A self-powered hydrogel electronic skin with decoupled multimodal sensing for closed-loop human-machine interactions

Domain: neuroscience
Published: 2026-02-12
Editorial outcome: minor_revision

Abstract
Bridging biological and artificial systems, intelligent interfaces drive the demand for flexible electronics that emulate the skin’s multifunctionality. However, achieving such multifunctionality in a compact, self-sustained form remains challenging, as multimodal sensors often rely on rigid materials, discrete components, and external power sources. Herein, this study presents a single-component poly(vinyl alcohol) hydrogel e-skin integrating thermogalvanic, piezoionic, and diffusion mechanisms for self-powered sensing of skin temperature, arterial pulsation, and sweat secretion, simultaneously. The hydrogel features high stretchability, low modulus, and a prismatic architecture synergizing ionic polarization. Moreover, a temporal machine learning model with local attention is developed to decouple multimodal signals. Of practical importance, an active multimodal signal generator wristband is developed as a multifunctional human-machine interface for physiological detection, robotic control, and haptic feedback reproduction. Hence, this hydrogel e-skin represents an efficient material platform for intelligent interactions, showing broad potential for real-time health monitoring, robotic control, and virtual reality.

Introduction
As human-machine integration technologies continue to develop, wearable electronic interfaces featuring multimodal sensing and closed-loop interactive capabilities have become a key enabler for intelligent devices1,2. To this end, flexible sensors are essential for real-time health monitoring of physiological parameters, such as body temperature, physical movements, and biochemical markers, which serve as the basis for embodied intelligence and human-machine interaction3,4. To date, single-signal monitoring wearable sensors have seen substantial development, with demonstrated applications across various physiological parameters5–7. To accelerate next-generation intelligent systems, current research has been increasingly focusing on integrated multifunctional platforms8,9. One feasible design strategy involves using thin, flexible substrates to accommodate multiple sensor modules that are based on different working mechanisms10,11. These modules, which may differ in response times and signal processing pathways, are arranged through stacked or parallel configurations into a representative architecture that enables coordinated multimodal sensing12,13. Despite the use of compact components, integrating different sensors and supporting circuitry often results in structurally complex, bulky devices that rely on external power sources14,15. As a result, such configurations not only add redundancy and complicate fabrication, but also compromise sensitivity and wearability, limiting their practical effectiveness in real-world scenarios16–18.

Unlike artificial electronic systems that rely on multiple discrete modules and complex circuitry, human skin exemplifies a seamlessly integrated, multimodal sensory system19–22. It features specialized dendritic endings of afferent nerve fibers that simultaneously perceive a diverse range of stimuli—temperature, pressure, and biochemical markers—with rapid response and high spatial resolution23. Inspired by natural skin, engineering electronic skins (e-skins) has attracted growing attention, driving the development of single-component multimodal sensors that simultaneously respond to multiple external stimuli24–26. These developments can simplify fabrication, lower energy consumption, and enable lightweight, miniaturized devices27,28. Recent advances in multimodal sensors and e-skins have demonstrated simultaneous perception of temperature, pressure, strain, and humidity by incorporating ionic conductors, conductive polymers, or hybrid nanocomposites (Supplementary Table 1)29. However, most existing single-component multimodal sensors suffer from asynchronous signal acquisition and redundant outputs, causing significant coupling errors and reduced sensing accuracy30. They also remain limited in system-level integration, often lacking capabilities of remote operation and independent energy supply31,32. In addition, only a few prototypes have achieved closed-loop perception and feedback, which is essential for practical human-machine interaction. Therefore, a major challenge lies not only in achieving skin-like multifunctionality but also in coordinating and integrating different mechanisms within a single material system33,34. Synergistic integration of these components is crucial for developing skin-like wearable devices that can simultaneously respond to multiple stimuli, enable closed-loop feedback, and operate with self-sustained power.

In this work, we present a hydrogel e-skin with self-powered multimodal sensing and closed-loop interactive capabilities. A high-strain (>800%), low-modulus (<300 kPa) poly(vinyl alcohol) (PVA) hydrogel is developed to achieve soft and conformal contact with the skin, which is critical for capturing subtle physiological signals. Multimodal sensing of body temperature, arterial pulsation, and sweat secretion is integrated within the PVA hydrogel, enabling real-time, non-invasive health monitoring. Each sensing function is modeled after a receptor mechanism in human skin: (i) thermal sensing via the thermogalvanic effect mimics the ionic current behavior of free nerve endings; (ii) piezoionic-enabled pulse monitoring reflects the mechanosensitive response of Merkel cells; and (iii) diffusion-based salt ion concentration sensing draws inspiration from the stretch-sensitive signaling of Ruffini endings (Fig. 1a). Notably, the thermogalvanic, piezoionic, and diffusion effects support both sensing and self-powering, enabling the hydrogel sensing unit to actively generate stimuli-responsive electrical signals. To integrate these mechanisms more effectively, the PVA hydrogel is engineered into a novel prismatic architecture that concentrates mechanical stress at the tip and synergizes the three effects along the skin-normal direction (Fig. 1b, Supplementary Figs. 1 and 2). This design also amplifies stress-induced ion polarization in the hydrogel, resulting in a significant increase in output current (from 0.7 to 100 μA) and mechanical sensitivity (GF > 18.7).

Building on the PVA hydrogel e-skin, an active multimodal signal generator (AMSG) wristband interface is developed for multifunctional human-machine interactions. In addition to self-powered multimodal sensing, the interface enables active closed-loop interactions by enabling gesture-based robot control and haptic feedback reproduction. The AMSG wristband comprises a signal generation unit based on the hydrogel e-skin, a signal reproduction unit, and signal processing and transmission circuitry (Fig. 1c). All electronic components, including the flexible printed circuit board, the signal reproduction unit, the sensor array and their connections are fully encapsulated into a flexible wristband, providing a wearable and comfortable interface for human users. Using machine learning (ML) algorithms, including long short-term memory (LSTM) and convolutional neural network (CNN) for signal processing, this interface is capable of high-accuracy classification and recognition of complex stimuli, high-reliability response to hand gestures, and reproduction of tactile sensations from robotic fingers (Fig. 1d). Hence, the hydrogel e-skin offers a promising all-round toolkit for real-time multi-stimulus interaction in complex human-machine systems, with potential applications in intelligent prosthetics, soft robotics, and immersive virtual interfaces.

Results
Design principle
Figure 2a illustrates the preparation process of the PVA hydrogel e-skin. Through freeze-thawing and solvent substitution, nanocrystalline formation and structural densification are achieved, yielding hydrogels with high strength, high stretchability, and skin-like modulus. Specifically, PVA powder was first dissolved in dimethyl sulfoxide (DMSO) at a concentration of 15 wt.% (Supplementary Fig. 3) to obtain a homogenous solution. Upon freeze-thawing, PVA macromolecular chain conformations pre-assembled into a loosely interwoven network, while some non-covalent interactions within and between the polymer chains were disrupted. DMSO was subsequently substituted by glycerol, a poor solvent, to further enhance PVA intermolecular interactions over polymer-solvent interactions, facilitating their transition into a compact network via hydrogen bonding (Supplementary Fig. 4). However, excessive stiffness of the organogel at this point could cause nonconformal skin contact, reducing practical sensitivity and stability due to stress concentration and poor mechanical coupling. To address this, glycerol was finally replaced by an aqueous ferric solution, forming a hydrogel with preserved chain entanglement and crystallinity but reduced stiffness. Nuclear magnetic resonance (1H-NMR) spectroscopy was used to validate the completeness of solvent substitution. The characteristic hydroxyl peaks of glycerol (ca. 4.41 and 3.35) were observed only after glycerol substitution and disappeared upon replacement with the ionic solution in the final hydrogel e-skin (Fig. 2b and Supplementary Fig. 5).

The sequential solvent substitution strategy offers a distinct advantage in preparing strong yet soft PVA hydrogels (Fig. 2c). To track mechanical changes throughout the process, PVA gels at each step after freeze-thawing, glycerol substitution, and final ionic solution substitution are designated as FT, GS, and IS, respectively. Notably, the fracture energy of GS (78.9 kJ m−2) and IS (50.4 kJ m−2) were 14 and 9 times that of FT (5.6 kJ m−2), respectively (Supplementary Fig. 6). Moreover, the final PVA hydrogel IS not only maintained the high stretchability of GS (>830%) but also reduced the effective modulus of elasticity from 2.73 MPa to 300 kPa (Fig. 2d), which is within the typical range of human skin.

The mechanical enhancement observed after glycerol substitution is attributed to the densified network structure, where PVA chains become more entangled and cross-linked (Fig. 2e). The nanocrystalline domains formed can arrest microcracks and hinder crack propagation via a pinning effect, thereby enhancing toughness. In amorphous regions, less entangled PVA chains act as sliding elements that distributes stress across the network, contributing to the overall strength and elasticity under deformation. Upon further stretching, the breaking of covalent bonds in individual chains releases not only their own elastic energy but also that stored in interconnected chains, enabling efficient energy dissipation. When glycerol is substituted by the ionic solution, Fe3+ ions can bind to the carboxyl oxygens of PVA chains and enhance cross-linking, but the aqueous environment also loosens the PVA network. Some strong interchain hydrogen bonds are thereby disrupted, reducing the energy dissipation from covalent bond breakage and the elastic energy stored in interconnected chains upon stretching. This effectively lowers the stiffness of the PVA hydrogel, despite the reduced stress response and overall toughness.

To validate and reveal the molecular mechanisms underlying the mechanical properties of PVA gels, a series of molecular-level characterizations were conducted. Differential scanning calorimetry (DSC) was performed to characterize the crystallinity of PVA gels. As shown in Fig. 2f, all samples exhibited a small endothermic peak around 170°C, attributed to the melting of imperfect or loosely packed crystalline domains. In contrast, a prominent melting peak at 245°C, especially in GS and IS, indicates the presence of more thermally stable, well-ordered crystalline domains. Using the melting enthalpy of 100% crystallized PVA as a reference, FT showed the lowest crystallinity of 7.92%, which increased to 30.2% in GS. IS exhibited a slightly lower crystallinity of 25.2% than GS, which is consistent with the observed decline in mechanical properties. As the hydrogen bonds between PVA chains can be partially disrupted by the aqueous solution and replaced by Fe3+ ionic coordination, the total number of hydrogen bonds is reduced in IS, thereby lowering the thermal stability of PVA molecular chains in its crystalline regions. This trend is also supported by Fourier-transform infrared (FTIR) spectroscopy (Supplementary Fig. 7). The stretching peak of the hydrogen bond O–H at ~3300 cm−1 exhibited a redshift in GS and a blue shift in IS, indicating enhanced hydrogen bonding with glycerol substitution and its reduction after ionic replacement.

X-ray diffraction (XRD) analysis was performed to further investigate the crystalline structures of the gels (Fig. 2g). All PVA gels exhibited a characteristic diffraction peak at 2θ = 19.3°, corresponding to the (101) plane of semi-crystalline PVA35. The peak was relatively weak in FT but became more pronounced in both GS and IS, indicating increased crystallinity following glycerol and ionic solution substitution. Wide-angle X-ray scattering (WAXS) further revealed the primary diffraction peaks of FT, GS, and IS at 1.32, 1.37, and 1.36 Å−1, respectively (Fig. 2h and Supplementary Fig. 8). According to the Bragg equation L = 2π/qmax, the average distance between crystalline domains decreased slightly from 0.48 nm in FT to 0.46 nm in GS and IS, indicating tighter packing of the crystalline domains. The comparable peak intensities and inter-crystalline distances in GS and IS suggest that the slight reduction in the crystallinity of IS is mainly due to water-induced loosening of the polymer network, while the crystalline domains formed during glycerol substitution are mostly preserved even after ionic solution substitution.

Two-dimensional correlation spectroscopy (2D-COS) can reveal more subtle changes in the molecular interactions of PVA gels (Fig. 2i)36. The synchronous and asynchronous spectra capture simultaneous and sequential variations in spectral intensity at specific wave numbers37, with red indicating positive correlations and blue indicating negative correlations. Five autopeaks were identified in the synchronous spectrum, centered at 1149 cm−1, 1143 cm−1, 1118 cm−1, 1084 cm−1, and 1013 cm−1. Based on Noda’s rule, the sequence of spectral changes was determined as 1084 cm−1 → 1143 cm−1 → 1118 cm−1 → 1149 cm−1 → 1013 cm−1, corresponding to the vibrations of C–O stretching (hydrogen-bonded) → C–C skeleton vibrations → C–C/C–O stretching in the main chain → C–C stretching in the main chain → C–O stretching (non-hydrogen-bonded). This sequence suggests that the introduction of Fe³⁺ in the ionic solution preferentially disrupts the original hydrogen-bonded hydroxyl network structure, destabilizing the interchain hydrogen bonding and the hydroxyl-rich PVA backbone. As a result, the PVA chain segments gradually rearrange into a gently softened cross-linked structure.

These characterization results reveal that while the stiffness of IS is significantly reduced, its crystallinity remains relatively high. This decoupling of crystallinity and stiffness is primarily attributed to the partial disruption of strong interchain hydrogen bonds and the formation of weaker Fe³⁺-mediated ionic interactions. Therefore, the sequential solvent substitution strategy shows clear advantages over conventional methods such as freeze-thawing, wet annealing, salt precipitation, and tensile-assisted processing38–45. Unlike these approaches that typically increased toughness at the expense of higher stiffness, this method enables the fabrication of soft, elastic PVA hydrogels (Fig. 2j, Supplementary Tables 2–4). The controlled stiffness allows the hydrogel to better conform to the skin and match the modulus of human tissue, thereby improving comfort and wearability.

Device characteristics
Leveraging its high stretchability, low Young’s modulus, and good biocompatibility, the PVA hydrogel e-skin shows strong potential as a flexible sensor material that can be conformally attached to human skin. More importantly, through the Fe³⁺ ionic solvent substitution, the hydrogel e-skin is ingeniously engineered for multimodal sensing of skin temperature, pressure, and electrolyte (Na⁺) concentration. Temperature sensing is achieved via the thermogalvanic effect, where a temperature gradient leads to redox imbalances. Pressure sensing relies on the piezoionic effect, in which mechanical stress induces directional migration of unipolar ions. Electrolyte sensing is enabled by the diffusion effect, where ion concentration gradients lead to differential ion diffusion. As these mechanisms operate through distinct ion-driven processes, they function independently in the same system with minimal interference, allowing for synergistic multimodal sensing.

