Abstract
Semiconducting devices based on hydrogel are increasingly popular in wearable and flexible bioelectronics because of their biocompatibility, mechanical conformability and ionic-electronic signal transduction properties. In material classes conductive polymer hydrogels, MXene and carbon-nanomaterial composites, liquid-metal hybrids, and adhesive fibrous interfaces practical applications as wearable sensors with continuous, long-duration functionality are limited by a variety of, mechanistically coupled degradation modes: environmental instability, electrode corrosion, signal interference, mechanical fatigue and cyclic swelling-shrinking, biofouling and microbial contamination, and polymer aging. Although materials-level interventions and, individually, machine-learning approaches to sensing functionality (pattern recognition, material property prediction) have been previously reviewed, no previous literature has mapped data-driven reliability and degradation-prognostication strategies based on battery health management, renewable energy control, wearable motion-artifact rejection, structural corrosion monitoring, and additive manufacturing quality control to the specific failure modes of hydrogel semiconductor devices. This Review seals that gap, critically comparing techniques on computational cost, data requirements, and cross-domain transfer assumptions; proposing a conceptual monitoring framework with a corresponding practical implementation pathway, edge-computing considerations, and uncertainty/explainability treatment; suggesting a standardized dataset structure; and reviewing regulatory and clinical-validation pathways. This Review seeks to offer a practically actionable roadmap to validated, self-monitoring hydrogel bioelectronic systems by connecting materials science, data-driven reliability engineering and biomedical device regulation.
1 Introduction
Hydrogel-based semiconducting materials have emerged as a leading platform for wearable and flexible bioelectronics, owing to their tissue-like softness, high water content, and ionic-electronic signal transduction capability, which minimize the mechanical and electrical mismatch between rigid electronic components and soft biological tissue (Zhu et al., 2025; Xue et al., 2026). Conductive hydrogels combining biopolymers, conductive polymers, carbon nanomaterials, MXenes, and liquid metals have been used to build stretchable strain sensors, electrophysiological electrodes, and skin-interfaced diagnostic patches, with recent designs achieving simultaneous self-healing, anti-drying, and adhesive functionality (Ji et al., 2025; Ren et al., 2023; Hajalilou, 2025; Li et al., 2025; Song et al., 2025; Wang, T. et al., 2025). Section 2 surveys this materials landscape in more detail, since the specific chemistry underlying a given hydrogel semiconductor device shapes both its sensing capability and its dominant degradation pathway.
This progress positions hydrogel semiconductors as strong candidates for continuous, long-duration physiological monitoring; recent system-level reviews of hydrogel-based bioelectronic interfaces likewise identify poor contact, signal interference, and low signal conversion efficiency as recurring, unresolved barriers to this transition (Jiao et al., 2025). Despite steady materials progress, the transition of hydrogel semiconductor devices from laboratory prototypes to reliable, long-duration wearable products remains constrained by a set of interlinked degradation mechanisms, detailed in Section 3: environmental instability, electrode corrosion and interfacial degradation, signal interference, mechanical fatigue from cyclic swelling and shrinking, biofouling and microbial contamination, and polymer aging. These mechanisms are not merely additive: several are mechanistically coupled, such that progress on one is frequently undermined by another.
These challenges are addressed today almost exclusively through materials-level interventions: anti-freezing or anti-drying additives, double-network or dual-crosslinked architectures for fatigue resistance, antifouling zwitterionic or antibacterial coatings, and optimized electrode-hydrogel interfacial chemistry (; ; ; Wei et al., 2025). A related “interface economy” framing has recently been proposed for battery electrode interfaces, explicitly weighing a given interfacial site’s reversible benefit against its irreversible cost rather than optimizing capacity alone (Wu et al., 2026); an analogous logic is adopted in Section 6.7 when positioning data-driven forecasting relative to materials-level fixes. Comparatively little attention has been paid to a complementary, data-driven layer: using forecasting and optimization algorithms to anticipate degradation before it manifests as device failure, and to computationally compensate for the interference that materials-level fixes cannot fully eliminate.
1.1 Novelty and distinction from prior reviews
First, materials-focused reviews of hydrogel and ionogel bioelectronics (Jiao et al., 2025; Xue et al., 2026; Zhu et al., 2025; Li et al., 2025) comprehensively catalog chemistries, fabrication routes, and device architectures, but do not address computational forecasting of degradation. Second, and most directly adjacent, a small number of recent reviews examine machine learning integrated with hydrogel or ionogel sensors (He et al., 2025; Mu et al., 2025) or wearable electrochemical biosensors generally (). These reviews focus on machine learning as a tool for improving sensing function itself gesture, handwriting, and motion recognition, materials property prediction, and biomarker classification rather than on forecasting the device’s own degradation over its service life. He et al. (2025), for example, explicitly note that ML-integrated soft sensing had not been systematically reviewed prior to their work, but their scope centers on pattern-recognition accuracy; degradation, corrosion, and reliability forecasting fall outside it. identify biofouling and signal drift as open challenges for wearable electrochemical biosensors but propose only materials-level countermeasures, not computational forecasting. Third, more comprehensive surveys of biodegradable and sustainable electronic substances () talk much of degradation, but as a planned final design approach to environmental sustainability, which is a materially different issue than the undesirable, in-service degradation that this Review covers. There is no review, to date, that systematically extrapolates data-driven reliability and degradation-modelling methods in similar engineering contexts to the in-service failure modes of hydrogel semiconductor devices the gap this review aims to address.
2 Material systems for hydrogel semiconductor devices
Hydrogel semiconductor devices are built from four broad material classes, each offering a distinct balance of conductivity, mechanical compliance, and self-repair capability, and each carrying a distinct reliability profile. Figure 1 summarizes these four classes, their shared signal-transduction basis, and the common reliability trade-off that motivates the remainder of this Review.
FIGURE 1
2.1 Conductive polymer hydrogels
Conductive polymer hydrogels (CPHs), combining biopolymer or synthetic hydrogel networks with intrinsically conducting polymers such as PEDOT:PSS or polypyrrole, synergize hydrogel-like tissue compliance and hydration with tunable mixed ionic-electronic conductivity (Xue et al., 2026). This mixed-conduction property is what allows CPHs to mediate directly between the ionic signaling of biological tissue and the electronic signaling of conventional circuitry, reducing the interfacial impedance mismatch that otherwise degrades signal fidelity at the tissue-device boundary. Recent work has focused on scalable fabrication of CPHs via 3D printing and photolithography, and on improving the mechanical and electrical harmony between the polymer and hydrogel phases (Wang, Y. et al., 2025; Ji et al., 2025). Despite this progress, CPH reviews consistently flag long-term stability and multimodal signal fidelity as unmet challenges, precisely the gap this Review addresses from a forecasting perspective.
2.2 MXene and carbon-nanomaterial hydrogels
Two-dimensional MXenes (most commonly Ti3C2Tx) and carbon nanomaterials such as graphene and carbon nanotubes (CNTs) are incorporated into hydrogel matrices to exploit their high intrinsic electrical conductivity and large surface area, which enhances ionic exchange at the electrode-hydrogel interface (Ren et al., 2023). MXene-hydrogel composites have demonstrated strong performance in motion-monitoring applications, with dry-resistant nanocomposite designs improving mechanical robustness under repeated deformation (Liu et al., 2023). A recurring materials-science challenge for this class is that MXene sheets are themselves prone to oxidative degradation under ambient humidity and temperature, compounding the hydrogel matrix’s own dehydration sensitivity a coupling directly amenable to the impedance-based forecasting approach. These materials are frequently combined with other classes discussed here (e.g., MXene-doped conductive polymers) to balance conductivity against the flexibility limitations that stiffer nanomaterial fillers can introduce (Zhu et al., 2025).
