Ultrasensitive multi-degree-of-freedom piezoionic sensor via synergistic hydrogel-ion interactions

Domain: physics
Published: 2025-12-25
Editorial outcome: major_revision

Abstract
Accurate body motion capture is of great significance for biomechanical assessment and immersive virtual/augmented reality (VR/AR) systems. Piezoionic flex-sensors, which mimic the ionic transport sensing mechanism of cutaneous mechanoreceptors, promises a self-powered approach for wearable body movements detection. However, their practical utility has been limited by low piezoionic flex-sensitivity (typically below 0.5 mV/degree) and unidirectional bending response, which restricts their ability to resolve complex multi-degree-of-freedom (multi-Dof) body motions. Here we report a soft, piezoionic and multi-Dof (SPIM) flex-sensor that overcomes these limitations. It ingeniously integrates zwitterionic dipole-ion interactions and size-induced steric hindrance effects to enable fast ion channels formation and amplified cation/anion transport imbalance, thus cooperatively contributing to a record-high piezoionic flex-sensitivity of 3.2 mV/degree. Theoretical simulations combining poroelastic mechanics and Poisson-Nernst-Planck model corroborated the synergistic enhancement. Furthermore, a unique architecture of square prism-shaped fiber with two pairs of symmetric nanomesh electrodes allows the SPIM flex-sensor to decouple multi-Dof joint flexion to distinguish complex body movements. We demonstrate a SPIM flex-sensor platform for metaverse applications, including a digital-twin of free-pose hand motions and complex yet intuitive VR control via single-joint dexterity equipped with a single compact sensor device.

Introduction
Soft flex-sensors that transduce bending deformations into measurable electrical signals represent a promising technology for real-time body motion tracking, which finds important applications such as biomechanical assessment for rehabilitation, and posture reconstruction for augmented and virtual reality (AR/VR)1–8. Conventional rigid techniques, including multi-camera systems9,10 and inertia measurement units11,12, both offer mature hardware performance, but are limited by costly setups, complex calibration, and wearing discomfort. In contrast, emerging flexible and/or soft flex-sensors based on resistive13–15, capacitive16,17, piezoelectric18–20, triboelectric21,22, and optical23 mechanisms represent a promising wearable strategy to record joint flexion states. While resistive and capacitive flex-sensors adopt simple structures, they suffer from continuous power consumption and susceptibility to electromagnetic interference.

Piezoelectric and triboelectric flex-sensors provide self-powered electrical signals, yet they respond selectively to dynamic stimuli. A distributed fiber-optic sensor has been devised to decouple complex deformation modes for reconstruction of multi-joint finger bending24. However, its utility might be compromised by complicated integration of multiple optical components.

Recently, the mechanically gated ion migration in skin mechanoreceptors has inspired the design of piezoionic flex-sensors to convert bending-induced stress/strain gradient into ionic voltage/current, accompanied by desirable characteristics of mechanical softness, self-powering capability, and compatibility with ionic biological processes25. Nafion-based designs have demonstrated feasibility as flexible matrix in piezoionic flex-sensors, but are constrained by low stretchability resulting in a limited sensing range (below 120 bending degrees)26–28. Additionally, other stretchable ionic polymers such as PAM29, PVA30, PEO31, TPU32–34, and PVDF-HFP35 offer improved deformability but exhibit low bending sensitivity (below 0.5 mV/degree) due to the absence of polymer-ion interaction modulation. More recently, Jia et al. regulated the polymer-ion interaction by selecting chloride salts to match the ion flux polarity with the polycation/polyanion matrix36. Although the flexoelectric coefficient was boosted, the flex-sensing performance (bending range of 75o and sensitivity of 0.04 mV/degree) still struggled to detect subtle or vigorous joint movements. Besides, previously reported piezoionic flex-sensors mostly were targeting at one dimensional or single-axis bending, which made the monitoring of multi-degree-of-freedom (multi-Dof) joint motion unachievable through a compact single device.

In this work, we propose a soft, piezoionic, and multi-Dof (SPIM) flex-sensor that offers high resolution, real-time body motion tracking. The SPIM flex-sensor features a combination of compositional and structural design. Specifically, the deliberate pairing of organic salt with polyzwitterionic hydrogel leverages ion-dipole interactions and size-induced steric hindrance effects, facilitating fast ion channels formation and amplifying charge mobility imbalance, respectively. Such dual factors synergistically contribute to a record-high piezoionic flex-sensitivity of 3.2 mV/degree, along with a broad detection range of up to 180 degrees. Finite element simulations combining poroelastic mechanics and Poisson-Nernst-Planck model corroborate the mechanistic origins of this enhancement. By adopting an architecture of square prism-shaped fiber with two pairs of symmetric nanomesh electrodes, the SPIM flex-sensor is able to decouple multi-Dof joint bending to distinguish complex body movements (e.g., flexion/extension, abduction/adduction, and clockwise/anticlockwise circumduction). We showcased the efficacy of the self-adhesive SPIM flex-sensor as an on-skin solution for high-fidelity joint kinematics tracking in metaverse applications, including digital-twin of free-pose hand motions and complex yet intuitive VR control through single-joint dexterity. Altogether, the SPIM flex-sensor marks a significant step forward in soft electronics, merging ultrahigh sensitivity, wide-angle responsiveness, and multidimensional motion resolution within a compact, wearable format suitable for next-generation human-machine interfaces.