Temperature sensing performance
First, the hydrogel e-skin functions as a self-powered temperature sensor, leveraging the natural temperature difference between human skin and the ambient environment to spontaneously generate a thermoelectric voltage. Under a temperature gradient, the Fe²⁺/Fe³⁺ redox equilibrium within the hydrogel e-skin shifts asymmetrically, with the reversible reaction favoring the lower-temperature direction. This generates a potential difference between the electrodes and drives a current through the external circuit (Fig. 3a). The reversibility of the redox reaction is confirmed by cyclic voltammetry (CV) tests (Supplementary Figs. 9 and 10), where the CV curves remained highly overlapping in 10 cycles at different scan rates and temperatures. This indicates that the oxidized species (Fe3+) formed at the hot side and the reduced species (Fe2+) at the cold side migrate efficiently to opposite electrodes under the concentration gradient, sustaining continuous current output.

Across different Fe²⁺/Fe³⁺ concentrations in the PVA hydrogels (Supplementary Fig. 11a), the highest Seebeck coefficient (Se) of 1.6 mV K−1 was found at a 0.05 M concentration, slightly lower than that in pure Fe²⁺/Fe³⁺ aqueous solutions (1.7 mV K−1). The high water content of the hydrogel also facilitates the thermogalvanic effect, as Se would rapidly drop at lower water content (Supplementary Fig. 11b). According to the liquid thermoelectric theory46–48, Se is the ratio of the temperature difference (ΔT) to the potential difference (ΔE) and directly related to the entropy change of the redox reaction (ΔSrc) by the equation Se = ΔE/ΔT = ΔSrc/nF, where n is the number of electrons transferred and F is Faraday’s constant. As described by Powell49, the molar entropy of ions is influenced by both their charge and effective ionic radius. Therefore, molecular dynamics (MD)50–52 simulation is used to estimate the solvation radii of Fe²⁺/Fe³⁺ ions in PVA hydrogels (Fig. 3b and Supplementary Note 1). The radial distribution function (RDF) and coordinated number distribution of oxygen atoms around Fe²⁺/Fe³⁺ ions were calculated, with the distance to the first minimum in the Fe–O RDF taken as the radius of the first solvation shell (R1). Both Fe3+ and Fe2+ exhibited a value of 6 for N(r) in r = R1, indicating that they are bonded to 6 aqueous molecules in the first solvation shell (Supplementary Tables 5 and 6)53. The R1 of Fe3+ and Fe2+ ions were 2.25 and 2.33 Å, respectively, with a difference of 0.08 Å, smaller than the interpolated value of 0.1 Å in pure water (Fig. 3c). While this slight reduction is attributed to the tighter binding of water molecules to Fe3+ and Fe2+ ions caused by PVA polymerization, the overall entropy change remains close to that in pure water. This indicates that the polymer network has minimal impact on the ionic thermoelectric effect, which is consistent with the measured Seebeck coefficients.

The thermoelectrical performance of the PVA hydrogel was evaluated by its electric outputs under various temperature gradients (Fig. 3d). As ΔT rose from 8 to 24 K, the short-circuit current (Isc) increased from 5.2 to 14.5 μA, and the open-circuit voltage (Voc) from 15 to 37 mV. Based on these measurements, the maximum output power density (Pmax) was calculated as 11 mW m−2, with the normalized power density (Pmax/ΔT^2) reaching 19 μW m−2 K−2. Moreover, the hydrogel demonstrates sensitivity to temperature changes as small as 0.1 K (Supplementary Fig. 12). Since the distance from the heat source can also affect output, the highest current density was found at a 3-mm distance, with lower performance observed at both closer and further distances due to a reduced temperature gradient (Supplementary Fig. 13).

To achieve stable and reliable target signals, the PVA hydrogel was fabricated into multiple four-pronged modular units and assembled into an array. At ΔT = 10 K, Voc stabilized at 16 mV, while Isc increased to 20 μA when scaled to 20 parallel units, demonstrating a highly linear relationship between current output and number of units (Supplementary Fig. 14). At increasing ΔT, both Isc and Voc showed a positive correlation, consistent with the COMSOL simulation results (Fig. 3e, Supplementary Figs. 15 and 16). In addition, the effective thermal conductivity (keff) of the hydrogel remained stable at ~0.45 W m−1 K−1 at temperatures from 295 to 320 K (Supplementary Fig. 17), while electrochemical impedance spectroscopy showed negligible variation in internal resistance (Supplementary Fig. 18). Although the final prismatic structure may slightly reduce thermoelectric currents compared with simple cubic blocks—due to the smaller electrode contact area at the apexes and higher overall resistance from the reduced volume—it still maintains a robust linear response (Supplementary Fig. 19). Together, these properties ensure stable thermal sensing performance of the hydrogel over a wide operating temperature window.

Pressure sensing performance
The piezoionic effect in the hydrogel e-skin arises from the charge imbalance induced by deformation-driven ionic flux (Fig. 3f). Within the hydrogel, the diffusion of anions is partially hindered by the polymer network due to their relatively large size, leading to localized ionic enrichment under deformation. As anions and cations are driven at different rates by stress concentration, their distinct distribution gradients generate a transient potential difference and a net current along the deformation direction. Further leveraging this, the prismatic structure design is introduced to amplify the vertical stress concentration, generating an electric field along the force direction (d33 mode). Compared to the cubic structure, this design achieved over a hundredfold increase in output current under identical stress conditions (Fig. 3g and Supplementary Fig. 20). Using COMSOL simulations, the ion movement and distribution in the prism were revealed (Fig. 3h). Ions were initially assumed to be distributed uniformly with no potential difference across the hydrogel. Upon loading, stress concentration and deformation at the prism tip led to changes in contact area and surface charge, resulting in high sensitivity to even low pressures (Supplementary Fig. 21).

Theoretically, the relationship between output current and applied pressure is derived in Supplementary Note 2 as
I = (eκ n+ Z+ / ηh) * F
F = k * N * E * (p-q)b^2 / 2(p-q)b / h^2 * 3h
where n+ = n- are the concentration of cations and anions, Z+ = Z- are their respective charges, e is the elementary charge (1.602×10−19 C), κ is permeability, η is viscosity, h, p, and q are prism height, bottom side length, and top side length, respectively, k is the mechanical transfer efficiency, N is the number of prisms, E is the effective Young’s modulus, and b is the prism height at steady state under pressure. This formula describes a linear relationship between output current and applied pressure. Meanwhile, the sensitivity (S) of the hydrogel e-skin was determined experimentally as the slope of the relative current change versus pressure, defined by S = δ(ΔI/I0)/δP54, where ΔI = I-I0 is the change in current, I0 is the initial current, and P is the applied pressure. As shown in Fig. 3i, the hydrogel exhibited a continuous current-pressure response, showing a higher sensitivity of 1.94 kPa−1 at pressures below 2 MPa and 0.4 kPa−1 above 2 MPa. While the linearity aligns with theoretical derivations, the sensitivity change is attributed to the prismatic structure. At low pressures, the prism tip deforms more readily, resulting in higher sensitivity. As pressure increases, the base of the prisms is stiffer and more resistant to deformation, thereby reducing sensitivity. This stiffness effect is also captured in Eq. (2) and supported by COMSOL simulations of the three-dimensional structure, which reveal that higher pressures are required to achieve deformation near the prism bases (Fig. 3j and Supplementary Fig. 22).

The repeatability of the hydrogel e-skin was evaluated under cyclic mechanical loading. While repeated loading typically leads to energy dissipation and irreversible structural changes in hydrogels, the hydrogel e-skin showed minimal long-term performance loss. Mechanical hysteresis and the Mullins effect were most evident only during the first loading-unloading cycle and quickly stabilized after two cycles (Supplementary Fig. 23). The hysteresis loops were mostly overlapping over 20 cycles, further indicating the hydrogel’s highly elastic behavior (Supplementary Figs. 24 and 25). In addition, across various loading frequencies, it exhibited stable current responses under both repeated stretching and compression (Supplementary Fig. 26). No significant decline in current output was observed even after over 500 cycles (Supplementary Fig. 27).

Moreover, the low-modulus prismatic hydrogel e-skin showcases high sensitivity to pressures as low as 60 Pa (Supplementary Fig. 28). Its dynamic response features a rapid pressure response time of only ~37 ms (Supplementary Fig. 29), which is significantly faster than the temperature response time (15.2 s) and comparable to that of human skin (30–50 ms). The hydrogel e-skin is also capable of generating distinct current response waveforms at different compression rates, such as 1 and 10 mm s−1, indicating its ability to dynamically track varying loading speeds (Supplementary Fig. 30). With its high sensitivity and repeatability to pressure stimuli, the hydrogel e-skin shows strong potential for physiological monitoring. Notably, it can capture high-resolution pulse waveforms (Fig. 3k and Supplementary Fig. 31), with clearly identifiable Percussion (P), Tidal (T), and Dicrotic (D) waves, demonstrating its suitability for real-time pulse and blood pressure monitoring.

Electrolyte sensing performance
In addition to temperature and pressure sensing, the hydrogel e-skin is also designed for continuous, non-invasive monitoring of sweat electrolytes, another key indicator of physiological health. To this end, the polydimethylsiloxane (PDMS) substrate in contact with the skin is engineered with interfacial hydrophilic pores (Supplementary Fig. 32). Although PDMS is widely used as a substrate material in wearable and microfluidic devices due to its flexibility and physicochemical stability, its inherent hydrophobicity greatly limits its effectiveness in liquid transport applications14. To overcome this, the PDMS surface is modified through hydrochloric acid treatment to achieve durable hydrophilicity. The improved surface wettability, as evidenced by a reduced contact angle (Fig. 3l and Supplementary Fig. 33), significantly enhances sweat transport efficiency, resulting in a transport rate of 3.3 ml min−1 upon initial surface moisturization by sweat (Fig. 3m). Through the engineered hydrophilic channels, sweat is captured by the hydrogel e-skin and transported across the concentration gradient, generating a potential difference and a measurable current change (Fig. 3n). In addition, the ionic diffusion is further enhanced by the Soret effect, which arises from the temperature gradient between the skin and the ambient environment and leads to differential ion mobility. Using NaCl solution to emulate human sweat, the hydrogel e-skin demonstrates excellent linearity and reusability. In Fig. 3o, the output current gradually increased to 2 μA as the ion accumulated during sweat uptake, whereas adding pure water diluted ion concentration, leading to a significant decrease in current. Moreover, the hydrogel maintained a stable current response over 10 cycles of alternating NaCl solution and pure water stimulation, demonstrating reusability for practical applications (Fig. 3p). Compared with cubic shapes, the prismatic structure showed no significant difference in ion diffusion currents, as the two ion transport distances were comparable in solvated environments (Supplementary Fig. 34).

Together, these results demonstrate the strong performance of the hydrogel e-skin, with its desirable sensitivity, stability, and detection range to temperature, pressure, and electrolyte stimuli. Notably, its self-powered multimodal sensing marks a significant advantage over previously reported sensors that relied on external power sources or unsustainable self-powered systems.

Active multimodal signal generator wristband interface
Building upon the self-powered multimodal sensing capability of the hydrogel e-skin, an AMSG interface is developed. The hydrogel-based AMSG unit is assembled with a signal replication module, signal processing and transmission circuits, and a flexible encapsulation layer into a highly integrated, multifunctional wristband. This interface not only enables reliable, real-time, and non-invasive monitoring of the aforementioned key physiological signals but also supports comprehensive human-machine interaction through gesture command control, remote temperature alerts, and haptic feedback.

Machine learning-based multimode sensing applications
The human body, as a natural heat source, typically maintains a skin temperature around 36°C, which may vary with health conditions such as fever. Simultaneously, arterial pulsations and spontaneous perspiration produce continuous pressure and ionic stimuli, respectively, as key physiological inputs for the AMSG. To replicate these stimuli in a controlled laboratory setting, a custom multimodal stimulation platform is developed (Fig. 4a). It simulates body temperature using a heated Peltier element at the base, emulates arterial pulses via applied pressure from above, and replicates sweat secretion by dispensing NaCl solution. Notably, the AMSG exhibits distinct current patterns in response to each stimulus: with temperature changes, the current increases and decreases gradually; under applied pressure, it shows a sharp rise followed by a slow recovery; and with varying electrolyte concentrations, the current magnitude changes proportionally.

To investigate potential crosstalk between the signals, we applied repeated pressure stimuli of 2, 4, 6, and 8 kPa under different temperature gradients (Supplementary Fig. 35a) and different Na+ concentrations (Supplementary Fig. 35b). The piezoionic current increments remained nearly identical in all cases, indicating that the pressure-induced ionic signals are not affected by variations in temperature nor Na⁺ concentration. This independence arises from the distinct physical origins of the signals: the piezoionic current is generated by mechanically induced, transient ionic polarization, whereas the thermoelectric and diffusion currents are driven by inherently slow, steady-state processes. A minor interaction was observed between the temperature and Na⁺ concentration signals: a larger temperature gradient produced higher diffusion currents because temperature not only drove the thermogalvanic potential but also triggered the Soret effect, causing additional Na+ migration along the thermal gradient (Supplementary Fig. 36). Nevertheless, within the temperature range investigated in this work, both temperature- and concentration-driven diffusion remained in the linear regime, which allows their contributions to superimpose proportionally and produce a stable, predictable current response.

