluating cell‐to‐cell uniformity and mapping internal loss mechanisms in large‐scale electrolyzers. (Figure 13c) [201, 202]. At a larger scale, in situ health‐monitoring methodologies have further been extended to electrolyzer stacks, allowing the diagnosis of performance inconsistency, hydrogen crossover, catalyst utilization, and membrane degradation among individual cells during long‐term operation (Figure 13d) [203]. These methodologies establish a critical link between material degradation mechanisms and device‐level performance evolution.

FIGURE 13.

(a) Integrated reference electrode. Reprinted with permission from Ref. [198]. Copyright 2026, Elsevier. (b) Integrated sense wire in MEA. Reprinted with permission from Ref. [198]. Copyright 2021, Elsevier. (c) Multi‐scale diagnostics: cell (GEIS + DRT) and stack evaluation (DRT). Reprinted with permission from Ref. [201]. Copyright 2026, American Chemical Society. Reprinted with permission from Ref. [202]. Copyright 2025, American Chemical Society. (d) Local ionic concentration monitoring. Reprinted with permission from Ref. [203]. Copyright 2024, American Chemical Society. (e) Operando water and gas management. Reprinted with permission from Ref. [206]. Copyright 2024, Royal Society of Chemistry.

Complementary to electrochemical diagnostics, operando imaging techniques provide unique opportunities to visualize mass transport and water‐management behavior within functioning electrochemical devices. Neutron imaging, x‐ray radiography, magnetic resonance imaging (MRI), and fluorescence‐based microscopy have been widely employed in PEM fuel cells and PEM electrolyzers to directly monitor water generation, distribution, flooding, and removal processes within catalyst layers, porous transport layers, and flow channels [170, 171, 172, 173, 190, 191, 192, 193]. These techniques reveal the spatial heterogeneity of reactant transport and local operating environments that are often inaccessible by conventional electrochemical measurements. In particular, neutron imaging enables quantitative visualization of water accumulation and transport inside operating electrochemical devices, whereas x‐ray imaging and MRI provide complementary information regarding liquid‐water distribution and membrane hydration states (Figure 13e) [204, 205, 206]. Fluorescence‐based microscopy further enables high‐spatial‐resolution observation of liquid‐water transport pathways within porous electrodes [207]. This imaging‐based diagnostic is highly transferable to AEMWE systems to address their unique transport bottlenecks, such as anode dehydration and gas‐trapping in porous transport layers. Applying these complementary imaging modalities allows researchers to quantitatively track the concentration gradients of the alkaline working fluid and the dynamic distribution of generated gas, offering crucial insights for optimizing high‐performance AEM electrolyzer architectures. In addition, operando local ion‐concentration mapping techniques have recently emerged as powerful tools for monitoring proton or hydroxide transport efficiency at the membrane‐electrode interface, thereby directly linking local reaction environments to catalyst stability and electrolyzer performance [208]. Although most of these advanced diagnostic methodologies have thus far been demonstrated in PEM‐based systems, they provide highly valuable guidance for next‐generation AEMWE research. Their implementation in AEMWE is expected to bridge the knowledge gap between material degradation and device failure, establish equivalent degradation mechanisms across membranes, catalyst layers, electrodes, single cells, and stacks, and ultimately facilitate the development of cross‐scale optimization strategies for industrially scalable AEMWE systems.

Despite remarkable advances in operando and in situ characterization, their deployment in industrial AEMWE stacks remains challenging because many techniques require specialized cell architectures, optical access, or large‐scale facilities that are difficult to integrate into high‐pressure, large‐area electrolyzer systems. Furthermore, embedded sensors may perturb local transport and electrochemical processes, while the harsh operating environment‐including high current densities, concentrated alkaline electrolytes, temperature gradients, and pressurized H₂/O₂ places stringent demands on sensor durability and signal reliability. Nevertheless, these techniques have substantially expanded diagnostic capabilities from catalyst active sites to electrodes, membrane‐electrode assemblies, single cells, and even stack‐level systems. By correlating atomic‐scale structural evolution with interfacial transport, component degradation, and device‐level performance losses, they provide an unprecedented opportunity to establish equivalent degradation pathways across multiple length scales and reveal the coupling and propagation of failure processes throughout the electrolyzer. Looking forward, the development of pressure‐compatible, minimally invasive, and distributed diagnostic platforms, combined with multiscale degradation analysis and predictive diagnostic tools, will be critical for bridging the gap between fundamental degradation mechanisms and practical scale‐up design, thereby accelerating the realization of durable and industrially scalable AEM water electrolyzers.