2.3 Liquid metal and hybrid composites
Eutectic gallium-indium (EGaIn) and other gallium alloys in liquid form can be a unique blend of extreme stretchability (sometimes 200 percent strain) with self-healing properties, as the liquid metal can reflow after mechanical damage instead of breaking permanently (Hajalilou, 2025). This renders liquid-metal hybrids especially appealing to devices under large, repeated deformation, but their combination with aqueous hydrogel phases presents its own stability challenges, which eco-friendly and green-chemistry formulations have already started to solve ().
2.4 Adhesive and fibrous interface hydrogels
A fourth, even more conspicuous, group is concerned with the quality of the electrode-tissue interface rather than with bulk conductivity. Adhesive hydrogel electrodes based on silk fibroin, gelatin, and chitosan have been designed to minimize baseline drift and achieve conformal, low-impedance contact with the skin (), and systematic benchmarking of crosslinker-free hydrogel electrodes has demonstrated that intrinsic low-pass filtering at the interface can effectively reduce motion artifact compared to traditional Ag/AgCl electrodes (). Such interface-oriented materials can be thought of as complementary to the above mentioned conductivity-oriented classes: a highly conductive hydrogel bulk will still need a stable, adhesive, low-drift interface to convert that conductivity into a long-term reliable signal.
2.5 Comparative reliability considerations across material classes
Table 1 gives a summary of the four material classes and their main reliability advantage, however, it is also important to take into account where the advantage of each of the classes does not apply. CPHs provide the best analogue of native ionic-electronic signaling and on dehydration they are the most vulnerable to conductivity loss. MXene and carbon-nanomaterial composites have high raw conductivity but add another, mostly unexplored oxidative degradation route, unique to the nanomaterial filler itself. Hybrids Liquid-metal hybrids are resistant to mechanical fatigue by self-healing reflow, but rely on the hydration status of the surrounding hydrogel to sustain electrical contact to the liquid-metal phase. Interfacial signal quality is explicitly targeted by adhesive and fibrous interface hydrogels, which are generally not optimised in bulk conductivity, and are commonly combined with one of the other three classes as opposed to being a separate semiconducting layer. None of the current formulations of material classes address the complete range of degradation processes; and this fact, more than the failure of any one chemistry, is the best materials-side case of the complementary, cross-cutting data-driven layer suggested.
TABLE 1
| Material class | Representative chemistries | Key reliability advantage | Representative references |
|---|---|---|---|
| Conductive polymer hydrogels (CPHs) | PEDOT:PSS, PPy, PAA-SA blends | Mixed ionic-electronic conductivity; reduces interfacial impedance mismatch | Xue et al., 2026; Wang, T. et al., 2025; Ji et al., 2025 |
| MXene and carbon nanomaterials | Ti3C2Tx MXene, graphene, CNT | High conductivity and surface area; supports multimodal sensing | Ren et al., 2023; Liu et al., 2023; Zhu et al., 2025 |
| Liquid metal and hybrid composites | EGaIn/Ga-alloys in polymer networks | Self-healing and high stretchability (>200% strain) | Hajalilou, 2025; |
| Adhesive and fibrous interface hydrogels | Silk fibroin, gelatin/chitosan adhesives | Low-drift, noise-tolerant electrode-tissue coupling | ; |
Material classes, their representative chemistries, and key reliability advantages.
Across all four material classes, a common reliability trade-off recurs, illustrated at the bottom of Figure 1: designs optimized for higher conductivity or greater stretchability tend to show heightened sensitivity to dehydration, electrode-interface corrosion, or motion-induced signal interference. This trade-off is a structural feature of hydrogel semiconductor devices as a class.
3 Degradation mechanisms in hydrogel semiconductor devices
Hydrogel semiconductor devices, irrespective of the material class underlying them, degrade in service through several mechanistically distinct pathways, summarized below and considered jointly, in light of their coupling.
3.1 Environmental instability: hydration-dependent property drift
Hydrogel semiconductor layers rely on a continuous ionic-conductive water network to sustain their electrical properties. Ambient humidity fluctuation, elevated skin temperature, and evaporative water loss progressively reduce ionic conductivity and alter the mechanical modulus of the hydrogel, producing a slow, cumulative drift in baseline device response that is difficult to distinguish from a genuine physiological signal change (). This effect is common to all four material classes, though it manifests differently: liquid-metal hybrids are comparatively robust to modulus drift but still depend on the surrounding hydrogel phase’s hydration, while CPHs and MXene composites are more directly sensitive to ionic-conductivity loss. Internal hydration-locking strategies and external elastomeric encapsulation have both been proposed to mitigate this drift (Zhang et al., 2025; ), yet the drift is compounded by diurnal and activity-dependent humidity cycling in real-world wearable use, conditions that are rarely reproduced in standard laboratory shelf-life testing.
3.2 Electrode corrosion and interfacial degradation
At the hydrogel-electrode interface, repeated mechanical flexing, ionic exchange, and micro-scale electrochemical reactions can degrade the metallic or conductive-polymer electrode surface over time, increasing contact impedance and introducing non-stationary noise into the acquired signal (). Because this degradation is interfacial rather than bulk-material in origin, it is often not captured by hydrogel-only stability metrics and requires device-level, in-situ characterization to detect, a limitation also highlighted in recent reviews of hydrogel-based soft bioelectronic interfaces more broadly (Jiao et al., 2025) and addressed directly by the adhesive interface hydrogels (). Machine-learning-based corrosion-rate prediction, already validated for structural metals and coatings using electrochemical impedance features, offers a conceptually transferable diagnostic approach for this mechanism (Rajendran and Subbian, 2026; Tiwari et al., 2025; Li, R. et al., 2026; Li, Y. et al., 2024).
3.3 Signal interference under ambulatory conditions
Even where the hydrogel and electrode remain structurally intact, motion artifacts, mechanical strain transients, and ionic-drift-induced baseline wander introduce interference that can obscure the physiological signal of interest. This is a well-documented challenge in wearable photoplethysmography (PPG) and electroencephalography (EEG) systems, where motion-induced artifacts remain a primary obstacle to continuous, ambulatory monitoring (Seok et al., 2021). Materials-level electrode redesign can partially mitigate this problem: a recently benchmarked crosslinker-free hydrogel electrode achieved intrinsic low-pass filtering that measurably reduced baseline drift and motion artifact relative to conventional Ag/AgCl electrodes (), illustrating that materials and data-driven approaches to this failure mode are complementary rather than competing.