It is also worth noting that the evolution of the current response typically exhibits a higher-order Markov property. Therefore, the output at any given time depends primarily on the present and recent stimulus states rather than on distant past or future inputs55. This localized dependence and stationary behavior provides a theoretical basis for real-time decoupling of multiple simultaneous physiological inputs. To implement this, an ML-based decoupling model is developed (Fig. 4b), featuring three stacked LSTM layers with a local attention mechanism, followed by three fully connected layers. As a variant of recurrent neural networks (RNNs), LSTM integrates a forget gate, input gate, and output gate to adaptively regulate information flow and efficiently capture long-term dependencies56,57. The attention mechanism is mathematically defined as:
et,j = score(st, sj), j ϵ [t-40, t+40]
ct = Σj exp(et,j) * sj / Σk exp(et,k)
xt = [st, ct]
where et,j is the attention weight, st is the input signal at the current timestep, sj is the historical signal within a certain window, and ct is the context vector. ct and the current signals over the latest 10 s are concatenated to obtain xt for each LSTM time step. Given that a single stimulus typically generates a signal lasting about 40 s, the local attention window is set to [t−40, t+40] to capture the most relevant temporal context. This localized attention not only enhances feature extraction from critical regions but also substantially reduces computational complexity by minimizing the influence of noise from distant time points58. Based on this attention mechanism, the internal model of LSTM is further constructed (Fig. 4c), with its internal computational flow defined by the following equations:
ft = σ(Wf * [ht-1, xt] + bf)
it = σ(Wi * [ht-1, xt] + bi)
Ce_t = tanh(WC * [ht-1, xt] + bC)
Ct = ft * Ct-1 + it * Ce_t
Ot = σ(WO * [ht-1, xt] + bO)
ht = Ot * tanh(Ct)
where ft is the forget gate output, which retains a portion of the historical state, it is the input gate modulating the contribution of new input, Ce_t is the candidate cell state, representing a nonlinear transformation from the current input to previous hidden states, Ct is the cell state that stores long-time memory, Ot is the output gate that determines the output from the cell state, and ht is the final output of the LSTM. Subsequently, a three-layer fully connected network is appended to the final LSTM layer to map the stimulus state vector Ct at every timestep, thereby decoupling the real-time signals of temperature, pressure, and Na⁺ electrolyte concentration. This design enables the AMSG to accurately perceive and differentiate multimodal stimulus responses.

When temperature, pressure, and Na⁺ electrolyte stimuli in their characteristic patterns are simultaneously applied to the AMSG, the resulting compound current signal can be accurately decoupled into distinct components corresponding to each stimulus (Fig. 4d and Supplementary Fig. 37). In particular, over the entire test duration of 3000 time steps (sampling rate: 10 Hz), the LSTM model achieved mean absolute errors below 2% for temperature and pressure sensing. Despite the relatively weak current signals from the Na⁺ electrolyte stimulus, the model maintained an estimation error within 30%.

To further validate its performance, five representative physiological states—relaxing, walking, running, sleeping, and fever—are simulated with controlled variations in body temperature (36–41°C), heart rate (60–130 bpm), and sweat Na+ concentrations (10–90 mmol L−1) (Fig. 4e and Supplementary Movie 1). The output signals closely matched those from commercial sensors, confirming the accuracy and reliability of the AMSG and decoupling model. Integrated with a multimodal monitoring module and a user interface, the system can display real-time variations in the physiological signals. For example, during running, the monitored pulse rate elevated to 133 bpm, while the Na+ concentration accumulated to 90 mmol L−1 due to increased perspiration. During sleep, on the other hand, the pulse rate dropped to 62 bpm, while sweat secretion was negligible with Na+ concentration approaching zero. In addition, these data can be transmitted via cloud connectivity to remote health management systems for continuous monitoring and feedback (Fig. 4f).

Machine control and haptic reproduction
Beyond multimodal physiological monitoring, the AMSG is further integrated into a human-machine interface with closed-loop control and feedback capabilities. As finger and wrist movements activate certain superficial forearm muscles, the resulting muscle contractions generate pressure signals. These subtle signals can be detected by the AMSG with the aid of a woven wristband that applies gentle back pressure to ensure firm skin contact (Supplementary Figs. 38 and 39). Combined with deep learning, the signals associated with finger and wrist motions are decoded into gesture commands for real-time robotic arm control.

As shown in Fig. 5a, the AMSG wristband functions as a self-powered wireless human-machine interface (Supplementary Movie 2). Characteristic current signals are generated by various gestures, such as grasping, flexing, extending, and rotating (Supplementary Fig. 40), and transmitted via Bluetooth to a processing workstation. To extract relevant features and accurately recognize gesture commands, a ResNet-34 deep learning framework based on a one-dimensional CNN is constructed. The framework comprises a convolutional layer, a max-pooling layer, and a fully connected layer for automated feature extraction and classification, translating the user gesture inputs into robotic arm movements. The dataset is randomly divided into a training set (75%) and a test set (25%) for model training and validation. As a result, the AMSG wristband interface achieved an average recognition accuracy of 97.57% across 10 wrist gestures in 200 sign language tests (Fig. 5b). Furthermore, principal component analysis (PCA) was performed to project high-dimensional signal sequences into a 2D feature space for visualizing the model’s classification performance (Fig. 5c). The PCA distribution revealed distinct class boundaries with signals from the same gesture clustering together and those from different gestures well separated. Finally, through the mechanical control module, the user interface can provide real-time visual feedback of gesture commands and corresponding robotic arm actions (Fig. 5d). These capabilities demonstrate significant potential for digital twin applications.

Furthermore, two-way communication between the user and the robotic system is achieved by applying the AMSG at both ends: while the wristband captures user gestures to control robotic motion, the AMSG on the robotic hand can also sense temperature and pressure variations during interaction and relay this information back to the user (Fig. 5e). To complete this feedback loop, a thermal actuator and a haptic feedback module are integrated into the user wristband, combining both sensory input and haptic feedback within a single wearable platform (Supplementary Movies 3 and 4). When the robotic hand grasps an object, the AMSG at its fingertip detects temperature and pressure variations, generating corresponding signal outputs. These signals are then processed by a control module, which linearly maps them to the thermal output of a micro-Peltier device and the vibration frequency of a linear motor (Fig. 5f). As a result, the user can perceive the object’s temperature and contact forces in real time, enabling haptic reproduction without direct physical interaction. The integration of deep learning algorithms and processing modules further enables a range of intelligent features. For example, a thermal safeguard function is built into the AMSG wristband interface. By analyzing current responses as the robotic hand approaches hot or cold surfaces, the interface is able to distinguish between safe and hazardous temperatures. When the surface temperature exceeds 80°C or drops below −20°C, thermal and vibrational warnings can be delivered to alert the user in real time (Fig. 5g).

Figure 5h demonstrates a representative application scenario showcasing the capability of the AMSG wristband interface, including sensing, haptic feedback, as well as safety alert functions. The entire workflow produced a three-stage current profile: (1) non-contact temperature sensing, (2) contact-based accurate temperature sensing, and (3) grip-force sensing (Supplementary Movie 5). As the robotic hand approached a paper cup filled with hot water, the current signal gradually increased to 14 μA. If the detected temperature remained within a safe range, the grasping action would proceed; otherwise, robotic motion would be stopped. Upon contact, the current rose to 30 μA due to the sudden temperature increase. Meanwhile, the thermal actuator at the user end was triggered to reproduce the detected temperature. A similar response can also be achieved for cold objects, enabled by the polarity of the thermoelectric signal. In addition, the safety feature is integrated into the pressure-sensing feedback. For specific objects, an adaptive grasping action generates a piezoelectric signal below a set threshold. By comparing the current amplitude (C) and slope angle (θ) with the set threshold, the interface detects excessive gripping force. If the current amplitude (C) and slope angle (θ) exceed their limits, grasping is stopped to prevent potential damage to the object. In this process, real-time pressure and temperature information is transmitted via Wi-Fi and displayed on the user interface (Fig. 5i). Together, these demonstrations highlight the potential of AMSG as a self-powered, multifunctional human-machine interface.

Methods
Materials
PVA (Mw = 89,000–98,000, 99% hydrolyzed), FeCl3·6H2O (Mw = 270.3, ≥99%), and FeCl2·4H2O (Mw = 198.81, ≥99%) were purchased from Macklin Industries Corporation. Hydrochloric acid (37%), Glycerol (AR, ≥99%), NaCl (Mw = 58.44, Spectral level), and DMSO (Mw = 78.13, ≥99.5%) were purchased from Aladdin Industrial Corporation. All chemicals were used as received. Graphite papers of 0.1 mm thickness were used as electrodes.

Fabrication of freeze-thawed PVA hydrogels
PVA powder was dissolved in deionized water under vigorous stirring at 95°C for 3 h to form a homogeneous solution (15 wt.%). The solution was cast into Teflon molds and then frozen at −20°C for 8 h, followed by thawing at 25°C for 2 h. The freeze-thawing cycle was repeated three times to obtain the FT hydrogels.

Fabrication of GS PVA organogels
PVA powder was dissolved in DMSO under vigorous stirring at 95°C to form a 15 wt.% solution. After degassing via sonication for 2.5 h, the obtained clear solution was cast into 3D-printed molds and frozen at −80°C for 2 h. The frozen gels were then immersed in glycerol at room temperature for 24 h to initiate substitution between DMSO and glycerol, with the glycerol replaced at least twice to ensure thorough exchange. The resulting organogel was designated as GS.

Fabrication of IS PVA hydrogels
Different concentrations of FeCl2/FeCl3 solutions (0.01 M, 0.02 M, 0.05 M, 0.1 M) were prepared by dissolving FeCl2·4H2O and FeCl3·6H2O powder in deionized water, followed by sonication for 10 min to obtain clear solutions. GS PVA hydrogels were then immersed in the ionic solutions for 48 h at room temperature. The resulting hydrogels were designated as IS.

Preparation of porous electrodes
PDMS was spin-coated on graphite paper at 400 rpm for 50 s and then cured at 80°C for 20 min. The coated layer was patterned into porous structures (500 μm diameter, 1 mm spacing) using laser cutting, followed by immersion in 37% hydrochloric acid for 1 h to obtain the final porous electrodes.

Water content measurement
The hydrogel was immersed in 0.05 M FeCl2/FeCl3 solution for 1–30 days. The initial weight and the weight after immersion (ma) were recorded, and the water content was calculated as
(ma - m0) / m0 * 100%

Mechanical test
All mechanical tests were conducted using a universal tensile testing machine at room temperature. For tensile tests, PVA gels were cut into dog-bone-shaped specimens with a gauge width of 3 mm and a thickness of 2 mm. Tensile tests were conducted at a loading rate of 100 mm min−1, and the integrated area under the stress–strain curve was calculated as toughness. Compression tests were carried out at a rate of 50 mm min−1. For each group, at least three independent specimens were tested.

Pure shear test
Hydrogels were cut into rectangular specimens with a gauge length of 40 mm, a width of 3 mm, and a thickness of 2 mm as measured using a caliper. To obtain the fracture energy, two identical samples, one notched and one unnotched, were tested as a pair26. For the notched sample, a 1.5-mm straight cut was introduced at the midpoint of the gauge length. An initial clamp distance of 1 or 2 mm was used for every pair of specimens, which were loaded at a rate of 100 mm min−1. The fracture energy was calculated by:
Γ = U(ΔLc) / A
where A is the cross-sectional area (width multiplied by thickness), ΔLc is the critical elongation upon crack initiation in the notched sample, and U(ΔLc) is the mechanical work done to stretch the unnotched sample to ΔLc.

1H-NMR characterization
Gel samples were first dissolved in DMSO-d6, and the mixtures were analyzed using 1H nuclear magnetic resonance (NMR) spectroscopy (Bruker Plus 600 MHz, Germany).

ATR-FTIR characterization
FTIR spectroscopy coupled with an attenuated total reflectance module (ATR-FTIR, Cary 610/670, Varian, USA) was used to characterize the functional groups of the hydrogels.

XRD characterization
XRD spectra of hydrogels were obtained using an X-ray diffractometer (Rigaku SmartLab SE) with a Cu Kα radiation source with a scanning range from 5° to 90° and a step size of 0.02°.

DSC characterization
The crystallinity of PVA gels was measured by DSC at a heating rate of 20°C min−1 from 50 to 250°C under argon atmosphere at a flow rate of 50 ml min−1. The hydrogel slices were first immersed in a chemical cross-linking agent (consisting of 10 ml of 50 vol% glutaraldehyde, 1 mL of 36.5 to 38 wt.% hydrochloric acid, and 100 ml of ultrapure water) to stabilize the amorphous chains. They were then washed thoroughly with ultrapure water for 2 h to remove excess hydrochloric acid and dried in a blast oven at 40°C for 24 h. The melting enthalpy of the hydrogel was calculated by integrating the area of the melting heat absorption transition peak between 200°C and 250°C. Its ratio to the melting enthalpy of 100% crystallized PVA (138.6 J g−1) was used to determine the degree of crystallinity.

Theoretical calculation of the maximum output power density (Pmax) and normalized power density (Pmax/ΔT^2)
For the theoretical calculation of Pmax:
Pmax = Voc * I / 4
in which
I = ISC / A
where Voc is the open-circuit voltage, I is the current density, ISC is the short-circuit current, and A is the cross-sectional area.
With Pmax, the normalized power density (Pmax/ΔT^2) can be calculated as
Pmax / ΔT^2 = Voc * I / (4 * ΔT^2)
where ΔT is the temperature difference between hot and cold ends.