These techniques not only enable the elucidation of dynamic evolution mechanisms of catalytic active centers at the atomic scale, but also facilitate the construction of comprehensive real time correlation maps spanning from microscopic phenomena (e.g., ion transport and gas evolution at the three‐phase interface), mesoscopic (e.g., changes in electrode pore structure and membrane‐electrode interface stability) and macroscopic (e.g., internal temperature, pressure, and flow field distribution within the electrolyzer). This multiscale diagnostic capability allows us to transcend the spatiotemporal limitations of ex situ characterization, enabling direct observation of the interactive and cascading degradation processes among components under operating conditions. Ultimately, it places material design, electrode engineering, and system optimization within a unified dynamic feedback framework.

6. Conclusions and Perspectives

6.1. Synergistic Effects of Multi‐Scale Design Strategies

This review systematically examines the multi‐scale challenges and optimization strategies confronting industrial‐grade AEM electrolyzers as they transition from laboratory research to large‐scale manufacturing. The analysis reveals that the performance, efficiency, and durability of AEM electrolyzers arise from complex interactions and synergistic effects across multiple hierarchical levels: (1) at atomic and nano‐scale, encompassing catalyst activity, dissolution behavior, and intrinsic membrane stability; (2) at micro and millimeter‐scale, involving MEA structure and three‐phase boundary transport; (3) at macroscopic system‐scale, comprising stack assembly uniformity, flow field design, thermal management, and intelligent control.

The successful commercialization of AEM system necessitates moving beyond traditional, compartmentalized research and development (R&D) models. Therefore, an integrated, multi‐scale design philosophy must be embedded throughout the entire R&D process. This approach demands close collaboration among materials scientists, electrochemists, and engineering experts, enabling the seamless integration of rational design of nanocatalysts, precise control of meso‐scale electrode structures, and robust system‐level implementation with adaptive control strategies. Such cross‐scale collaborative innovation and optimization are crucial to overcoming core bottlenecks related to the lifespan, efficiency, and cost of AEM electrolyzers. By addressing these interconnected challenges, this promising laboratory technology can be transformed into a reliable and economically competitive industrial‐grade solution for green hydrogen production, thereby supporting the vision of a global hydrogen economy.

6.2. Future Directions

Figure 14a shows the projected trajectory (2025–2040) of the levelized cost of hydrogen (LCOH) for hydrogen production by AEMWE, indicating substantial cost‐reduction potential and a clear path to commercialization. To become market‐competitive, the short‐term technological target is too lower LCOH from $5/kg H₂ (in 2025) to $2–4/kg H₂ (in 2030). Over the long term (by 2040), LCOH in AEMWE must reach the U.S. Department of Energy (DOE) ultimate target of $1/kg H₂ in order to achieve cost parity with conventional grey hydrogen (approximately $1–1.5/kg H₂) [77, 209, 210, 211, 212]. To transition AEMWE from laboratory‐scale prototypes (“Lab”) to gigawatt‐scale fabrication facilities (“Fab”), a clear understanding of the gap between the state‐of‐the‐art and the quantitative targets required for commercialization is essential. Table 5 provides a comprehensive technology roadmap, contrasting the state‐of‐the‐art achievements in both academic research and industrial original equipment manufacturers (OEMs) against the rigorous milestones established by the U.S. Department of Energy (DOE) and the International Renewable Energy Agency (IRENA). The LCOH cost breakdown reveals the main obstacles to future technological advances. As shown in Figure 14b, electricity costs dominate the total system cost (70%), while capital expenditure and operating expenditure (CAPEX/OPEX) comprise the remaining 30%. Within system CAPEX, the stack is the largest contributor (45%), followed by the balance of plant (BOP, 30%) and civil construction, installation, and engineering–procurement–construction EPC (25%). A closer examination at the stack internal cost structure shows the MEA represents 40% of stack cost, the BPP contributes 30%, and the PTL accounts for 12.5% [40, 213]. Therefore, from a technological perspective, future research must pursue a two‐pronged strategy: on one hand, reduce the up‐to‐70% electricity shares by improving system energy conversion efficiency; on the other hand, substantially lower stack CAPEX by developing low‐cost core materials, especially MEA and BPP.