3.4 Mechanical fatigue and cyclic swelling-shrinking
In addition to one-directional hydration loss, repeated swelling-deswelling processes, caused by the absorption of body sweat, variation in ambient humidity and local pressure, exert cyclic mechanical stresses different to constant dehydration on hydrogel semiconductor devices worn over extended periods. The swelling and shrinking behaviour of hydrogels depends on pore size, crosslinking density, and hydrophilicity, and can be specifically designed to suit biomedical use, but swelling-shrinking cycling becomes uncontrolled and repeated in a wearable application, leading to fatigue of the network and the formation of microcracks and delaminated electrode interface (). This fatigue builds up over time and is generally not apparent in short-periodic bench testing, but it directly adds to the interfacial degradation, as a fatigued, disbonding hydrogel network provides mechanical forces to the electrode interface in a less predictable manner than an intact one. Double-network and toughened hydrogel architectures, of the kind discussed for self-healing devices (Song et al., 2025), partially mitigate this fatigue but do not eliminate it under sustained cyclic loading.
3.5 Biofouling and microbial contamination
Continuous skin contacts exposes hydrogel semiconductor devices to biofouling from shed keratinocytes, sebaceous oils, and sweat-borne proteins, which form insulating layers that disrupt electrical contact and cause signal degradation or outright sensor malfunction, a well-documented problem for wearable electrochemical sensors operating in sweat (Wei et al., 2025). Beyond passive fouling, prolonged moist occlusive contact between a hydrogel device and skin can promote microbial colonization, which both accelerates biofouling and raises separate safety and hygiene considerations for long-term wear (). Antifouling strategies such as zwitterionic or PEGylated hydrogel surface chemistries and antibacterial nanoparticle additives have been developed specifically to counter this mechanism (Wei et al., 2025), but, as with the other degradation mechanisms surveyed here, these are materials-level countermeasures that do not by themselves provide any indication of when biofouling has begun to compromise signal quality in a given worn device a gap the sensor-fusion approach is positioned to help address, since biofouling-induced impedance rise has a signature that may be distinguishable from other degradation mechanisms given appropriately fused multi-channel data.
3.6 Polymer aging and chemical degradation
Independent of external environmental and mechanical stressors, hydrogel polymer networks undergo intrinsic chemical aging: hydrolytic, enzymatic, or oxidative breakdown of the polymer backbone or crosslinks over time, a process whose rate varies substantially by chemistry reported weight-loss rates for natural hydrogel formulations range from roughly 10%–40% over several weeks for slow-degrading cellulose-based networks to 50%–90% over one to 4 weeks for faster-degrading gelatin-based networks (). For most wearable sensing applications this aging is undesired, in contrast to the deliberately engineered biodegradation pursued in transient and sustainability-oriented electronics (), and it proceeds gradually enough that it can be difficult to separate from the environmental instability using bulk conductivity measurements alone. Polymer aging alters baseline mechanical and electrical properties in ways that shift the operating point of the regression-based forecasting models proposed, meaning that a forecasting model calibrated on a fresh device may require periodic recalibration as the underlying polymer network itself ages a consideration distinct from, and additional to, the sim-to-real gap discussed.
3.7 Coupled nature of degradation mechanisms
The six mechanisms above are coupled rather than independent. Environmental instability accelerates interfacial degradation, since a softened or dehydrated hydrogel matrix places greater mechanical stress on the electrode interface during motion; interfacial degradation raises baseline contact impedance, amplifying the apparent magnitude of motion-induced interference; mechanical fatigue from cyclic swelling-shrinking compounds both environmental instability and interfacial degradation through progressive network damage; biofouling adds an additional, externally driven impedance-rise pathway that is difficult to distinguish from corrosion using impedance magnitude alone; and polymer aging shifts the baseline against which all other mechanisms are measured. This coupling is why a single materials-level fix rarely resolves device reliability in isolation, and it is the mechanistic basis for the multi-channel, forecasting-based framework proposed.
4 Prior work in hydrogel devices: A systematic comparison
As introduced in Section 1.1, the closest prior review to the present work is He et al. (2025), which comprehensively surveys the integration of machine learning with hydrogel and ionogel soft sensors. Their review covers ML-assisted materials design and property prediction, and ML-enabled pattern-recognition applications including handwriting, gesture, object, motion, and speech recognition, health monitoring, and food testing. Individual device-level studies within that literature illustrate the pattern: convolutional neural networks have been used to decode hand-written digits and sign-language gestures from MXene-hydrogel and double-network hydrogel strain sensors, and machine-learning-enabled thermogalvanic hydrogels have been developed for synergistic thermal and strain sensing. In each case, the machine-learning component’s role is to improve the accuracy or richness of the sensed signal at a single point in time; none forecasts how the underlying sensor’s reliability will evolve over its service life, and none proposes a mechanism for detecting or compensating for the degradation mechanisms.
A second, complementary body of work addresses wearable sensor reliability from the materials side without a data-driven forecasting component. provide a comprehensive review of wearable electrochemical biosensors and explicitly identify biofouling, biocompatibility, and sensor degradation as open challenges, but the countermeasures they discuss antifouling coatings, hydrogel-based antifouling interlayers are materials interventions rather than computational ones. Kang et al. (2026), reviewing a closely related class of hydrogel-based self-powered electrochemical sensors, similarly identify a lack of standardized testing and reporting protocols as a central barrier to industrial scalability, echoing the standardization gap this Review addresses in Section 7, but do not propose a forecasting or optimization framework to address it. The materials-oriented reviews of hydrogel bioelectronic interfaces in general (Jiao et al., 2025) and of biodegradable electronic materials in general () are useful in terms of degradation chemistry and encapsulation strategy but also lack any data-driven content on reliability-prediction.
This is a comparison that is summarized in Table 2. The trend of all six comparators is that machine learning, where it exists, is used to enhance the sensing functionality itself, but not to predict or control the degradation of the sensor itself the difference that this Review is founded on, and the novelty claim made.
TABLE 2
| Study | Scope/primary focus | AI or data method used | Addresses reliability/degradation forecasting? | Research gap relative to this review |
|---|---|---|---|---|
| (He et al., 2025) (Adv. Sci.) | ML-assisted hydrogel/ionogel sensor design and pattern-recognition applications (gesture, handwriting, health monitoring) | CNN/ANN for signal classification; ML for materials property prediction | No focus is functional sensing accuracy, not long-term device reliability | No treatment of degradation forecasting, corrosion, or closed-loop correction |
| (Mu et al., 2025) (RSC Adv.) | Wearable sensors integrated with ML for human-machine interaction | General ML/DL for HMI signal decoding | No reliability and failure modes not addressed | No degradation-specific discussion; hydrogel materials treated only as substrates |
| () (Adv. Sci.) | Wearable electrochemical biosensors for healthcare monitoring | Materials/device review; biofouling noted qualitatively | Partial identifies biofouling and drift as challenges but proposes materials fixes only | No forecasting, optimization, or AI-based degradation mitigation proposed |
| (Jiao et al., 2025) (J. Mater. Chem. C) | Hydrogel-based soft bioelectronic interfaces, general applications | None (materials/device review) | No | No data-driven reliability content; complementary materials-side reference for this review |
| (Kang et al., 2026) (Front. Mater.) | Mechanically stimulated self-powered electrochemical sensors (related hydrogel device class) | None (mechanism/classification review) | Partial identifies lack of standardized testing as a challenge | No proposed forecasting framework; standardization discussed only qualitatively |
| () (Discov. Mater.) | Biodegradable electronic materials broadly (conductors, semiconductors, encapsulation) | None (materials/sustainability review) | No focus is end-of-life degradation and sustainability, not in-service reliability | Different degradation framing (intentional biodegradation vs. unwanted in-service failure); no AI/forecasting content |
| This review | Hydrogel semiconductor device reliability across four material classes | Regression, metaheuristic optimization, sensor fusion, corrosion-ML, LSTM (transferred from adjacent domains) | Yes central focus | Addresses the gap: no prior work systematically maps data-driven reliability/forecasting methods onto hydrogel semiconductor failure modes |
Systematic comparison of prior reviews and closely related studies.