Ethics oversight
All procedures involving human research participants were conducted in accordance with the experimental protocol approved by the Ethics Committee of Taiyuan University of Technology (TYUT2026012301). All participants were informed with written consent.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study stands out due it its innovative ideas, including achieving high stretchability and low modulus through sequential solvent substitution of the PVA hydrogel, amplifying vertical stress concentration and enhancing piezoionic output by over 100 times through prismatic architecture engineering, and ensuring durable hydrophilicity and improving sweat transport efficiency via hydrochloric acid treatment of the PDMS substrate. The demonstrated self-powered multimodal sensing capability, including thermogalvanic, piezoionic, and diffusion effects, is a significant advancement over existing technologies. Howeverm the completeness of the manuscript would be improved by addressing the following question:\n1. This study mentions using machine learning (LSTM/CNN) for decoupling the sensing capabilities of temperature, pressure, and electrolyte. Does the signal processing and transmission circuitry require external power? If so, this potentially contradicts the paper’s central claim of self-powered multimodal sensing without external power.\n2. The submission of a temporal machine learning model with local attention to decouple multimodal signals is promising. However, despite the successful decoupling methodology, there appears to be a lack of fundamental exploration and quantitative explanation regarding the mutual influence (crosstalk) among the individual sensing modalities (temperature, pressure, and sweat electrolyte)", "reviewer_2": "The manuscript reports a poly(vinyl alcohol)-based hydrogel capable of sensing temperature, pressure, and electrolyte concentration through integrated thermogalvanic, piezoionic, and diffusion mechanisms. The authors demonstrate a machine learning–based signal decoupling model and a wearable wristband prototype with gesture-controlled robotics and haptic feedback. The functions realized are kind of interesting.\nThe material design is well introduced, and the characterization is comprehensive and supported by experimental data.\nStill, the authors should better position their platform relative to existing multifunctional hydrogel e-skins and emphasize how design advances current approaches in performance, functionality, or practical use.\nIn addition, the novelty and significance of the work could be better highlighted. While the multimodal hydrogel shows promising potential for use in flexible electronics and soft robotics, the application-oriented aspect of this study should be further addressed. The following issues should be further clarified:\n1. The introduction discusses only the limitations of single-modal sensors but does not adequately position the work against existing multimodal sensing materials. The authors should also compare their material with current state-of-the-art multimodal hydrogels or e-skins to clarify the specific advantages and novelty of their approach.\n2. In Fig. 2, the denotation of the samples is inconsistent. Some data are labeled as GS, while others as GS×2 (e.g., NMR, DSC). If GS×2 represents the final material at this stage, the sample labels across all characterizations should be unified accordingly. The authors also need to clarify why two glycerol substitution steps were performed.\n3. In Fig. 3j, the sensitivity is fitted using two linear regions with an abrupt turning point. Despite the prism geometry, the hydrogel is a homogeneous material, and this current-pressure relationship would not be expected to exhibit such discontinuity. The authors should justify this model or consider a more appropriate fitting approach.\n4. The current application studies showcase physiological sensing, gesture control, and haptic feedback as rather separate, independent functions. To support the claims of multimodal integration, the authors should consider a practical use-case demonstration that activates multiple functions simultaneously, rather than individual proofs of concept, to better highlight the relevance and application potential of the multimodal platform.\n5. The manuscript contains several technical and formatting errors that require careful correction, including:\nLine 347: “hydrogen e-skin”\nFigure 4d: “m mol L⁻¹” should be “mmol L⁻¹.”\nIn Fig. 3h, labels are not fully visible.\n6. Supplementary results should further include: readout circuits schematics of temperature, pressure, and electrolyte concentration(sweat) sensing. Supplementary video is kind of simple, more demonstration videos should be further enriched by adding 1)pulse monitoring, 2) grasping a paper cup illustrated in fig5.h, 3) a full demonstration of complex robot arm control/manipulation as illustrated in Fig. 5a, in addition, after hand gesture recognition, a notable delay of the robot arm execution could be observed, authors are required to further discuss the reason behind, and how to mitigate the delay, 4) other videos showcase the novelty of this work.", "reviewer_3": "In this manuscript, Bai et al. report a polyvinyl alcohol hydrogel synthesized by a sequential solvent exchange process to achieve a highly soft, tough, and stretchable material. They demonstrate that their PVA hydrogel is responsive to a wide variety of stimulus, including temperature, mechanical, and ionic strength, which could make it an elegant single material solution for multimodal sensing.\nHowever, while the team presented the use of an active multimodal signal generator that employs machine learning to attempt to decouple confounding crosstalk between stimuli, rigorous characterization should also be conducted to understand how the sensor is influenced by various stimuli. Additionally, the supporting demonstrations for their human-machine interface should be extensively reported. The authors should address the following comments before further consideration.\n1. The authors note that “the PVA hydrogel is engineered into a novel prismatic architecture.” This structure does not appear to be novel as in the recent report (Nat Commun 2023, 14, 2907), and other similar pyramidal structures are commonly employed in pressure sensors.\n2. The sequential solvent substitution process does not appear to have “clear advantages” over other methods, such as the solvent-exchanged-assisted wet annealing in their reference (Adv. Mater., 2023, 2210624). Similarly, this work introduced subsequential solvent exchange of PVA hydrogels from DMSO, glycerol, and an aqueous solution. Additionally, by tuning the solvent-exchange wet annealing process the work reported PVA hydrogels with similar properties: 100kPa stiffness, MJ/m3 toughness, 1000% strain.\n3. In figure 2j, the graph comparing properties of PVA hydrogels fabricated with other processes does not appear to be accurate, since solvent-exchanged-assisted wet annealing can similarly produce comparable soft hydrogels with <1MPa stiffness as mentioned.\n4. For clarification, the authors note that “Electrolyte sensing was enabled by the diffusion effect, where temperature gradients led to differential ion diffusion.” Did they mean that ionic gradients lead to ion diffusion?\n5. In figure S11, why is there still a current density when electrode distance = 0mm? Aren’t the electrodes shorted?\n6. Could the authors clarify this calculation: “the maximum output power density (Pmax) was calculated as 19 μW·m-2·K-2,” since this value and units differ from the power density in figure 3d.\n7. In figure 3g, how does the prismatic structure result in 100x sensitivity? Is this for the same load or the same strain?\n8. How does the prismatic structure affect the performance of thermogalvanic and electrolyte sensing compared to cubed structure?\n9. Critically, for simultaneous multimodal sensing, characterizations should be conducted while manipulating one stimulus at a time to observe their influence on the current output measurements. For instance, ionic concentration of NaCl can also influence the sensitivity of thermogalvanic and piezoionic effects.\nIn figure 3d, how is this signal decoupled into the temperature, pressure and Na concentrations? It seems that the compound current raw recordings should simply be a superposition from all the inputs from thermogalvanic, piezoionic, and electrolyte stimuli. The compound signal does not appear to encode any of the high frequency responses in the plot of the current corresponding to pressure below. Likewise, the lower frequency current contributions from the temperature sensing component does not in the compound recording.\nIt seems unclear how different stimuli together are influencing the output. Rather than solely relying on a machine learning to “decouple” these, rigorous characterization should be further investigated.\n10. In figure 5g, does the sensor contact the heat and cold source? Why is there no apparent change in the current recording in response to pressure?\n11. Are there any supporting videos for figure 5f for real-time linear motor control from sensor feedback? Are there additional videos to accompany figure 5h to show responsive robotic hand control according to feedback? Further supporting videos should better demonstrate “closed-loop” functionality."}, "author_response": "Manuscript title: A self-powered hydrogel electronic skin with decoupled\nmultimodal sensing for closed-loop human-machine interactions\nDear Editor and Reviewers:\nWe are sincerely grateful to the editor and reviewers for taking the time to\nmeticulously review our manuscript and provide constructive feedback. Your\ncomments have been instrumental in enhancing the depth and rigor of our study. In\nresponse to the reviewers' valuable suggestions, we have undertaken extensive\nrevisions to our manuscript. These revisions include:\n1. Enhancement of Experimental Data: We have augmented our experimental data to\nprovide a more robust foundation for our findings, ensuring that the results are both\ncomprehensive and reliable.\n2. Clarification of Mechanistic Explanations: We have refined our explanations of\nthe underlying mechanisms to ensure precision and clarity, thereby enhancing the\nreader's understanding of our research.\n3. Supplementation of Demonstration Videos: We have included additional\ndemonstration videos to visually present the closed-loop applications of our multimodal\nsensing and feedback system, thereby providing a more intuitive understanding of the\nintegrated sensing-feedback process.\nWe have presented a comprehensive point-by-point response to all comments. We are\nconfident that these revisions have significantly bolstered the quality of our manuscript.\nWe appreciate the opportunity to revise our work for consideration in Nature\nCommunications. We believe that our manuscript is now better positioned to contribute\nto the scientific discourse in your esteemed journal.\nReviewer #1:\nThis study stands out due it its innovative ideas, including achieving high stretchability\nand low modulus through sequential solvent substitution of the PVA hydrogel,\namplifying vertical stress concentration and enhancing piezoionic output by over\n100 times through prismatic architecture engineering, and ensuring durable hydrophilicity\nand improving sweat transport efficiency via hydrochloric acid treatment of the PDMS\nsubstrate. The demonstrated self-powered multimodal sensing capability, including\nthermogalvanic, piezoionic, and diffusion effects, is a significant advancement over\nexisting technologies. However, the completeness of the manuscript would be\nimproved by addressing the following question:\nResponse: We sincerely appreciate the reviewer’s positive assessment of our work,\nincluding the fabrication strategy, structural design, multifunctionality, and application\ndemonstrations. We have carefully considered all the concerns raised, and our point-\nby-point responses are provided below.\nComment 1.1: This study mentions using machine learning (LSTM/CNN) for\ndecoupling the sensing capabilities of temperature, pressure, and electrolyte. Does the\nsignal processing and transmission circuitry require external power? If so, this\npotentially contradicts the paper’s central claim of self-powered multimodal sensing\nwithout external power.\nResponse: Thanks for your insightful comment. We agree that the original, undefined\ndescription of “self-powered sensing” in the manuscript may cause misunderstanding.\nIn this work, the term “self-powered” refers to the hydrogel sensing unit, which can\nautonomously generate electrical signals through thermogalvanic, piezoionic, and\ndiffusion-driven mechanisms without any external power supply.\nThe AMSG wristband integrates several components, including a PVA-hydrogel-based\nelectronic skin, which functions as the self-powered signal generation unit, signal\nprocessing circuitry, and a wireless transmission module. As shown in Fig. R1, the\nhydrogel produces stimulus-dependent current outputs under temperature, pressure, and\nelectrolyte conditions, confirming that the hydrogel module can spontaneously produce\npotential differences and drive current in external circuits, thereby operating in a fully\nself-powered manner. This definition is consistent with the widely accepted criteria for\nself-powered sensors [Nat. Commun. 15, 530 (2024); Sci. Adv. 10, eadq8989 (2024)].\nThe potential misunderstanding comes from the processing and signal transmission\nmodules. We acknowledge that commercial Wi-Fi chips and front-end signal\nprocessing require external power. At present, commercially available Wi-Fi\ntransmitters typically require a minimum supply voltage of 3.3 V and a current of no\nless than ~300 mA, which exceeds the output capability of current self-powered\nsystems based on thermoelectric, triboelectric, or piezoelectric mechanisms [Adv.\nMater. 37, 2416897 (2025); Adv. Funct. Mater. 33, 202303562 (2023)]. In addition, the\nfinal signal processing and decoupling, along with program control and software, must\nstill be performed on a computer. Even so, our design substantially reduces the external\npower demand compared with conventional resistive and capacitive sensors that require\ncontinuous power for both sensing and communication [for example, Adv. Mater. 33,\n2102069 (2021)].\nLooking forward, we will work to enhance the hydrogel’s self-driven power output;\nalternatively, to the best of our knowledge, ultra-low-power Wi-Fi communication\nchips are currently under development and may become available. By combining\nimproved self-powered conversion efficiency with next-generation low-power\ncommunication modules, a fully self-powered wireless sensing system may be feasible\nsoon.\nFig. R1. Multimodal sensing capabilities. a Schematic illustration of the\nthermogalvanic effect as the temperature sensing mechanism. b Self-powered current\nvariation of PVA hydrogels under different temperature differences. Darker areas\nindicate the presence of a temperature difference. c Schematic of the piezoionic effect\nfor pressure sensing. d Self-powered current variation based on the piezoionic effect\nunder pressure. e Schematic of the diffusion effect for sweat detection. f Current\ngenerated by sodium ions at different concentrations based on ion diffusion.\nTo clarify the definition, we have revised the description on page 3, lines 83-85 of the\nmanuscript: “Notably, the thermogalvanic, piezoionic, and diffusion effects support\nboth sensing and self-powering, enabling the hydrogel sensing unit to actively generate\nstimuli-responsive electrical signals.”.\nComment 1.2: The submission of a temporal machine learning model with local\nattention to decouple multimodal signals is promising. However, despite the successful\ndecoupling methodology, there appears to be a lack of fundamental exploration and\nquantitative explanation regarding the mutual influence (crosstalk) among the\nindividual sensing modalities (temperature, pressure, and sweat electrolyte).