FIGURE 14.

(a) LCOH reduction trajectory and stack component cost breakdown for AEMWE (2025–2040). (b) Overall LCOH composition and CAPEX decomposition of the AEMWE system. (c) Key cost‐reduction drivers and technological milestones for AEMWE commercialization. (Through AI modified: the data sourced from the U.S. Department of Energy [209].).

TABLE 5.

Technology roadmap and quantitative commercialization targets for AEMWE.

Key performance indicators (KPIs)	State‐of‐the‐art in academia	State‐of‐the‐art in industry OEMs	Commercialization targets (U.S. DOE/IRENA)	Key R&D directions
Current density and cell voltage	1.0–3.0 A cm−2 @ 1.8 V (1 M KOH) [77]	0.5–1.0 A cm−2 @ 1.8–2.0 V [77]	> 2.0 A cm−2 @ 1.8 V (U.S. DOE Target) [209, 210, 211, 212]	R&D: Optimize mass transfer to achieve >2.0 A cm−2 @ 1.8 V using pure water or low concentration electrolyte [10].
Voltage degradation rate and lifetime	50–787 µV/h (>1000 h @ 60–80°C) [77]	< 10 µV/h (over 1000–10 000 h) [77])	< 4 µV/h [209, 210, 211, 212] (DOE target, >40 000 h lifetime); < 2 µV/h [10] (IRENA 2050 target, 100 000 h lifetime)	R&D: Develop highly stable AEMs and enhance catalyst‐ionomer interfacial robustness [10, 210].
Noble metal loading	Anode: non‐PGM (NiFe Base); cathode: PGM‐reliant (Pt/C) [77]	PGM‐free (anode and cathode)	PGM‐free or < 0.1 mg cm−2 total loading.	R&D: Develop highly active non‐PGM OER catalysts and transition from CCM to integrated self‐supported nanoarrays [10].
LCOH milestones	—	$4.0–$6.0/kg H2 (current commercial scale)	$2.0/kg H2 by 2026 (DOE) [209, 210, 211, 212]; $1.0/kg H2 by 2050 (IRENA) [10]	R&D: Scalability of large‐area CCS/CCM MEAs, transition from “Lab” to gigawatt‐scale (“Fab”) [211].

To facilitate the large‐scale commercialization of AEMWE, a synergistic approach across four strategic dimensions is imperative (Figure 14c): (1) Advanced materials, prioritizing high‐conductivity AEMs and PGM‐free catalysts to minimize MEA costs and eliminate reliance on scarce resources. (2) Manufacturing and Scaling, transitioning to roll‐to‐roll production and stack upscaling to capitalize on economies of scale. (3) System Integration, refining BOP architectures and thermal management while embedding AI‐driven control strategies that enable real time optimization of operating parameters and dynamic response to fluctuating renewable energy. (4) OPEX Optimization, targeting system efficiencies >75% (HHV) and operational lifespans >50 000 h augmented by predictive maintenance frameworks powered by machine learning (e.g., trained on membrane conductivity decay, gasket creep, and catalyst degradation data) to preempt component failure, reduce unplanned downtime, and lower the dominant electricity cost (≈70% of LCOH). Future efforts must integrate these pathways to transition from lab‐scale breakthroughs to industrial hydrogen production, ensuring economic competitiveness and long‐term durability through the synergy of material innovation, intelligent control, and prognostic health management.

Funding

The authors would like to thank the financial support from Natural Science Foundation of Zhejiang Province (ZCLZ24B0301), Guangxi Science and Technology Major Program (Guike JF2504850026), “Pioneer” and “Leading Goose” R&D Program of Zhejiang (2026C02A1076), Central Government Guided Local Science and Technology Development Fund Project (no. 2025ZY01113), Australian Research Council (DP220101290), National Natural Science Foundation of China (no. 22575217), and the Lhasa Central Government Guiding Local Science and Technology Development Funds (no. LSKJ202458) during the course of this work.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.