5 Transferable data-driven forecasting and optimization techniques
Four families of data-driven technique, each mature in an adjacent domain, offer conceptually transferable value for the degradation mechanisms, alongside the comparative advantages, computational costs, and cross-domain transfer assumptions that govern their applicability. Table 3 summarizes the full comparison.
TABLE 3
| Technique | Origin domain | Failure mode addressed | Computational requirement | Data requirement | References |
|---|---|---|---|---|---|
| Regression-based SOH/RUL forecasting | Li-ion battery management | Environmental instability (hydration loss, modulus drift) | Low-moderate (linear/ensemble models feasible on-device) | Moderate: labeled aging trajectories needed | Wang Y. et al., 2025; |
| Metaheuristic-optimized tuning (PSO, GWO) | Renewable energy/power-electronic control | Adaptive recalibration under variable ambient conditions | Moderate: iterative search, typically run periodically off device | Low: operates on existing forecast-model parameters | ; Shaikh et al., 2025 |
| Sensor fusion/adaptive filtering | Wearable PPG/EEG motion-artifact suppression | Signal interference; separating drift from transient noise | Low: filtering is lightweight, real-time capable | Low-moderate: synchronized multi-channel streams | Seok et al., 2021 |
| ML corrosion-rate/EIS-coupled diagnostics | Structural and in situ corrosion monitoring | Electrode corrosion and interfacial degradation | Moderate-high: gradient boosting/deep models | High: impedance-spectrum-labeled corrosion data | Rajendran and Subbian, 2026; Tiwari et al., 2025; Li, R. et al., 2026; Li, Y. et al., 2024 |
| ML-assisted defect detection (additive manufacturing) | Additive manufacturing quality assurance | Fabrication-stage defects seeding later degradation | High: CNN-based image/process monitoring | High: labeled defect imagery | Ng et al., 2024 |
| LSTM/recurrent time-series forecasting | Structural health monitoring (fiber-optic fatigue) | Cumulative mechanical fatigue at electrode interface | High: sequential model training and inference | High: long-duration labeled time series | Jia et al., 2025 |
| Materials-level anti-dehydration/encapsulation (non-AI baseline) | Hydrogel materials science | Environmental instability (direct fix, not a forecast) | None (materials intervention) | None | Zhang et al., 2025; ; ; ; |
Comparison of data-driven techniques for hydrogel semiconductor reliability.
5.1 Regression-based degradation forecasting
In the battery domain, data-driven state-of-health (SOH) and remaining-useful-life (RUL) estimation has matured into a well-validated methodology, using regression models trained on voltage, current, temperature, and impedance-derived features to forecast capacity fade and internal resistance growth over the device lifetime (Wang T. et al., 2025). Reviews of electric-vehicle battery degradation estimation similarly emphasize that regression and hybrid data-driven models can achieve reliable forecasts using operationally accessible signals, without requiring destructive or invasive characterization (). A parallel line of work has coupled electrochemical impedance spectroscopy directly with machine-learning regression to assess degradation probability from impedance features alone (Li, Y. et al., 2024), which is methodologically close to what proposes for hydrogel devices. The conceptual parallel to hydrogel semiconductor devices is direct: impedance, strain-response, and conductivity signals already acquired during normal device operation could, in principle, serve as regression features for forecasting hydration loss, modulus drift, or the onset of interfacial corrosion, long before these manifest as an outright signal failure.
5.2 Metaheuristic-optimized parameter tuning
Metaheuristic algorithms such as Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO), and their hybrids, have been widely applied to tune control and forecasting-model parameters in renewable energy and power-electronic systems, consistently outperforming classical fixed-parameter tuning approaches under variable, real-world operating conditions (). Hybrid GWO-PSO variants have more broadly demonstrated superior convergence and solution stability over algorithm alone across complex, multi-constraint engineering design problems (Shaikh et al., 2025), reinforcing the case for hybrid rather than single-algorithm tuning in the hydrogel context. Applied to hydrogel semiconductor devices, an analogous approach would use metaheuristic optimization to adaptively tune the thresholds and coefficients of a degradation-forecasting model as environmental conditions (ambient humidity, wear duration, activity level) vary, rather than relying on a single static calibration derived from laboratory conditions alone.
5.3 Sensor fusion for interference mitigation
In wearable PPG and EEG systems, motion artifact suppression increasingly relies on fusing the primary physiological signal with auxiliary motion or reference channels (e.g., accelerometer data), using adaptive filtering or learning-based fusion to separate genuine signal from motion-induced noise (Seok et al., 2021). This principle maps naturally onto hydrogel semiconductor devices with multi-parameter sensing capability: fusing impedance, strain, and temperature channels could allow a forecasting model to distinguish a true degradation signature from a transient motion-induced fluctuation, improving both diagnostic and physiological signal accuracy. Such fusion is especially relevant for separating the biofouling-driven impedance rise from the corrosion-driven impedance rise, since the two mechanisms may produce similar single-channel signatures but different multi-channel ones (for example, biofouling would be expected to leave mechanical strain response comparatively unaffected, whereas corrosion-driven electrode delamination would not).
5.4 Corrosion prediction and manufacturing-stage defect detection
Beyond forecasting and control, two further machine-learning domains offer transferable value. Corrosion-rate prediction using random forest, gradient boosting, and deep learning models has been validated for structural metals and protective coatings under variable environmental exposure (Rajendran and Subbian, 2026; Tiwari et al., 2025), including approaches that couple electrochemical impedance spectroscopy directly with machine-learning classifiers for in situ corrosion assessment (Li, R. et al., 2026; Li, Y. et al., 2024) a pairing of technique and signal type that maps unusually well onto the impedance-based sensing already native to hydrogel semiconductor devices. This could analogously model interfacial corrosion at the hydrogel-electrode junction. Separately, machine-learning-assisted defect detection and closed-loop process control in additive manufacturing (Ng et al., 2024) is directly relevant where hydrogel semiconductor devices are fabricated via printing or layer-wise deposition, since fabrication-stage defects often seed the interfacial degradation observed later in service.
5.5 Why these methods: comparative advantages, limitations, and computational requirements
The four technique families were selected specifically because each maps onto a signal type or failure signature already native to hydrogel semiconductor sensing (impedance, strain, multi-channel time series), rather than requiring new sensing modalities; alternative approaches considered but not adopted as primary candidates include physics-based electrochemical models, which offer higher interpretability but require detailed knowledge of hydrogel-electrode reaction kinetics that does not yet exist, and pure anomaly-detection approaches, which can flag departures from normal operation but do not by themselves forecast a specific failure mode or timeline. As summarized the four selected families differ substantially in computational and data burden. Regression-based forecasting and sensor fusion are comparatively lightweight and, in simplified form, plausibly deployable on-device; metaheuristic optimization is typically run periodically rather than continuously, since it involves iterative search rather than single-pass inference; and the deep-learning-based corrosion and defect-detection methods (LSTM, CNN-based image monitoring) carry the highest computational and labeled-data burden of the four, making them, at present, better suited to periodic off-device or cloud-based analysis than to continuous on-device operation. This cost gradient directly informs the tiered architecture, where lightweight fusion and regression run continuously at the edge while more demanding corrosion- and defect-detection models run less frequently.