\nResponse: Thanks for your insightful comment on the multimodal sensors. We agree\nthat a fundamental and quantitative investigation of the mutual influence among\ndifferent sensing modalities (temperature, pressure, and sweat electrolyte) can further\nstrengthen the understanding of our multimodal hydrogel system.\nThese sensing mechanisms originate from distinct driving forces: the thermogalvanic\neffect arises from temperature-gradient driven redox potential differences, the\npiezoionic effect is triggered by stress-induced ionic polarization, and the diffusion\neffect results from concentration-driven ion migration. According to liquid\nthermoelectric theory, the Seebeck coefficient , where\nis the entropy change of the redox couple, is the number of transferred\n𝑆𝑒 = Δ𝐸/Δ𝑇 = Δ𝑆𝑟𝑐/𝑛𝐹\nelectrons, and is the Faraday constant [J. Mater. Chem. A 10, 19690–19698 (2022)].\n𝛥𝑆𝑟𝑐 𝑛\nWithin the moderate temperature range used in this work, the thermovoltage increases\n𝐹\nproportionally with the temperature difference. In contrast, the pressure-driven\npiezoionic effect reflects transient directional ion migration caused by local\ndeformation of the polymer network. Smaller cations migrate more rapidly through the\npolymer network than larger anions, leading to charge imbalance and the formation of\nan internal electric field. This ion redistribution generates local concentration and\npotential gradients, and the ionic chemical potential gradient scales with the applied\nstress. Similarly, the diffusion-induced signal arises from Na+ transport follows Fick’s\nlaw and scales linearly with the external Na+ concentration gradient, and the\nmicrochannel design ensures stable mass transport without reaching adsorption\nsaturation in the tested range. Consequently, these three mechanisms operate\nindependently at different temporal scales, enabling independent responses within the\nsame material system.\nAlthough these driving forces operate independently, minor interactions may still occur\nas the same ionic species can participate in different processes. To quantify this\ninterference, we conducted additional controlled experiments as follow.\nExperiment 1: Crosstalk between temperature and pressure responses\nTo evaluate the potential crosstalk between temperature and pressure responses, we\nmeasured the piezoionic current under different temperature differences (Fig. R2a and\nR2b). When the hydrogel was fixed at 30 °C without applying a temperature difference,\npressures of 2, 4, 6, and 8 kPa generated ionic piezoelectric currents of 0.8, 1.7, 2.3,\nand 2.7 μA, respectively. A baseline thermoelectric current was generated as ΔT\nincreased. However, the pressure-induced current variations (ΔI) remained almost\nunchanged, indicating that the pressure-driven ionic response is independent of the\nthermal stimuli.\nThis independence could be attributed to the distinct response mechanisms: the\nthermoelectric response originates from temperature-driven redox potentials, involving\nelectron transfer at the electrode interface and requiring several seconds of reaction time.\nIn contrast, the piezoelectric response arises from rapid and mechanically induced local\nionic polarization, thus enabling short-range ion migration within milliseconds.\nExperiment 2: Crosstalk between pressure and sweat electrolyte responses\nAs shown in Fig. R2c and R2d, although increasing Na⁺ concentration produced higher\ndiffusion currents, the pressure-induced ΔI remained essentially constant across all\ntested concentrations. Similarly, this independence can be attributed to the fundamental\ndifferences between the two mechanisms. The diffusion current is a slow and steady-\nstate process driven by concentration gradients, whereas the pressure response, as\ndiscussed before, is a rapid, stress-induced transient ionic polarization generated during\nthe elastic compression of the hydrogel. The two processes occur on distinct timescales\nand follow different transport pathways, and therefore do not interfere with each other.\nAs a result, the pressure-induced piezoionic current remains essentially unaffected by\nNa⁺ concentration.\nFig. R2. a Piezoionic current at different temperature levels, where the cold end\ntemperature is set to 30 °C, while the hot end temperatures are set to 30, 31, 32, and\n33 °C, respectively. The applied pressures are 2, 4, 6, and 8 kPa, respectively. b\nRelationship between pressure and current variation (ΔI) at different temperature levels\n(ΔT). c Piezoionic current under different Na⁺ concentrations, with applied pressures of\n2, 4, 6, and 8 kPa, respectively. d Relationship between pressure and current variation\n(ΔI) under different Na+ concentrations.\nExperiment 3: Crosstalk between temperature and sweat electrolyte responses\nWe further examined the possible crosstalk between temperature and sweat electrolyte\nresponses. As shown in Fig. R3, increasing Na⁺ concentration enhances the\nthermogalvanic current, and larger temperature differences also increase the diffusion-\ndriven current. This coupling originates from the synergy of the Soret effect and the\nthermogalvanic effect [Science 368, 1091–1098 (2020)]. The total ionic flux can be\ndescribed by , where is the thermal diffusion coefficient.\nWhen a temperature difference is applied, Na⁺ ions preferentially migrate toward the\n𝐽 = −𝐷∇𝑐−𝐷𝑇𝑐∇𝑇 𝐷𝑇\ncolder end, generating an additional directed ion flux. At the same time, elevated\ntemperature enhances the diffusion of Na⁺ ions driven by concentration gradients.\nAs a result, within a certain temperature range, the thermogalvanic effect and concentration\ndiffusion act together.\nIn our system, at Na⁺ concentrations of 50, 100, and 150 mM, the thermoelectric\ncurrents induced by a 1 K temperature difference were 0.89, 1.05, and 1.39 μA,\nrespectively. Under a 2 K temperature difference, the corresponding currents increased\nto 1.82, 2.10, and 2.70 μA. Compared with the baseline thermoelectric current of 0.80\nμA in the absence of Na⁺, these results suggest that each 50 mM increase in Na⁺\nconcentration contributes an additional ~0.15 μA·K⁻¹ to the thermoelectric response.\nFig. R3. a Thermoelectric current induced by different temperature differences at\nvarious Na⁺ concentrations, with the hot junction temperature fixed at 30 °C. The cold\njunction temperatures were set to 29, 28, and 26 °C, respectively. b Thermoelectric\nresponse under different Na+ concentrations. Time-dependent diffusion current curves\nduring stepwise absorption of 50μL Na⁺ solutions at different temperature differentials:\n(c) 0 K, (d) 2 K, (e) 4 K. The cold junction temperature was fixed at 30 °C. The hot\njunction temperature was set to 30, 32, and 34 °C, respectively. f Effect of Na+\nconcentration on diffusion current variation (ΔI) under different temperature\ndifferences (ΔT).\nIn summary, the thermoelectric and piezoionic responses, as well as the diffusion and\npiezoionic responses, operate independently due to their distinct driving forces and\ncharacteristic timescales. Although diffusion and thermoelectric signals exhibit a\npredictable linear coupling under temperature gradients, this behavior does not affect\nthe separability of the three physical signals. The linearity ensures that the machine-\nlearning model can reliably decouple and reconstruct each sensing modality.\nThis discussion has been added to page 19 of the manuscript, lines 442–456, as follows:\n“To investigate potential crosstalk between the signals, we applied repeated pressure\nstimuli of 2, 4, 6, and 8 kPa under different temperature gradients (Fig. R2a) and\ndifferent Na+ concentrations (Fig. R2b). The piezoionic current increments remained\nnearly identical in all cases, indicating that the pressure-induced ionic signals were not\naffected by variations in temperature nor Na⁺ concentration. This independence arose\nfrom the distinct physical origins of the signals: the piezoionic current was generated\nby transient, mechanically induced ionic polarization, whereas the thermoelectric and\ndiffusion currents were driven by inherently slow, steady-state processes. A minor\ninteraction was observed between the temperature and Na⁺ concentration signals: a\nlarger temperature gradient produced higher diffusion currents because temperature\nnot only drove the thermogalvanic potential but also triggered the Soret effect, causing\nadditional Na+ migration along the thermal gradient (Fig. R3). Nevertheless, within\nthe temperature range investigated in this work, both temperature- and concentration-\ndriven diffusion remained in the linear regime, which allowed their contributions to\nsuperimpose proportionally and produce a stable, predictable current response.”\nSupplementary Fig. S35 and Fig. S36 include the experimental results corresponding\nto Fig. R2 and Fig. R3.\nReviewer #2:\nThe manuscript reports a poly (vinyl alcohol)-based hydrogel capable of sensing\ntemperature, pressure, and electrolyte concentration through integrated thermogalvanic,\npiezoionic, and diffusion mechanisms. The authors demonstrate a machine learning–\nbased signal decoupling model and a wearable wristband prototype with gesture-\ncontrolled robotics and haptic feedback. The functions realized are kind of interesting.\nThe material design is well introduced, and the characterization is comprehensive and\nsupported by experimental data.\nStill, the authors should better position their platform relative to existing\nmultifunctional hydrogel e-skins and emphasize how design advances current\napproaches in performance, functionality, or practical use.\nIn addition, the novelty and significance of the work could be better highlighted. While\nthe multimodal hydrogel shows promising potential for use in flexible electronics and\nsoft robotics, the application-oriented aspect of this study should be further addressed.\nThe following issues should be further clarified:\nResponse: We sincerely appreciate the reviewer’s positive evaluation of our work.\nYour constructive and insightful suggestions have helped us further refine and improve\nthe manuscript. We have carefully addressed all comments and have revised the\nmanuscript accordingly. The detailed responses and revisions are provided below.\nComment 2.1: The introduction discusses only the limitations of single-modal sensors\nbut does not adequately position the work against existing multimodal sensing materials.\nThe authors should also compare their material with current state-of-the-art multimodal\nhydrogels or e-skins to clarify the specific advantages and novelty of their approach.\nResponse: Thank you for highlighting the important point. To better position our work\nin relation to existing multimodal sensing platforms, we have revised the introduction\nto explicitly compare our material with state-of-the-art multimodal hydrogels and e-\nskins.\nA comparative discussion has been added on Page 3, Lines 59–67. The revised text is\nshown below: “Recent advances in multimodal sensors and electronic skins have\ndemonstrated simultaneous perception of temperature, pressure, strain, and humidity\nby incorporating ionic conductors, conductive polymers, or hybrid nanocomposites\n(Table R1). However, most existing single-component multimodal sensors suffer from\nasynchronous signal acquisition and redundant outputs, causing significant coupling\nerrors and reduced sensing accuracy. They also remain limited in system-level\nintegration, often lacking capabilities of remote operation and independent energy\nsupply. In addition, only a few prototypes have achieved closed-loop perception and\nfeedback, which is essential for practical human-machine interaction.”\nWe also added Table R1 to Table S1 of the Supplementary Information to provide a\nclearer comparison with representative multimodal sensors.\nTable R1. Comparison of Flexible Sensors\nSelf- Multimodal Single- Response\nRefs Wearability\npowered sensing unit time\nStrain: 830%\nThis work √ √ √ Modulus: 300 37 ms\nkPa\n[1] Adv. Mater. 34,\n× √ √ Rigid 100 ms\n2205249 (2022)\n[2] Adv. Funct.\nMater. 34, 2314419 √ × √ Strain:530% 230 ms\n(2024)\n[3] Nat. Commun.\n× √ × Rigid N/A\n16, 5689 (2025)\nStrain: 50%\n[4] Nat. Commun.\n× × × Modulus: 17 12 ms\n16, 591 (2025).\nMPa\nComment 2.2: In Fig. 2, the denotation of the samples is inconsistent. Some data are\nlabeled as GS, while others as GS×2 (e.g., NMR, DSC). If GS×2 represents the final\nmaterial at this stage, the sample labels across all characterizations should be unified\naccordingly. The authors also need to clarify why two glycerol substitution steps were\nperformed.\nResponse: Thank you for your careful review. In the revised manuscript, we have\ncorrected the sample labeling. “GS” now consistently refers to PVA organogel obtained\nafter two successive glycerol substitution processes.\nWe also clarified the rationale for performing two substitution cycles. During the\nglycerol substitution process, we compared the Fourier transform infrared (FTIR)\nspectra of the DMSO–GS PVA organogel obtained after substitution once with those\nof the GS PVA organogel obtained after substitution twice (Fig. R4a). The twice-\nsubstituted GS organogel exhibited a more pronounced redshift and broader O–H\nstretching vibration band, indicating stronger and more uniform hydrogen-bonding\ninteractions. Macroscopically, the DMSO–GS organogel appeared slightly warped due\nto uneven shrinkage caused by incomplete glycerol displacement (Fig. R4b), whereas\nthe twice-substituted organogel showed a regular block-like shape. These observations\nindicate that two substitution cycles are required for thorough solvent exchange and for\nproducing a structurally uniform GS organogel.\nFig. R4. a Fourier transform infrared spectra of the FT PVA Organogels, DMSO-GS\nPVA Organogels and GS Organogels. b Comparison photos of PVA Organogels after\nsingle and double glycerol displacement.\nFig. R4 has been added to the Supplementary Information as Fig. S4. The method\ndescription has been updated as follows: “Hydrogel Fabrication—GS PVA Organogels:\nPVA powder was dissolved in DMSO under vigorous stirring at 95°C to form a 15 wt.%\nsolution. After degassing via sonication for 2.5 h, the obtained clear solution was cast\ninto 3D-printed molds and frozen at -80°C for 2 h. The frozen gels were then immersed\nin glycerol at room temperature for 24 h to initiate substitution between DMSO and\nglycerol, with the glycerol replaced at least twice to ensure thorough exchange. The\nresulting hydrogel was designated as GS.”\nAdditionally, Fig. 2 in the manuscript has been updated as Fig. R5.\nFig. R5. Preparation and material properties of PVA hydrogels through sequential\nsolvent substitution. a Schematic illustration of the fabrication process, including\nfreeze-thawing (FT), glycerol substitution (GS), and ionic substitution (IS), leading to\nprogressively enhanced hydrogel networks. b 1H-NMR spectra of FT, GS, and IS in\nDMSO-d at 295 K. c Tensile stress–strain curves of PVA hydrogels after different\n6\nsolvent substitution treatments, compared to the range of human skin (orange shaded\nregion). d Toughness and stiffness of the hydrogels across different treatments. e\nSchematic of the mechanical enhancement mechanism involving dense crystalline and\nionic crystalline regions. f DSC thermograms of FT, GS, and IS. g XRD profiles of FT,\nGS, and IS. h WAXS patterns showing the crystallinity evolution among FT, GS, and\nIS. i Synchronized and asynchronized 2D-COS maps of FT, GS, and IS, where red and\nblue indicate positive and negative intensity, respectively. j Comparison of hydrogel\ntoughness achieved in this study with that of various reported hydrogels and biological\ntissues.