5.6 Limitations and assumptions in cross-domain transfer
Transferring these techniques from batteries, corrosion engineering, and structural health monitoring to hydrogel semiconductor devices is not a drop-in substitution, and several assumptions warrant explicit caution. First, signal magnitude and regime differ substantially: battery SOH models operate on volt-level electrical signals with well-characterized charge-discharge cycling, whereas hydrogel semiconductor devices operate on millivolt-to-microvolt-level ionic-electronic signals with no equivalent standardized cycling protocol, so feature-engineering choices that work well for batteries cannot be assumed to transfer without re-validation. Second, the origin-domain methods generally assume approximately stationary or slowly time-varying operating conditions between measurements (a parked, temperature-controlled battery pack; a fixed structural monitoring site), an assumption that is directly violated by ambulatory wearable use, where humidity, temperature, and mechanical loading can change within minutes; this is the central reason proposes continuous metaheuristic retuning rather than a one-time calibration. Third, the origin-domain methods are trained on large, labeled degradation datasets (decades of battery cycling data; extensive structural corrosion databases) that presently have no hydrogel-semiconductor equivalent, meaning that any near-term application of these techniques to hydrogel devices will necessarily involve substantial domain adaptation, transfer learning, or synthetic/accelerated-aging data rather than direct model reuse. Fourth, the failure statistics are not merely of a different degree but are often monotonic and irreversible: battery capacity fade or structural corrosion is in general monotonic and irreversible, but hydrogel environmental instability can be reversible to a degree, and consequently, it is necessary to modify the structure of the forecasting models to treat monotonic-degradation assumptions correctly in the presence of reversibility. They are formulated as open assumptions, which are to be empirically tested, rather than as established premises of the framework suggested.
6 Proposed conceptual framework and practical implementation pathway
Building on the material systems reviewed in Section 2 and the four technique families identified in Section 5, a conceptual monitoring pipeline for hydrogel semiconductor devices is proposed, illustrated in Figure 2. Beyond the conceptual architecture, this section develops a concrete implementation pathway, real-time deployment considerations, an uncertainty and explainability treatment, and positioning relative to materials-level strategies.
FIGURE 2
6.1 Sensing and data-acquisition layer
The framework starts with the sensing ability of the device with which it is designed to operate (that is, the native sensing capability), and which in a normal wear situation continuously measures impedance, strain, and ionic-conductivity signals without any extra hardware to the multimodal hydrogel semiconductor sensors it already includes. Impedance is used to measure the status of the electrode-hydrogel interface; strain is used to monitor the mechanical stimulus desired, as well as the mechanical fatigue; and ionic conductivity is an explicit measure of the hydration status underlying the environmental instability.
6.2 Forecasting and optimization core
The multi-channel signals obtained are then fed to a feature-extraction and sensor-fusion step which separates out the degradation-relevant trends in the signal stream between motion- and activity-induced transients. These combined capabilities are then inputted to a regression-based degradation and corrosion-prediction framework, the thresholds and coefficients of which are further refined through metaheuristic optimization in response to changes in ambient conditions. In line with the cost gradient, this core is designed with three levels of architecture: lightweight regression and fusion execute continuously; metaheuristic retuning executes periodically, a design choice; and the more computing-intensive corrosion- and defect-specific models executes at a lower duty cycle, a design choice.
6.3 Closed-loop correction and alerting
When the output of the forecasting core exceeds a specified reliability level, the framework will either send a real-time signal correction, like re-calibration of the baseline with the predicted drift, or a maintenance warning to the user or clinician, indicating that the device might need replacement or maintenance before signal quality deteriorates further. It is this closed-loop which makes the proposed structure stand out of a simply diagnostic forecasting model: the system is not expected to simply report its predictions, but be able to act on them, in a similar way that battery management systems can modify charging behavior based on state-of-health predictions made by the system.
6.4 Practical implementation pathway
Sensing hardware: Bipolar or four-point impedance-measurement circuit to enable a frequency sweep (10 Hz-10 kHz) across the hydrogel-electrode interface; resistive or capacitive strain gauge co-located with the sensing area; low-power temperature/humidity sensor positioned at or near the skin interface. Input parameters: magnitude and phase of impedance at a small number of representative frequencies, strain amplitude and rate, and local temperature/humidity, all sampled at the recommended cadence. Data processing steps: (i) baseline normalisation against an initial post-application calibration window; (ii) feature extraction, such as the rate of impedance drift, strain-response variance under a fixed load and short term autocorrelation of the ionic-conductivity signal; (iii) sensor fusion to isolate true drift and transient artifact; and (iv) forecast-model inference to give a degradation-risk score to each of the mechanisms. Anticipated outputs: a constantly updated forecast of drift (e.g., predicted days before the baseline drift has surpassed a clinically/functionally significant threshold), a risk indicator of corrosion/interfacial-degradation, and, when thresholds are exceeded, a discrete alert event. Validation under real operating conditions would proceed in three stages: bench-scale accelerated aging under controlled humidity/temperature/strain cycling to establish ground-truth degradation trajectories against which the forecasting model’s predictions can be scored; short-duration (multi-day) human wear trials comparing forecast output to periodic destructive or semi-destructive inspection; and, only after these are satisfied, longer-duration ambulatory field trials of the kind as a precursor to clinical validation.
6.5 Real-time deployment: edge computing and hardware constraints
A wearable hydrogel semiconductor device is subject to strict constraints on memory, processing speed, and power that materially shape which of the techniques can run on-device versus requiring an external computing tier. Tiny Machine Learning (TinyML) approaches, which compress and quantize models for deployment on microcontroller-class hardware, have demonstrated that lightweight classification tasks can run continuously on wearable-class devices at sub-milliwatt power draw a recent TinyML implementation for real-time ECG anomaly detection on edge hardware achieved 92.3% accuracy at 0.024 mW using model pruning and quantization on commodity microcontroller platforms (Hizem et al., 2025). This precedent supports the tiered architecture proposed: the regression and sensor-fusion components of the forecasting core, being comparatively lightweight, are plausible candidates for on-device, continuous execution using similar quantization and pruning techniques, whereas the deep-learning-based corrosion and manufacturing-defect models and metaheuristic optimization are better suited to periodic execution, either on a paired smartphone or cloud tier, or in a low-duty-cycle on-device mode. This tiering explicitly makes trade-offs between forecast latency and offline capability versus battery life and cost of devices an explicit and reported trade-off, not an assumed one, which must be made explicit and reported in future work that implements this framework.