\nComment 2.3: In Fig. 3j, the sensitivity is fitted using two linear regions with an abrupt\nturning point. Despite the prism geometry, the hydrogel is a homogeneous material, and\nthis current-pressure relationship would not be expected to exhibit such discontinuity.\nThe authors should justify this model or consider a more appropriate fitting approach.\nResponse: Thanks for your insightful suggestion. We agree that, given the\nhomogeneous nature of the hydrogel, its current-pressure relationship should be\ncontinuous. Continuous fitting may be more appropriate than fitting the data in separate\nintervals. We adopted a more suitable fitting method referenced in literature [Adv.\nMater. 36, 2406235 (2024)]. The updated fitting yields a continuous current-pressure\ncurve, as shown in Fig. R6.\nFig. R6. Relative current change as a function of applied pressure.\nFig R6 has replaced the original Fig. 3j in the manuscript. The revised description is\nnow on page 14, lines 340–342: “As shown in Fig. 3j, the hydrogel exhibited a\ncontinuous current-pressure response, showing a higher sensitivity of 1.94 kPa⁻¹ at\npressures below 2 MPa and 0.4 kPa⁻¹ above 2 MPa.”\nComment 2.4: The current application studies showcase physiological sensing, gesture\ncontrol, and haptic feedback as rather separate, independent functions. To support the\nclaims of multimodal integration, the authors should consider a practical use-case\ndemonstration that activates multiple functions simultaneously, rather than individual\nproofs of concept, to better highlight the relevance and application potential of the\nmultimodal platform.\nResponse: Thank you very much for this valuable suggestion. To better demonstrate\nthe multimodal integration, we have designed a use-case scenario that combines\nphysiological monitoring, gesture-based robotic control, and haptic feedback in a single\nworkflow (Video 5). Video 5 has been uploaded as one of the supplementary materials.\nIn this demonstration, the robotic hand carries out experimental operations in a\npotentially hazardous environment, while the operator remains in a protected area and\ncontrols the process through a transparent safety enclosure. During the task, the\noperator can perceive both the temperature of the contacted objects and the applied\ngripping force in real time. We believe that this integrated demonstration more clearly\nillustrates the practical relevance of our multimodal platform for future applications in\nsafety-critical and remote-operation environments.\nComment 2.5: The manuscript contains several technical and formatting errors that\nrequire careful correction, including:\nLine 347: “hydrogen e-skin”\nFigure 4d: “m mol L⁻¹” should be “mmol L⁻¹.”\nIn Fig. 3h, labels are not fully visible.\nResponse: Thank you for your careful review of the images and text. We have corrected\nall noted technical and formatting issues, and conducted a thorough re-examination of\nthe manuscript.\nSpecifically, Page 15, line 359: “hydrogen e-skin” has been revised to “hydrogel e-\nskin”, all instances of “m mol L-1” have been standardized to “mmol L-1”, including:\nPage 21, line 506: “10–90 mmol L-1” Page 21, line 511: “90 mmol L-1”. Additionally,\nFig. 4 has been updated as Fig. R7, and the visibility issue in Fig. 3h has been resolved\nand replaced with Fig. R8.\nFig. R7. Multimodal physiological sensing decoupled via a machine learning model.\na Schematic illustration of the AMSG wristband interface for multimodal sensing of\nbody temperature, pulse rate, and sweat secretion, simulated by individually applying\ntemperature, pressure, and Na+ electrolyte stimuli to obtain their characteristic signals.\nb Architecture of the machine learning-based signal decoupling model, featuring three\nLSTM layers with local attention and three fully connected layers for extracting and\ndecoupling the signal features of each stimulus independently. c Internal structure of\nthe LSTM network. d Comparison of compound signals under multiple simultaneous\nstimuli and their decoupled components. e Multimodal monitoring of body temperature,\npulse rate, and sweat Na+ concentration across various activity states (relax, walk,\nrun, sleep, fever), benchmarked against commercial devices. f User interface displaying the\nreal-time waveforms of multimodal signals and corresponding monitoring outputs.\nFig. R8. COMSOL simulation of ion concentration gradients and potential distributions\nunder pressure.\nComment 2.6: Supplementary results should further include: readout circuits\nschematics of temperature, pressure, and electrolyte concentration(sweat) sensing.\nSupplementary video is kind of simple, more demonstration videos should be further\nenriched by adding 1)pulse monitoring, 2) grasping a paper cup illustrated in fig5.h, 3)\na full demonstration of complex robot arm control/manipulation as illustrated in Fig.\n5a, in addition, after hand gesture recognition, a notable delay of the robot arm\nexecution could be observed, authors are required to further discuss the reason behind,\nand how to mitigate the delay, 4) other videos showcase the novelty of this work.\nResponse: Thank you very much for your thoughtful comments. We have added the\nreadout circuit schematics for temperature, pressure, and electrolyte (sweat) sensors,\nprovided more demonstration videos, and addressed the robot arm latency issue through\ncorresponding discussion and optimization.\nAs shown in Figure R9, the signal processing and transmission module consists of a\nsensing unit and a sampling processing unit. The sensing unit converts physical stimuli\ninto electrical signals, which are then amplified by a preamplifier, passed through a\nbandpass filter to remove noise, and subsequently digitized via a 24-bit analog-to-\ndigital converter. The digital signal is routed through a channel selection module to the\nmicrocontroller unit (MCU). Acting as the core controller, the MCU coordinates\nperipheral devices via I²C bus and I/O control, and communicates with the Wi-Fi\nmodule through a UART interface for wireless data transmission. The circuitry for each\nmodule is illustrated in Figure R10.\nFig. R9. Architecture of the multimodal sensing and feedback system, including signal\nprocessing, transmission, and power management.\nFig. R10. Circuit Design of Wireless Current Acquisition Module. a Control and\nData Processing Module. b Wireless Transmission Module. c Data Acquisition Module.\nd Power Supply and Reset Module.\nWe also sincerely appreciate your careful examination of our supplementary video. The\nlatency issue between gesture recognition and robotic arm execution primarily\noriginated from the complex model design and the low data processing efficiency of\nthe terminal GPU. Specifically, our original workflow involved segmenting one-\ndimensional signals, rendering them into images using a graphics library for storage,\nand then reloading them for CNN recognition.\nTo reduce end-to-end latency, we redesigned the workflow from “image rendering and\nCNN classification” to “segmented one-dimensional sequence and direct machine\nlearning recognition”. This modification preserves the recognition accuracy while\nsignificantly shortening both preprocessing and inference times. We also optimized the\nterminal GPU to achieve faster processing capabilities.\nIn addition, we have added Supplementary Videos V1–V5 to provide a more\ncomprehensive demonstration of AMSG’s functionality. Specifically:\nV1-AMSG for Multimodal Signal Sensing. Demonstrates the capability of\nAMSG to monitor human pulse, body temperature, and Na+ levels in sweat.\nV2-AMSG for Robotic Arm Control. Shows how the AMSG senses gesture-\ninduced motion signals and recognizes them via machine learning to control robotic\narm movements.\nV3-AMSG for Temperature and Pressure Sensing. Illustrates that the AMSG\nmounted on the robotic arm can sense the object’s temperature and contacted pressure\nduring approaching, contacting, and grasping.\nV4-Temperature and Pressure Reproduction at the Fingertips of Robotic\nArms. Demonstrates the intuitive feedback of different temperatures and grip forces\nduring the grasping process, where the temperature change at the wristband is provided\nby a miniature Peltier element, and the vibration is generated by a linear vibration motor.\nV5-A Closed-loop Human-machine Interaction Demonstration of AMSG for\nSensing, Control, and Feedback. Presents a remote-operation scenario involving\nhazardous chemicals handling, integrating physiological data monitoring of the\nexperimenter, robotic-arm manipulation, and real-time feedback of temperature and\ngrip force.\nWe believe these supplementary videos provide a clearer and more intuitive\ndemonstration of the integrated closed-loop system that encompasses multimodal\nsensing, mechanical control, and haptic reproduction.\nReviewer #3:\nIn this manuscript, Bai et al. report a polyvinyl alcohol hydrogel synthesized by a\nsequential solvent exchange process to achieve a highly soft, tough, and stretchable\nmaterial. They demonstrate that their PVA hydrogel is responsive to a wide variety of\nstimulus, including temperature, mechanical, and ionic strength, which could make it\nan elegant single material solution for multimodal sensing. However, while the team\npresented the use of an active multimodal signal generator that employs machine\nlearning to attempt to decouple confounding crosstalk between stimuli, rigorous\ncharacterization should also be conducted to understand how the sensor is influenced\nby various stimuli. Additionally, the supporting demonstrations for their human-\nmachine interface should be extensively reported. The authors should address the\nfollowing comments before further consideration.\nResponse: We sincerely appreciate the reviewer's careful evaluation of our manuscript.\nIn response to your valuable suggestions regarding the influence of different stimuli on\nsensor performance and the demonstrations of the human–machine interface, we have\nprovided point-by-point clarifications and added the necessary supplementary results,\nas detailed below.\nComment 3.1: The authors note that “the PVA hydrogel is engineered into a novel\nprismatic architecture.” This structure does not appear to be novel as in the recent report\n(Nat Commun 2023, 14, 2907), and other similar pyramidal structures are commonly\nemployed in pressure sensors.\nResponse: We sincerely appreciate the reviewer's thoughtful comment. Prismatic\narchitecture have indeed been explored in serval papers on sensor design. As you have\npointed out, in the work by Yang et al., gradient pyramid electrodes were developed to\nbroaden the linear sensing range and enhance the sensitivity through a capacitive\nmechanism [Nat. Commun. 14, 2907 (2023)]. However, we did not intend to claim\nnovelty for the prismatic structure itself; instead, our contribution lies in employing this\nconfiguration to amplify vertical stress concentration and enable a d -mode piezoionic\n33\nresponse.\nPrevious studies have shown the promise of piezoionic hydrogels for self-powered\nmechanoreception. Tuta et al. proposed the conversion of pressure into ionic current\nvia the piezoionic effect to build a hydrogel-based artificial skin [Science 376, 502–507\n(2022)]. Zhu et al. further demonstrated that stress concentration between rigid phases\ncan promote charge separation, while the high ionic mobility in the soft and\nintermediate phases facilitates ionic transport [Adv. Mater. 36, 2313127 (2024)].\nAlthough these studies highlight the potential of piezoionic hydrogels, the intrinsic\ncharacteristic of the piezoionic effect is that when the hydrogel is deformed in-plane,\npressure induces lateral ion migration, generating a charge imbalance primarily along\nthe transverse direction, which corresponds to the d mode (Fig. R11a). Under vertical\n31\ncompression, the cubic hydrogel exhibits minimal stress concentration and weak\ndirectional convection, resulting in a limited d response (Fig. R11b). [Adv. Funct.\nits apex, enhancing convection along the loading direction and thereby significantly\nimproving the d -mode ionic output.\n31 33\nAs a self-powered multimodal sensing device intended for skin attachment, both the\nthermogalvanic and diffusion effects naturally operate in the d mode (Fig. R12). In\n33\ncontrast, the piezoionic effect is limited by the weak d mode response under a cubic\n33\nconfiguration.\nFig. R12. Schematic illustrations of the thermogalvanic, diffusion, and piezoionic\neffects showing a transition from a mixed d31 and d33 configuration in (a) to a\nsynergized d33 mode enabled by the prismatic architecture in (b).\nTo address this, we designed the prismatic architecture to significantly amplify the\nvertical stress concentration within the hydrogel (Fig. R13), thereby activating the d\n33\nmode of the piezoionic effect and increasing the output current by over 100 times. This\ndesign enables the integrated acquisition of body temperature, pulse, and electrolyte\nsignals within a single hydrogel system.\nFig. R13. COMSOL simulation of stress distributions in PVA hydrogels at increasing\ndeformation.\nWe have added Fig. R11 to the Supplementary Information as Fig. S1.\nComment 3.2: The sequential solvent substitution process does not appear to have\n“clear advantages” over other methods, such as the solvent-exchanged-assisted wet\nannealing in their reference (Adv. Mater., 2023, 2210624). Similarly, this work\nintroduced subsequential solvent exchange of PVA hydrogels from DMSO, glycerol,\nand an aqueous solution. Additionally, by tuning the solvent-exchange wet annealing\nprocess the work reported PVA hydrogels with similar properties: 100 kPa stiffness,\nMJ/m3 toughness, 1000% strain.\nResponse: We sincerely thank the reviewer for the professional and important question.\nWe agree that “solvent-exchanged-assisted wet annealing” [Adv. Mater., 2023,\n2210624] is an important and influential work, offering broad tunability of hydrogels\nto achieve high stiffness, high toughness, and large strain capability. As noted in their\nwork, freeze–thaw hydrogels exhibit stiffness around 100 kPa but limited stretchability.\nIn contrast, wet-annealed samples can achieve strains exceeding 1000%, though\naccompanied by increased rigidity and reduced water content. Therefore, our work aims\nfor a targeted optimization, simultaneously considering properties including stiffness,\nwater content, stretchability, and strength. Our objective is not to maximize mechanical\nstrength alone. Instead, we aim to reduce rigidity while maintaining excellent tensile\nproperties, yielding a mechanical profile more suitable for flexible electronic skin\napplications (Fig. R14).\nFig. R14. Tensile stress–strain curves of PVA hydrogels after different solvent\nsubstitution treatments, compared to the range of human skin (orange shaded region).\nMore importantly, we aim to maintain high mechanical robustness while also achieving\nionic conductivity and self-powered sensing, which imposes constraints distinct from\nmethods focused purely on mechanical reinforcement. Efficient ionic transport and\nelectrochemical activity require sufficient hydration and flexible polymer networks.\nIn wet-annealed hydrogels, increased crystallinity improves stiffness but can suppress\nor disrupt ionic pathways, thereby reducing conductivity and weakening signal\nsensitivity. Excessive crystallization also diminishes polymer-network mobility,\nmaking it difficult to support thermogalvanic, piezoionic, and diffusion-driven signal\ngeneration.\nIn contrast, our stepwise solvent-exchange strategy enables moderate crystallization for\nmechanical reinforcement while maintaining continuous hydrated ionic channels,\nachieving a balance between structural integrity and ion transport. For self-powered\nperformance, the thermoelectric output relies on ionic diffusion and convective\ntransport in the aqueous phase. Wet annealing unavoidably reduces the water content\nto ~55%–80%, which strongly impedes ion migration and suppressing thermoelectric\noutput. Our dehydration experiments further verify that when the water content drops\nbelow ~80%, the Seebeck coefficient decreases significantly (Fig. R15), demonstrating\nthat high hydration is essential for sustaining thermoelectric performance. Therefore,\nthe solvent-exchange strategy adopted in this work helps retain high-water content to\nensure a strong thermoelectric driving force.\nIn summary, the sequential solvent-exchange approach provides an effective balance\namong high hydration, ionic conductivity, and structural stability, enabling synergistic\noptimization of mechanical properties, transport performance, and energy-conversion\nefficiency to meet the demands of multifunctional integration.\nFig. R15. a The Seebeck coefficient at increasing Fe2+/3+ concentrations. b Seebeck\ncoefficient varying with moisture content.\nWe have revised lines 266–268 on page 12 of the manuscript as follows: “The high\nwater content of the hydrogel also facilitated the thermogalvanic effect, as would\nrapidly drop at lower water content (Fig. R15b).” We have added Fig. R15 to the\n𝑆𝑒\nSupplementary Information as Fig. S11.\nComment 3.3: In figure 2j, the graph comparing properties of PVA hydrogels\nfabricated with other processes does not appear to be accurate, since solvent-\nexchanged-assisted wet annealing can similarly produce comparable soft hydrogels\nwith <1MPa stiffness as mentioned.\nResponse: We sincerely thank the reviewer for the professional comment. In the\noriginal version, we inadvertently used the hydrogel sample annealed at 120 °C as a\ngeneral representation of the wet-annealed condition, which led to an inaccurate\ncomparison. We have now corrected this in the revised manuscript, and Fig. R16 has\nreplaced the original Fig. 2j accordingly.\nFig. R16. Comparison of hydrogel toughness achieved in this study with that of various\nreported hydrogels and biological tissues.\nComment 3.4: For clarification, the authors note that “Electrolyte sensing was enabled\nby the diffusion effect, where temperature gradients led to differential ion diffusion.”\nDid they mean that ionic gradients lead to ion diffusion?\nResponse: Thanks for pointing out this issue. We acknowledge that our previous\nwording was inaccurate. What we intended to convey is that the diffusion effect is\nprimarily driven by the ion concentration gradient, which serves as the dominant force\ncausing ions to migrate from regions of high concentration to low concentration.\nFurthermore, the temperature gradients induce the Soret effect, witch further enhances\nion diffusion.\nSpecifically, when a temperature difference exists across the hydrogel, ions experience\nan additional thermodiffusion driving force, resulting in a larger ion flux than that\nproduced by concentration-driven diffusion alone. To validate this, we compared\ndiffusion currents under different temperature gradients and Na+ concentrations (Fig.\nR17). The results show that when increased from 0 K to 2 K and 4 K, the measured\ndiffusion current increased correspondingly under the same Na+ concentration. This\nΔ𝑇\nconfirms that the concentration gradient is the dominant driver while the Soret effect\nfurther acts as an auxiliary enhancement.\nFig. R17. Time-dependent diffusion current curves during stepwise absorption of 50μL\nNa⁺ solutions at different temperature differentials: (a) 0 K, (b) 2 K, (c) 4 K. The cold\njunction temperature was fixed at 30 °C. The hot junction temperature was set to 30,\n32, and 34 °C, respectively.\nTo avoid misunderstandings, we have revised lines 246–250 on page 11 of the\nmanuscript as follows: “Electrolyte sensing was enabled by the diffusion effect, where\nion concentration gradients led to differential ion diffusion. As these mechanisms\noperated through distinct ion-driven processes, they could function independently in\nthe same system with minimal interference, allowing for synergistic multimodal\nsensing.”\nWe also revised lines 385–387 on page 16 as follows: “In addition, the ionic diffusion\nwas further enhanced by the Soret effect, which arose from the temperature gradient\nbetween the skin and the ambient environment and led to differential ion mobility.”.\nComment 3.5: In figure S11, why is there still a current density when electrode\ndistance = 0mm? Aren’t the electrodes shorted?\nResponse: Thank you for pointing out these issues. We realize that the annotation in\nour original manuscript may have been misleading. The on the horizontal axis in\nFig. R18 actually represents the distance between the sensing electrode and the heat\n𝛥𝑑\nsource, which was used to evaluate the non-contact temperature-sensing capability.\nTherefore, mm indicates direct contact between the heat source and sensing\nunit, rather than a zero distance between the two electrodes. Meanwhile, the hot and\n𝛥𝑑 = 0\ncold electrodes remain fixed at opposite ends of the PVA hydrogel and do not touch\neach other, ensuring no short circuit occurs.\nFig. R18. Non-contact Temperature Sensing Test. a Current density changes as the\ndistance between the hydrogel e-skin and the heat source decreases from 10 mm to full\ncontact (0 mm). d is the distance from the heat source to the hot end electrode of the\ngel. b. The current magnitude depends on the temperature difference between the hot\nand cold ends. At smaller distances, heat radiation raises the cold-end temperature,\nreducing the temperature difference and lowering the output current.\nWe have clarified the definitions of in the supporting materials and added Fig. R18\nas Fig. S13. The description on page 12, lines 293–296 has been revised to: “Since the\n𝛥𝑑\ndistance from the heat source could also affect output, the highest current density was\nfound at a 3-mm distance, with lower performance observed at both closer and further\ndistances due to a reduced temperature gradient (Fig. R18).”\nComment 3.6: Could the authors clarify this calculation: “the maximum output power\ndensity (Pmax) was calculated as 19 μW·m-2·K-2,” since this value and units differ from\nthe power density in figure 3d.\nResponse: Thank you for pointing out this issue. The discrepancy in units arose\nbecause the original manuscript did not clearly distinguish between the calculation\nmethod for power density and normalized power density, which may cause confusion\nfor readers. Both calculations are used to evaluate power density, with the former\nproviding the actual power per unit area and the latter offering a normalized value that\nremoves temperature dependence for comparative purposes. In the revised manuscript,\nwe have clearly distinguished the two quantities and added the theoretical formulas for\ncalculating the maximum output power density ( ) and the normalized power density\nmax\n(Fig. R19). The revision is as follows:\nmax\n𝑃\n−2\n(“𝑃The oΔr𝑇etica)l calculation of the maximum output power density ( ) and the\nnormalized power density ( ): 𝑷𝒎𝒂𝒙\n−𝟐\nFor the theoretical calculati\n𝑷\no𝒎n 𝒂o𝒙f\n𝜟\nP\nm𝑻ax\n:\n𝑉𝑜𝑐 ×𝐼\nin which 𝑃𝑚𝑎𝑥 = 𝑅(1)\n4\n𝐼𝑆𝐶\nwhere is the open-circuit voltage, 𝐼 =is the current density, is the short-ci𝑅rc(u2i)t\n𝐴\ncurrent 𝑉 , 𝑜a𝑐nd is the cross-sectiona l a 𝐼 rea. 𝐼𝑆𝐶\n𝐴\nWith P , the normalized power density can be calculated as\nmax\n−2\n(𝑃𝑚𝑎𝑥 𝛥𝑇 )\n𝑃𝑚𝑎𝑥 𝑉𝑜𝑐 ×𝐼\nwhere is the temperature differen2ce= between h2o t and cold ends.” 𝑅(3)\n𝛥𝑇 4×𝛥𝑇\nR(1), R(2), R(3) are replaced with (3), (4), (5) in the supporting information\n𝛥𝑇\nrespectively.\nFig. R19. Output voltage-current curves and corresponding power densities under\nvarious temperature gradients.\nThe revised text is detailed on page 12, lines 290–292: “Based on these measurements,\nthe maximum output power density ( ) was calculated as 11 mW·m-2, with the\nnormalized power density ( ) reaching 19 μW·m-2·K-2.”\n𝑃𝑚𝑎𝑥\n−2\n𝑃𝑚𝑎𝑥 𝛥𝑇\nComment 3.7: In figure 3g, how does the prismatic structure result in 100x sensitivity?\nIs this for the same load or the same strain?\nResponse: Thank you for raising this question. The 100-fold increase in output current\nshown in Fig. 3g was obtained under identical load conditions, and the enhancement\narises directly from the prismatic structural design.\nSpecifically, the piezoionic effect originates from ion redistribution during deformation.\nIn our hydrogel system, the relatively large anions experience stronger steric hindrance\nfrom the polymer network, resulting in localized ionic accumulation during mechanical\ndeformation. Due to stress-induced asymmetric ionic mobility, anions and cations\nmigrate at different rates, forming a transient ionic concentration gradient and an\ninternal electric field.\nTo exploit this effect, we introduced a prismatic architecture to amplify vertical stress\nconcentration. Under compression, the anisotropic geometry promotes a larger mobility\ndifference between cations and anions along the loading direction, establishing a\nstronger ionic gradient and a higher piezoionic current. We compared cubic and\nprismatic hydrogels with identical cross-sectional areas under the same applied load\n(30 N) (Fig. R20). The prismatic hydrogel produced a peak piezoionic current\nexceeding 80 μA, whereas the cubic hydrogel generated less than 1 μA, demonstrating\nthe significant amplification enabled by the prismatic design.\nFig. R20. For hydrogel structures with cubic and prismatic shapes having identical\ncross-sectional areas, the time-dependent relationship of the piezoionic current under\nidentical load of 30N.\nWe have revised lines 317-321 on page 13 of the manuscript as follows: “Further\nleveraging this, the prismatic structure design was introduced to amplify the vertical\nstress concentration, generating an electric field along the force direction (d33 mode).\nCompared to the cubic structure, this design achieved over a hundredfold increase in\noutput current under identical stress conditions (Fig. 3g and Fig. R20).”. Fig. R20 has\nbeen added to Fig. S20 in the Supplementary Information.\nComment 3.8: How does the prismatic structure affect the performance of\nthermogalvanic and electrolyte sensing compared to cubed structure?\nResponse: Thank you for the thorough review and valuable comments. To more\ncomprehensively evaluate the effects of geometric design, we compared the\nthermogalvanic and electrolyte-sensing performance of cubic and prismatic hydrogel\nunits with the same base areas and heights. The results are summarized below:\nThermogalvanic sensing:\nThe prismatic structure has a smaller effective contact area at the top surface, which\nreduces the active electrode–electrolyte interface. Since thermoelectric conversion\nrelies on Fe3+/Fe2+ redox kinetics at this interface, the reduced contact area lowers the\nelectron-exchange rate and increases the equivalent impedance, leading to a slightly\nlower output current compared to the cubic structure. Nevertheless, both geometries\nexhibited stable and nearly linear thermoelectric response (Fig. R21), indicating that\nthe prismatic geometry affects only the output amplitude of the thermoelectric current\nwithout altering the intrinsic thermodynamic mechanism.\nFig. R21. Thermoelectric current comparison between prismatic (a) and cubic (b)\nstructures. c Thermoelectric current against temperature difference in prismatic and\ncubic structures. The cold-end temperature is fixed at 30 °C.\nElectrolyte sensing:\nElectrolyte sensing is primarily driven by Na+ concentration-gradient-induced diffusion,\ngoverned by chemical potential gradients and bulk ionic transport pathways. Unlike\nthermoelectric conversion, Na+ transport occurs mainly within the hydrogel matrix\nrather than at the electrode interface. Therefore, the macroscopic geometric differences\ndo not significantly affect the ionic transport pathways and solvation environments\n[Chem. Sci. 12, 12874-12910 (2021)]. As a result, both geometries produce nearly\nidentical diffusion currents under passive sensing conditions, demonstrating that\ndiffusion-based electrolyte sensing is largely insensitive to geometric topology (Fig.\nR22).\nFig. R22. Diffusion current comparison Between Prismatic (a) and Cubic (b) Structures.\nc Diffusion current against Na+ concentration in prismatic and cubic structures.\nHydrogel temperature maintained at 30 °C.\nIn addition, Fig. R21 has been added in the Supplementary Information as\nSupplementary Fig. S19. Page 13, lines 306–309 of the manuscript are supplemented\nas follows: “Although the final prismatic structure could slightly reduce thermoelectric\ncurrents compared with simple cubic blocks—due to the smaller electrode contact area\nat the apexes and higher overall resistance from the reduced volume—it still\nmaintained a robust linear response (Fig. R21).”\nFig. R22 has been added in the Supplementary Information as Fig. S34. Page 16, lines\n394–396 of the manuscript are supplemented as follows: “Compared with cubic shapes,\nthe prismatic structure showed no significant difference in ion diffusion currents, as its\nion transport distance remained similar to that in solvated environments (Fig. R22).”\nComment 3.9: Critically, for simultaneous multimodal sensing, characterizations\nshould be conducted while manipulating one stimulus at a time to observe their\ninfluence on the current output measurements. For instance, ionic concentration of\nNaCl can also influence the sensitivity of thermogalvanic and piezoionic effects.\nIn figure 3d, how is this signal decoupled into the temperature, pressure and Na\nconcentrations? It seems that the compound current raw recordings should simply be a\nsuperposition from all the inputs from thermogalvanic, piezoionic, and electrolyte\nstimuli. The compound signal does not appear to encode any of the high frequency\nresponses in the plot of the current corresponding to pressure below. Likewise, the\nlower frequency current contributions from the temperature sensing component does\nnot in the compound recording.\nIt seems unclear how different stimuli together are influencing the output. Rather than\nsolely relying on a machine learning to “decouple” these, rigorous characterization\nshould be further investigated.\nResponse: Thanks for your insightful comment on the durability of our multimodal\nsensors. We agree that using controlled-variable experiments to examine how\nindividual stimuli affect the current output is crucial for deconstructing how these\nsignals collectively influence the overall response mechanism. Therefore, we first\nperformed a series of controlled experiments to quantify the effect of each stimulus on\nthe output current and to identify any potential interference among the different sensing\nmodalities.\nAccording to liquid thermoelectric theory, the Seebeck coefficient\n, where is the entropy change of the redox couple, is the electron-\n𝑆𝑒 = Δ𝐸/Δ𝑇 = Δ𝑆𝑟𝑐/𝑛𝐹 Δ𝑆𝑟𝑐 𝑛\ntransfer number, and is the Faraday constant [J. Mater. Chem. A 10, 19690–19698\n(2022)]. Within the moderate temperature range used in this study, the thermovoltage\n𝐹\nincreases proportionally with the temperature difference. In contrast, the pressure-\ndriven piezoionic response originates from transient directional ion migration caused\nby local network deformation. Smaller cations move more rapidly through the polymer\nmatrix than larger anions, resulting in charge imbalance, local concentration and\npotential gradients, and a chemical potential gradient that scales with the applied stress.\nThe diffusion-induced signal follows Fick’s law and varies linearly with the Na⁺\nconcentration gradient, while the microchannel design ensures stable transport without\nreaching adsorption saturation. These three mechanisms therefore operate\nindependently at distinct spatial and temporal scales, producing characteristic current\nprofiles that provide the basis for subsequent signal decoupling.