6.6 Uncertainty quantification and model explainability
Since the outputs of the framework are to be used to drive user- or clinician-facing alerts, the forecasting core must provide calibrated uncertainty with each prediction, distinguishing between aleatoric uncertainty (irreducible measurement noise, e.g. due to motion artifact which was not completely removed by sensor fusion) and epistemic uncertainty. A degradation forecast as a single point estimate with no confidence interval runs the risk of excessively or insufficiently driving the closed-loop correction; an uncertain forecast could be surfaced as advisory and not automatically acted upon. In addition to uncertainty, explainable artificial intelligence (XAI) should be a priority of wearable healthcare applications due to the fact that black-box predictions are less likely to be accepted and followed up by end users and clinicians; a review of explainable AI (XAI) in the context of wearable data analytics applications has identified that explainability methods including feature-attribution techniques are underutilized in this field compared to their potential to establish user trust (). When applied to the model suggested here, a feature-attribution methodology may show (along with a corrosion-risk alarm) which of the input channels (impedance drift, strain variance, humidity exposure) contributed the most to the prediction, providing the user or clinician with an interpretable foundation of the alarm as opposed to an unexplainable score. Uncertainty quantification and explainability have not yet been applied to hydrogel semiconductor degradation forecasting specifically; nor have either of them been applied as refinements to the framework.
6.7 Positioning relative to materials-level reliability strategies
This framework is explicitly complementary to, rather than a substitute for, the materials-level strategies summarized in Section 2 and the final row of Table 2, such as hydration-locking additives, elastomeric encapsulation, antifouling coatings, and low-drift adhesive electrode design (Zhang et al., 2025; ; ; ; ; Wei et al., 2025). Materials-level interventions raise the baseline reliability ceiling of the device; the data-driven layer proposed here extends the usable service life within that ceiling by detecting and compensating for degradation as it accumulates, echoing the “interface economy” logic recently proposed for battery electrode interfaces, where a site’s reversible benefit is weighed against its irreversible cost rather than optimized against capacity alone (Wu et al., 2026). The framework is deliberately conceptual at this stage: it is intended as a proposed research direction and integration architecture, with the implementation pathway as a concrete next step toward validation, rather than a validated implementation.
7 Toward a standardized dataset structure for hydrogel semiconductor reliability
In all cases, the lack of standardised and shared datasets of degradation is the key empirical limitation to the empirical validation of the forecasting methods. Table 4 suggests a specific minimal dataset format, structured around the six types of data a hydrogel semiconductor reliability dataset should be based on: environmental conditions, electrical measurements, mechanical loading, degradation indicators, device/session metadata, and a validation protocol. This architecture is inspired by the type of multi-modal, time-stamped logging that was found to be useful in battery SOH data, modified to the signal types and degradation processes of hydrogel semiconductor devices.
TABLE 4
| Data category | Recommended fields | Suggested sampling/notes |
|---|---|---|
| Environmental conditions | Ambient relative humidity (%RH), skin/ambient temperature (°C), sweat pH and ionic content where measurable | 1–10 min intervals; continuous logging during wear periods |
| Electrical measurements | Impedance spectrum (magnitude/phase across a defined frequency sweep), baseline signal drift, contact resistance | Impedance sweeps every 10–60 min; continuous baseline logging |
| Mechanical loading | Strain amplitude and frequency, cumulative flex-cycle count, applied pressure where relevant | Event-triggered logging plus periodic cumulative-cycle summaries |
| Degradation indicators (ground truth) | Visual/microscopic inspection score, hydration weight loss (%), SEM-verified corrosion depth, biofouling/contamination index | Destructive or semi-destructive checkpoints at fixed intervals (e.g., days 1, 7, 14, 30) |
| Device and session metadata | Material class (Section 2), electrode type, encapsulation strategy, wear location, session duration | Logged once per device/session; enables cross-study comparison |
| Validation protocol | Split between accelerated bench aging and real ambulatory wear; cross-device and cross-subject validation folds | Report forecast horizon accuracy at a fixed false-alarm rate (Section 5.5) |
Proposed dataset structure for hydrogel semiconductor reliability studies.
There are two design decisions that should be explicitly justified. To begin with, destructive or semi-destructive ground-truth checkpoints (e.g., periodically measuring microscopy or weight-loss) must be included as well as non-destructive electrical logging to run because, the field does not yet have validated mappings between electrical signatures and true degradation state of hydrogel-electrode interfaces; otherwise, a forecasting model will be trained on electrical trends that do not actually indicate physical degradation. Second, the recommendation to report forecast accuracy at a fixed false-alarm rate rather than a single aggregate accuracy figure follows directly from the closed-loop alerting design, where the practical cost of a false alert and a missed detection are asymmetric and application-dependent. Table 4 is intended as a proposal to seed community discussion and coordinated data collection, of the kind that catalyzed comparable progress in battery health estimation.
8 Regulatory considerations and pathways to clinical validation
Hydrogel semiconductor devices intended for healthcare applications, and any AI/ML-based forecasting layer added to them, face regulatory and validation requirements largely unaddressed in the hydrogel materials literature to date. At the materials level, biocompatibility of skin-contacting hydrogel and electrode materials is governed by standards such as ISO 10993 (biological evaluation of medical devices), and electrical safety of any powered wearable component falls under frameworks such as IEC 60601 for medical electrical equipment; reviews of biodegradable and transient electronic materials for biomedical use likewise emphasize that regulatory and biocompatibility considerations remain a major open challenge for translating novel electronic materials into clinical products (), a challenge equally applicable to the non-biodegradable hydrogel semiconductor devices discussed in this Review.
The forecasting and closed-loop correction layer introduces an additional regulatory dimension specific to software-driven medical functionality: where a degradation forecast or alert is used to inform a healthcare decision, the forecasting model itself may fall within the scope of software-as-a-medical-device frameworks, which increasingly address AI/ML components specifically, including expectations around change control when a model is updated post-deployment and around the kind of uncertainty and explainability treatment discussed. A jurisdiction-by-jurisdiction regulatory analysis is beyond this review’s materials-and-methods scope, but three practical implications are worth flagging for researchers developing systems of this kind. First, the bench and short-duration wear validation proposed should be designed from the outset with downstream regulatory evidence requirements in mind (e.g., pre-specified accuracy and false-alarm-rate targets), rather than retrofitted later. Second, any closed-loop automatic correction that alters signal output rather than merely flagging degradation carries a higher regulatory bar than passive monitoring alone, and early-stage implementations may reasonably restrict the framework to advisory alerting until forecast reliability is established. Third, a realistic path to clinical validation likely proceeds through the same staged structure bench validation, controlled short-duration human wear, then longer-duration ambulatory field study with formal clinical validation and regulatory engagement following, rather than preceding, the establishment of a standardized dataset and benchmark against which forecasting performance can be demonstrated.
9 Remaining challenges and future perspectives
Data scarcity, standardization, and the computational and regulatory constraints on deployment have already been addressed above, in the sections where they are most directly relevant, rather than gathered into a single generic “future work” discussion. This section discusses the two challenges not fully addressed elsewhere and closes with a forward-looking outlook; Table 5 cross-references each open challenge to the section that addresses it.