\nManipulating pressure signals under varying temperature differences:\nThe piezoionic current was measured under various temperature differences while\nfixing the cold end at 30 °C (Fig. R23a, b). Without a temperature gradient, pressures\nof 2–8 kPa generated currents of 0.8–2.7 μA. As increased, a baseline\nthermoelectric current appeared; however, the pressure-induced increments\nΔ𝑇\nremained nearly unchanged. This independence arises from different transport\npathways: thermogalvanic signals emerge from temperature-driven redox reactions\nΔ𝐼\noccurring over seconds, whereas piezoionic currents result from rapid, localized ionic\npolarization within milliseconds.\nManipulating pressure signals under varying electrolyte concentrations:\nSimilarly, increasing Na⁺ concentration increased the diffusion-driven current, but the\npressure-induced remained almost identical at all tested concentrations (Fig. R23c,\nd). Diffusion is a slow, steady-state process governed by concentration gradients,\nΔ𝐼\nwhereas the piezoionic effect derives from transient stress-induced polarization.\nBecause they occur on distinct timescales and through different transport pathways, the\npressure response is not affected by Na⁺ concentration.\nFig. R23. a Piezoionic current at different temperature levels, where the cold end\ntemperature is set to 30 °C, while the hot end temperatures are set to 30, 31, 32, and\n33 °C, respectively. The applied pressures are 2, 4, 6, and 8 kPa, respectively. b\nRelationship between pressure and current variation (ΔI) at different temperature levels\n(ΔT). c Piezoionic current under different Na⁺ concentrations, with applied pressures of\n2, 4, 6, and 8 kPa, respectively. d Relationship between pressure and current variation\n(ΔI) under different Na+ concentrations.\nManipulating temperature differences signals under varying electrolyte\nconcentrations:\nIncreasing Na⁺ concentration enhanced the thermogalvanic current, while larger\ntemperature differences increased the diffusion-driven current (Fig. R24). This\ncoupling stems from the synergistic action of the Soret effect and the thermogalvanic\neffect [Science 368, 1091–1098 (2020)], which can be described by\nTemperature gradients drive Na⁺ ions toward the colder end, adding a\n𝐽 = −𝐷∇𝑐−\nthermodiffusion component to the overall flux. At the same time, elevated temperature\n𝐷𝑇𝑐∇𝑇.\naccelerates concentration-driven diffusion. Within the tested temperature range, both\ncontributions remain linear and superimpose additively. For example, at Na⁺\nconcentrations of 50, 100, and 150 mM, the thermoelectric currents under a 1 K\ntemperature difference were 0.89, 1.05, and 1.39 μA; under 2 K they increased to 1.82,\n2.10, and 2.70 μA. These results suggest an additional ~0.15 μA·K⁻¹ per 50 mM Na⁺,\nconsistent with the measured 4.5 μA thermoelectric current at 100 mM and 4 K.\nFig. R24. a Thermoelectric current induced by different temperature differences at\nvarious Na⁺ concentrations, with the hot junction temperature fixed at 30 °C. The cold\njunction temperatures were set to 29, 28, and 26 °C, respectively. b Thermoelectric\nresponse under different Na+ concentrations. Time-dependent diffusion current curves\nduring stepwise absorption of 50μL Na⁺ solutions at different temperature differentials:\n(c) 0 K, (d) 2 K, (e) 4 K. The cold junction temperature was fixed at 30 °C. The hot\njunction temperature was set to 30, 32, and 34 °C, respectively. f Effect of Na+\nconcentration on diffusion current variation (ΔI) under different temperature\ndifferences (ΔT).\nThis discussion has been added to page 19 of the manuscript, lines 442–456, as follows:\n“To investigate potential crosstalk between the signals, we applied repeated pressure\nstimuli of 2, 4, 6, and 8 kPa under different temperature gradients (Fig. S35a) and\ndifferent Na+ concentrations (Fig. S35b). The piezoionic current increments remained\nnearly identical in all cases, indicating that the pressure-induced ionic signals were not\naffected by variations in temperature nor Na⁺ concentration. This independence arose\nfrom the distinct physical origins of the signals: the piezoionic current was generated\nby transient, mechanically induced ionic polarization, whereas the thermoelectric and\ndiffusion currents were driven by inherently slow, steady-state processes. A minor\ninteraction was observed between the temperature and Na⁺ concentration signals: a\nlarger temperature gradient produced higher diffusion currents because temperature\nnot only drove the thermogalvanic potential but also triggered the Soret effect, causing\nadditional Na+ migration along the thermal gradient (Fig. S36). Nevertheless, within\nthe temperature range investigated in this work, both temperature- and concentration-\ndriven diffusion remained in the linear regime, which allowed their contributions to\nsuperimpose proportionally and produce a stable, predictable current response.”\nFig. R23 have been added in the Supplementary Information as Fig. S35. Fig. R24 have\nbeen added in the Supplementary Information as Fig. S36.\nRegarding the reviewer’s question about Fig. 3d, we agree that the composite signal\nrepresents a superposition of thermoelectric, piezoionic, and electrolyte-stimulated\nsignals. Because the three stimuli operate through independent transport pathways and\nexhibit distinct temporal signatures—sharp spikes for pressure, step-like plateaus for\ntemperature, and slow drifts for Na⁺ diffusion—the composite waveform retains\nidentifiable features from each stimulus. Also as confirmed by our control experiments,\nthe piezoionic response shows negligible interference with both diffusion and\nthermoelectric currents. Likewise, the diffusion and thermoelectric currents exhibit\ndistinct different magnitudes, and the influence of Na⁺ concentration under various\ntemperature gradients remains linearly correlated with the temperature difference.\nTogether, these characteristics minimize mutual interference and allow the coupled\ncurrent waveform to be interpreted as a superimposed signal containing three\nindependent modal contributions.\nTo further demonstrate the signal decoupling capability of our system, we conducted\nadditional experiments using smaller stimulus intensities, applying pressures of 0–3 N,\ntemperature differences of 0–4 °C, and Na⁺ concentrations of 0–100 mM (Fig. R25).\nStimuli were applied sequentially in the order of Na⁺, temperature, and pressure.\nThe resulting composite current exhibited distinct features corresponding to each stimulus:\nsharp transient spikes under pressure, stepwise variations under temperature, and\ngradual components induced by Na⁺ diffusion.\nFig. R25. a Coaxial plots of coupled signals and prediction curves for small variations\nin electrolyte, temperature, and pressure. Temperature was fixed at 30°C at the cold end,\nwhile the hot end varied from 30°C to 35°C. Pressure ranged from 0 to 3 N, and Na⁺\nconcentration varied from 0 to 100 mM in two permeation steps. b Comparison of\ncoupled signals and LSTM prediction results for electrolyte, temperature, and pressure\non the same X-axis.\nTo quantitatively decouple these signals, we employed a multilayer long short-term\nmemory (LSTM) neural network integrated with a local attention mechanism. The\ncontinuous current signal was segmented into fixed-length windows of 40 s as model\ninputs. At each time step t, the local attention module computed similarity scores e(t, j)\nbetween the current signal and its neighboring time frames within the range [t − 40, t +\n40], and then converted them into attention weights α(t, j) through a softmax function.\nThis mechanism adaptively focuses on the most relevant local segments while\neffectively suppressing long-range noise, thereby enhancing the extraction of modality-\nspecific features.\n𝑒𝑡,𝑗 = 𝑠𝑐𝑜𝑟𝑒(𝑠𝑡,𝑠𝑗),𝑗 𝜖 [𝑡 −40,𝑡+40] 𝑅(4)\nexp(𝑒𝑡,𝑗)\n𝑐𝑡 = ∑ 𝑆𝑗 𝑅(5)\n𝑗\n∑𝑘exp(𝑒𝑡,𝑘)\nSubsequently, a three-layer stacked L\nx\nS𝑡T\n=\nM [n\ns\ne𝑡t\n,\nw 𝑐𝑡o]rk was used, where each unit con\n𝑅\nta(i\n6\nn)s\na forget gate (fₜ), input gate (iₜ), output gate (oₜ), and cell state (Cₜ). The forget gate\ncontrols how much of the historical state is retained, the input gate regulates the\nincorporation of new information, and the output gate determines the effective output\nat each time step. Through this gated mechanism, the LSTM can capture both short-\nterm transient responses (e.g., pressure-induced ionic polarization) and long-term\nsteady changes (e.g., temperature or Na⁺ diffusion). The multimodal features are\nencoded into the cell state vector Cₜ, representing the evolving state of multiple stimuli\nover time.\n𝑓𝑡 = 𝜎[𝑊𝑓 ∙[ℎ𝑡−1,𝑥𝑡]+𝑏𝑓] 𝑅(7)\n𝑖𝑡 = 𝜎[𝑊𝑖 ∙[ℎ𝑡−1,𝑥𝑡]+𝑏𝑖] 𝑅(8)\n𝐶 𝑡 = tanh[𝑊𝐶 ∙[ℎ𝑡−1,𝑥𝑡]+𝑏𝐶] 𝑅(9)\nAfter feature extraction, a three-\n𝐶\nl𝑡ay\n=\ner\n𝑓\nf𝑡u\n∗\n𝐶\n𝑡−1\n+\n𝑖\n𝑡\n∗\n𝐶\n𝑡\nnetwork (FCN) was appen\n𝑅\nde\n(\nd\n1 0\nto\n)\nthe model. The FCN performs nonlinear mapping from each LSTM hidden state to three\nindependent outputs corresponding to temperature, pressure, and Na⁺ electrolyte\nconcentration. As a result, the model can achieve real-time separation and quantitative\nreconstruction of the mixed current signal at each moment, effectively decomposing it\ninto three independent physical components. This enables the independent recognition\nand dynamic tracking of multimodal stimuli in real time.\nFig. R25 have been added in the Supplementary Information as Fig. S37. This\ndiscussion has been added to page 21 of the manuscript, lines 496–499, as follows:\n“When temperature, pressure, and Na⁺ electrolyte stimuli in their characteristic\npatterns were simultaneously applied to the AMSG, the resulting compound current\nsignal was accurately decoupled into distinct components corresponding to each\nstimulus (Fig. 4d and Fig. S37).”\nComment 3.10: In figure 5g, does the sensor contact the heat and cold source? Why is\nthere no apparent change in the current recording in response to pressure?\nResponse: Thank you very much for your questions. In Fig. 5g, the sensor does not\ncontact either the heat or cold source, which is why no pressure-induced current\nvariation is observed in the recording.\nThis experiment was specifically designed to evaluate non-contact temperature sensing;\nthus, only thermal radiation from the object induces a temperature gradient across the\nhydrogel, generating a thermoelectric current (Fig. R26). By analyzing the real-time\ncurrent feedback during approach, the system can distinguish between safe and\nhazardous temperature zones. When the detected temperature exceeds a preset\nthreshold, the wristband provides thermal warning and vibrotactile feedback alerts the\nusers to prevent grasping overheated objects.\nFig. R26. Current signals of the AMSG as the robotic hand approaches or retreats from\nheat or cold sources, illustrated by optical and infrared images.\nThe feedback signal for the grasping process involving contact with hot source is shown\nin Fig. R27 (Fig. 5h in the manuscript). It further demonstrates a representative\napplication scenario of the AMSG wristband that integrates sensing, haptic feedback,\nand safety-protection functions. The entire workflow produces a characteristic three-\nstage current profile: (1) non-contact temperature prediction, (2) contact-based accurate\ntemperature sensing, and (3) grip-force sensing.\nAs the robotic hand approached a paper cup filled with hot water, the current signal\ngradually increased to ~14 μA. If the detected temperature remained within a safe range,\ngrasping proceed; otherwise, robotic motion was stopped. Upon contact, the current\nrose to ~30 μA due to the sudden temperature increase. Meanwhile, the thermal actuator\non the user side was triggered to reproduce the detected temperature. A similar response\ncould also be achieved for cold objects, enabled by the polarity of the thermoelectric\nsignal.\nIn addition, the safety mechanism also extends to pressure sensing. During grasping, a\ncurrent exceeding ~45 μA indicates excessive gripping force. By monitoring this\nthreshold, the system can avoid over-compression of fragile objects. The complete\ngrasping process is shown in Supplementary Video 3-5.\nFig. R27. Current signals of the AMSG as the robotic hand approaches or retreats from\nheat or cold sources, illustrated by optical and infrared images.\nTo improve clarity, we have revised the description on lines 579–589 of page 24 in the\nmanuscript to “The entire workflow produced a three-stage current profile: (1) non-\ncontact temperature sensing, (2) contact-based accurate temperature sensing, and (3)\ngrip-force sensing (Video S5). As the robotic hand approached a paper cup filled with\nhot water, the current signal gradually increased to 14 μA. If the detected temperature\nremained within a safe range, the grasping action would proceed; otherwise, robotic\nmotion would be stopped. Upon contact, the current rose to 30 μA due to the sudden\ntemperature increase. Meanwhile, the thermal actuator at the user end was triggered\nto reproduce the detected temperature. A similar response could also be achieved for\ncold objects, enabled by the polarity of the thermoelectric signal. In addition, the safety\nfeature was integrated in the pressure sensing feedback. For specific objects, an\nadaptive grasping action generated a piezoelectric signal below a set threshold.”\nComment 3.11: Are there any supporting videos for figure 5f for real-time linear motor\ncontrol from sensor feedback? Are there additional videos to accompany figure 5h to\nshow responsive robotic hand control according to feedback? Further supporting videos\nshould better demonstrate “closed-loop” functionality.\nResponse: Thank you for your valuable feedback and suggestions. Following your\nadvice, we have added more videos in the supplementary materials to more\ncomprehensively demonstrate the real-time control and closed-loop functionality of our\nsystem.\nSupplementary Video 3 presents the complete demonstration associated with\nSupplementary Fig. 5h. The workflow exhibits three distinct signal stages: (1) non-\ncontact temperature prediction, (2) contact-based accurate temperature sensing, and (3)\ngrip-force sensing. As the robotic hand approaches a cup containing hot water, the\nthermoelectric current increases progressively, enabling real-time temperature\nprediction and immediate decision-making on whether the grasping action should\nproceed. If the temperature exceeds the safe threshold, the system terminates the motion\ninstantly. Upon contact, the current rises sharply, allowing real-time identification of\nthe actual object temperature. During gripping, the piezoionic response produces a\nlarger and faster current change, which the system uses in real time to regulate gripping\nforce and prevent excessive compression.\nSupplementary Video 4 corresponds to Fig. 5f shows the linear vibration-control\nbehavior. Under different gripping forces, the vibration module presents clearly\ndistinguishable different vibration amplitudes and frequencies in a real time.\nSupplementary Video 5 demonstrates a complete closed-loop human-machine\ninteraction scenario. It showcases remote handling of hazardous chemicals, integrating\nphysiological monitoring of the operator, robotic-arm manipulation, and real-time\nreproduction of temperature and grip-force feedback.\nAll videos are uploaded in the supplementary materials. We believe that these\nadditional videos provide a clearer and more intuitive demonstration of the real-time\ncontrol and closed-loop capabilities of the AMSG system."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors fully addressed the comments raised by all the reviewers. This manuscript can be published without further revisions.", "reviewer_2": "Authors have well addressed my questions, get no further questions.", "reviewer_3": "The authors have fully addressed my previous comments."}, "author_response": ""}]

Decision letter