TABLE 5
| Open challenge | Description | Where addressed in this review |
|---|---|---|
| Data scarcity | No standardized, shared hydrogel degradation datasets spanning realistic wearable-use conditions | Section 7 (proposed dataset structure, Table 4) |
| Sim-to-real gap | Laboratory shelf-life/cycling tests do not reproduce combined humidity, motion, and temperature variability of daily wear | Section 9.1 |
| Standardization and reproducibility | Hydrogel composition, electrode material, and test protocol vary widely across studies | Sections 4, 7 |
| Regulatory and clinical validation | Standards pathways for AI-enabled hydrogel bioelectronics are undefined | Section 8 |
| Integration with materials-level strategies | Forecasting and encapsulation/hydration-locking approaches are currently developed independently | Sections 6.7, 9.2 |
| Real-time deployment | Memory, power, and latency constraints of wearable hardware | Section 6.5 |
Summary of open challenges identified and recommended.
9.1 The sim-to-real gap in wearable deployment conditions
Laboratory shelf-life and cycling tests may not adequately reproduce the combined humidity, motion, and temperature variability of everyday wearable use, so forecasting models trained on laboratory data risk poor generalization in the field. This gap is well documented in the battery literature, where models trained on standardized cycling protocols have required substantial recalibration for field deployment under real driving conditions (); an analogous recalibration step should be anticipated when transferring these methods to hydrogel devices worn under variable, uncontrolled daily conditions. For hydrogel semiconductor devices specifically, the sim-to-real gap is likely to be more pronounced than in the battery case, since wearable use introduces at least three simultaneously varying environmental axes (humidity, temperature, mechanical loading) rather than the more constrained charge-discharge cycling protocols typical of battery testing, reinforcing the case for the metaheuristic-based adaptive retuning rather than a single fixed forecasting model calibrated once in the laboratory.
9.2 Integration with materials-level reliability strategies
The forecasting framework proposed here is intended to complement, materials-level advances. A practical open question is how tightly these two layers should be co-designed: for example, whether encapsulation strategies should be tuned to produce a more predictable, forecastable degradation signature, rather than being optimized solely to minimize degradation rate in isolation. Addressing this question will likely require closer collaboration between materials scientists and forecasting-model developers than is currently typical in either community, a point revisited in the Conclusion.
9.3 Future outlook: multimodal, scalable, self-monitoring systems
Looking further ahead, and summarized alongside the maturity comparison in Figure 3, three converging directions can be anticipated. First, future hydrogel semiconductor devices are likely to become increasingly multimodal, simultaneously sensing mechanical, thermal, and electrochemical cues across the material classes; the forecasting core would need to expand accordingly to fuse an even wider range of input channels. Second, scalable fabrication routes such as printing and layer-wise deposition, which are themselves a source of manufacturing-stage defects as discussed, will increasingly need in-line, ML-assisted quality control integrated directly into the production pipeline. Third, and most ambitiously, a validated version of the proposed framework could extend substantially beyond hydrogels to the broader class of soft, ionic-electronic bioelectronic devices, providing a general template for predictive reliability engineering in wearable and implantable soft electronics.
FIGURE 3
10 Conclusion
Hydrogel semiconductor devices, across the conductive-polymer, MXene/carbon, liquid-metal, and adhesive-interface material classes reviewed, share a common reliability bottleneck: environmental instability, electrode corrosion, signal interference, mechanical fatigue, biofouling, and polymer aging six mechanistically coupled degradation mechanisms that materials-level interventions alone have not fully resolved. No single material chemistry currently formulated resolves all these simultaneously, which is the strongest materials-side motivation for the complementary data-driven layer this review has proposed. This Review has argued that data-driven forecasting and optimization techniques already mature in adjacent domains are conceptually transferable to this problem, while being explicit about the limitations and cross-domain assumptions involved. A conceptual framework integrating sensing, forecasting, and closed-loop correction has been proposed, together with a practical implementation pathway, edge-computing considerations, and an uncertainty/explainability treatment intended to move the framework from concept toward a testable system. A standardized dataset structure has further been proposed, and the regulatory and clinical-validation considerations that any practical implementation must eventually address have been outlined. Looking ahead, four concrete directions for future research can be identified. First, experimental validation of the proposed framework, beginning with the bench-scale accelerated-aging studies outlined, is the necessary next step to move towards the conceptual stage of this work. Second, coordinated development of the benchmark datasets proposed, ideally as a shared community resource analogous to public battery-cycling datasets, would allow the forecasting techniques compared to be evaluated on a case-to-case basis. Third, standardized reliability-testing protocols, building on the dataset structure and the regulatory considerations, would improve cross-study comparability in a field, testing conditions currently vary widely.
Statements
Author contributions
AG: Conceptualization, Writing – original draft, Investigation, Writing – review and editing. SP: Writing – original draft, Methodology, Writing – review and editing, Supervision. JS: Writing – original draft, Writing – review and editing, Formal Analysis, Validation.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1
AbdelaalY.AupetitM.BaggagA.Al-ThaniD. (2024). Exploring the applications of explainability in wearable data analytics: systematic literature review. J. Med. Internet Res.26, e53863. 10.2196/53863
2
Águila-LeónJ.Vargas-SalgadoC.Díaz-BelloD.Montagud-MontalváC. (2024). Optimizing photovoltaic systems: a meta-optimization approach with GWO-enhanced PSO algorithm for improving MPPT controllers. Renew. Energy230, 120892. 10.1016/j.renene.2024.120892
3
AlsaafeenN.ZiogasI.AlsaediS.AlshehhiM.AlmheiriS.RehmanA.et al (2026). Systematic benchmarking of a noise-tolerant conductive hydrogel electrode for epidermal bioelectronics. Adv. Sci.13 (20), e15131. 10.1002/advs.202515131
4
AnH.ZhangM.ZhouL. P.HuangZ.DuanY.WangC.et al (2023). Anti-dehydration and rapid trigger-detachable multifunctional hydrogels promote scarless therapeutics of deep burn. Adv. Funct. Mater.33, 2211182. 10.1002/adfm.202211182
5
BaiM.ChenY. R.ZhuL. Y.LiY.MaT.LiY.et al (2024). Bioinspired adaptive lipid-integrated bilayer coating for enhancing dynamic water retention in hydrogel-based flexible sensors. Nat. Commun.15, 10569. 10.1038/s41467-024-54879-7
6
CaiD. H.XiaR.ShaoY.ChenG.LiuL.LiY.et al (2025). Mechanically compatible sealing of hydrogel with coherent interface. Adv. Mater.37, 2414515. 10.1002/adma.202414515
7
Calderón MorenoJ. M.CheluM.PopaM. (2025). Eco-friendly conductive hydrogels: towards green wearable electronics. Gels11 (4), 220. 10.3390/gels11040220
8
CaoY.LiH.LiuC.LiX.FuF.YuJ. (2026). Protonation-mediated multifunctional silk fibroin hydrogel adhesives for epidermal interface sensing. Sci. China Mater.69 (4), 2050–2064. 10.1007/s40843-025-3935-1
9
DasK.KumarR. (2023). Electric vehicle battery capacity degradation and health estimation using machine-learning techniques: a review. Clean. Energy7 (6), 1268–1281. 10.1093/ce/zkad054
10
DennisonM. S.JebabalanS. K.SamrajA.ZaroogO. S.WanazusiT.MejiA. M.et al (2025). Biodegradable electronic materials for promoting sustainability in next-generation electronics a comprehensive review. Discov. Mater.5 (1), 182. 10.1007/s43939-025-00381-w
11
DuanH.PengS.HeS.TangS.-Y.GodaK.WangC. H.et al (2025). Wearable electrochemical biosensors for advanced healthcare monitoring. Adv. Sci.12 (2), e2411433. 10.1002/advs.202411433
12
FengW.WangZ. (2023). Tailoring the swelling-shrinkable behavior of hydrogels for biomedical applications. Adv. Sci.10 (28), 2303326. 10.1002/advs.202303326
13
HajalilouA. (2025). Liquid metal-polymer hydrogel composites for sustainable electronics: a review. Molecules30 (4), 905. 10.3390/molecules30040905
14
HeW.LinR.KongS.QiangM.HuangL.DaiB.et al (2025). Intelligent sensing: the emerging integration of machine learning and soft sensors based on hydrogels and ionogels. Adv. Sci.12 (48), e2517851. 10.1002/advs.202517851
15
HizemM.BousbiaL.Ben DhiabY.AoueileyineM.O.-E.BouallegueR. (2025). Reliable ECG anomaly detection on edge devices for internet of medical things applications. Sensors25 (8), 2496. 10.3390/s25082496
16
JiC.WangY.QiQ.LiY.CaoL. (2025). Electrically conductive hydrogels for flexible wearable devices: materials, design, and applications. Adv. Mater. Technol.10, e01044. 10.1002/admt.202501044
17
JiH.LyuY.TianZ.YeH.(2025). Assessment of corrosion probability of steel in mortars using machine learning. Reliab. Eng. and Syst. Saf.253, 110535. 10.1016/j.ress.2024.110535
18
JiaM.ZhouC.PeiX.XuZ.XuW.WanZ. (2025). Fatigue life prediction of CFRP-FBG sensor-reinforced RC beams enabled by LSTM-Based deep learning. Polymers17 (15), 2112. 10.3390/polym17152112
19
JiaoC.LiuJ.YanS.XuZ.HouZ.XuW. (2025). Hydrogel-based soft bioelectronic interfaces and their applications. J. Mater. Chem. C13, 2620–2645. 10.1039/d4tc04150j
20
KangJ.UmD.-Y.LeeS.KimS. (2026). Mechanically stimulated self-powered electrochemical sensors: principles, classifications, and future directions. Front. Mater.12, 1727201. 10.3389/fmats.2025.1727201
21
LiY.TanS.ZhangX.LiZ.CaiJ.LiuY. (2025). Design strategies and emerging applications of conductive hydrogels in wearable sensing. Gels11 (4), 258. 10.3390/gels11040258
22
LiR.WangH.ZhuY.MuX.WangX. (2026). Machine learning-assisted in situ corrosion monitoring: a review. Corros. Rev.44 (1), 20250014. 10.1515/corrrev-2025-0014
23
LiuY.FengH.GuiY.ChenT.XuH.HuangX.et al (2023). Flexible stretchable, dry-resistant MXene nanocomposite conductive hydrogel for human motion monitoring. Polymers15 (2), 250. 10.3390/polym15020250
24
MuG.ZhangY.YanZ.YuQ.WangQ. (2025). Recent advancements in wearable sensors: integration with machine learning for human-machine interaction. RSC Adv.15, 7844–7854. 10.1039/d5ra00167f
25
NgW. L.GohG. L.GohG. D.TenJ. S. J.YeongW. Y. (2024). Progress and opportunities for machine learning in materials and processes of additive manufacturing. Adv. Mater.36, 2310006. 10.1002/adma.202310006
26
RajendranM.SubbianD. (2026). Deep learning in corrosion assessment and control: a critical review of techniques and challenges. Corros. Rev.44 (1), 20240060. 10.1515/corrrev-2024-0060
27
RenY.HeQ.XuT.ZhangW.PengZ.MengB. (2023). Recent progress in MXene hydrogel for wearable electronics. Biosensors13 (5), 495. 10.3390/bios13050495
28
SeokD.LeeS.KimM.ChoJ.KimC. (2021). Motion artifact removal techniques for wearable EEG and PPG sensor systems. Front. Electron.2, 685513. 10.3389/felec.2021.685513
29
ShaikhM. S.LinH.XieS.DongX.LinY.ShivaC. K.et al (2025). An intelligent hybrid grey wolf-particle swarm optimizer for optimization in complex engineering design problem. Sci. Rep.15, 18313. 10.1038/s41598-025-02154-0
30
SongH. S.RumonM. M. H.KhanM. M. R.JeongJ. H. (2025). Toward intelligent materials with the promise of self-healing hydrogels in flexible devices. Polymers17 (4), 542. 10.3390/polym17040542
31
TiwariS.DashK.ParkN.ReddyN. G. S. (2025). Machine learning-based prediction of atmospheric corrosion rates using environmental and material parameters. Coatings15 (8), 888. 10.3390/coatings15080888
32
WangT.LiuJ.ZhaoY.LuY. (2025). Synergistic mastery: advancing mechanical and electrical harmony in conducting polymer hydrogel bioelectronics. Bioact. Mater.52, 300–317. 10.1016/j.bioactmat.2025.06.015
33
WangY.GuoS.CuiY.DengL.ZhaoL.LiJ.et al (2025). A comprehensive review of machine learning-based state of health estimation for lithium-ion batteries: data, features, algorithms, and future challenges. Renew. Sustain. Energy Rev.224, 116125. 10.1016/j.rser.2025.116125
34
WeiZ.LiY.GuoS.HanR.LuoX. (2025). A wearable electrochemical sensor utilizing multifunctional hydrogel for antifouling ascorbic acid quantification in sweat. Anal. Chim. Acta1337, 343548. 10.1016/j.aca.2024.343548
35
WuH.YinB.WangG.LiuX.JinC.LiuX. (2026). Hard carbon anodes for sodium-ion batteries: balancing closed-pore storage, surface reactivity, and sodium inventory. Front. Mater.13, 1865686. 10.3389/fmats.2026.1865686
36
XueY.NiZ.WangY.ShanL.LiuJ. (2026). Engineering conducting polymer hydrogels for bioelectronic interfacing. Adv. Funct. Mater.36 (29), e21327. 10.1002/adfm.202521327
37
ZhangX. C.LiD.YangX. X.WangL.LiG.WongT. W.et al (2025). Hydro-locking in hydrogel for extreme temperature tolerance. Science387, 967–973. 10.1126/science.adq2711
38
ZhuY.ChenB.LiuY.TanT.GaoB.LuL.et al (2025). Recent advances in conductive hydrogels for electronic skin and healthcare monitoring. Biosensors15 (7), 463. 10.3390/bios15070463
Summary
Keywords
conductive hydrogels, edge computing, explainable AI, hydrogel semiconductors, wearable bioelectronics
Citation
Geetha A, Preethi S and Santhakumar J (2026) Toward data-driven reliability in hydrogel semiconductor devices: forecasting approaches for degradation and signal stability. Front. Mater. 13:1945407. doi: 10.3389/fmats.2026.1945407
Received
22 July 2026
Revised
20 August 2026
Accepted
10 September 2026
Published
30 September 2026
Volume
13 - 2026
Edited by
Dipak K. Goswami, Indian Institute of Technology Kharagpur, India
Reviewed by
Milon Selvam Dennison, Kampala International University, Uganda
Updates
Copyright
© 2026 Geetha, Preethi and Santhakumar.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Anbazhagan Geetha, geethaa2@srmist.edu.in; S. Preethi, preethistar007@gmail.com
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.