Results
The design concept of SPIM flex-sensor
Mechanical pressure on the skin stimulates cutaneous mechanoreceptors, initiating ion migration through opened channels that transmit transmembrane ionic fluxes to the human brain for sensory interpretation (Fig. 1a). Inspired by this biological mechano-sensing process, piezoionic flex-sensors comprising a hydrogel matrix infused with mobile ions have been developed. These sensors generate a net ionic transport (i.e., ionic voltage/current) due to differences in cation/anion displacement rates under a stress/strain gradient caused by bending deformation (Supplementary Fig. 1). Previous efforts were mostly devoted to constructing piezoionic flex-sensors using different combinations of polymer and ion species, while few focused on the rational modulation of polymer-ion interactions to boost the flex-sensitivity. The piezoionic signal generation can be described as a cross-scale process: a strain gradient field across the hydrogel thickness first generates a hydrostatic stress gradient to drive an imbalanced ionic flux due to the polymer-ion modulation and ion size difference, and then the ionic flux transforms into electronic output signal at the hydrogel-electrode interface via the electrical double layer (EDL) charging (Supplementary Note 1, Supplementary Figs. 2 and 3).

In principle, piezoionic output is dependent on two key factors: the number of migrated charge carriers during an ionic flux event (associated with ionic conductivity) and the degree of imbalance in cationic/anionic mobility (related to ion transference number)25,37. With this in mind, we propose a synergistic strategy to achieve a record-high flex-sensitivity by regulating the imbalanced ionic flux behavior. Figure 1b illustrates three typical combinations including inorganic salt in a nonionic hydrogel, inorganic salt in a zwitterionic hydrogel, and organic salt in a zwitterionic hydrogel (chemical structures and hydrated ionic radii are listed in Supplementary Fig. 4). For the first conventional design (Fig. 1b(i)), taking lithium chloride (LiCl) in polyacrylamide (PAM) as an example, it shows a sluggish ion transport due to uncoordinated ionic transfer within the porous hydrogel network. For LiCl in a zwitterionic copolymer poly[2-(methacryloyloxy)ethyl] dimethyl-(3-sulfopropyl) (PSBMA)-co-PAM (denoted as PSA, Fig. 1b(ii)), the zwitterionic side chains contain positively charged quaternary ammonium group and negatively charged sulfobetaine sulfonate group on the same repeating unit. The zwitterionic groups enable the adsorption of counter ions in the electrolyte by zwitterionic dipole-ion interaction, thereby establishing separate cationic/anionic migration channels38,39, as supported by FTIR characterization in Supplementary Fig. 5. These migration channels facilitate the effective separation of Li+ and Cl- by readily overcoming electrostatic attraction between the charged groups and counter ions during deformation-induced ion flux.

Meanwhile, the solvation structure of the hydrated Li+ cations strongly affects the ion diffusion kinetics40–42. Density functional theory (DFT) calculations reveal that the binding energies of Li+-SBMA (−4.3 eV) is more negative than that of Li+-H2O (−1.6 eV), suggesting the preferential association with the sulfobetaine sulfonate group (Fig. 1c, detailed procedure in Supplementary Note 2). The electrostatic potential (ESP) results show that Li+-SBMA exhibits a lower ESP value with uniform surface charge distribution compared with Li+-H2O (Supplementary Fig. 6), indicating a kinetically favorable migration of Li+-SBMA. Radial distribution functions (RDFs) of the Li+-PAM and Li+-PSA systems further evidenced the strong coordinating ability of PSA to Li+ through the O atom of SBMA, which partially replaces the H2O molecules in the first Li+ solvation shell (Supplementary Fig. 7). Desolvation activation energies extrapolated from the Arrhenius equation using temperature-variable EIS curves (Supplementary Fig. 8), revealed values of 73.1 kJ/mol for PAM-LiCl and 56.2 kJ/mol for PSA-LiCl, validating the enhanced desolvation of hydrated Li+ and the resulting acceleration of cation transport kinetics. Molecular dynamics (MD) simulation was employed to further understand the ion transport behaviors inside the porous networks of PAM and PSA hydrogels upon applicaiton of an unidirectional flow pressure (detailed simulation procedure in Supplementary Note 3). According to the mean square displacement (MSD) shown in Fig. 1d, the calculated diffusion coefficients of Li+ in PAM and PSA were 4.3×10−6 and 5.5×10−6 cm2s−1, and those of Cl- in PAM and PSA were 4.8×10−6 and 7.2×10−6 cm2s−1 (Supplementary Fig. 9). The diffusion coefficients of both the cations and anions increased in the polyzwitterionic PSA hydrogel, indicating the boosted ion mobility due to the migration channel formation. Figure 1f compares the experimental results of conductivity, transference number and flex-sensitivity of the three hydrogel-ion combinations. These ion migration channels and the enhanced desolvation of Li+ were evidenced by the increased ionic conductivity from 16.9 to 30.2 mS/cm (EIS measurements for conductivity in Supplementary Fig. 10) and Li+ transference number (tLi+) from 0.32 to 0.4 (steady-state current method for transference number in Supplementary Fig. 11), respectively. The dual factors contributed to significantly improved flex-sensitivity from 0.17 to 1.9 mV/degree (testing details in Methods).

We further replace LiCl with Li [bis(trifluoromethylsulfonyl)imide] (Li(TFSI)) to magnify the size difference between the cation and anion (Fig. 1b(iii)). The bulky (TFSI)- anion with a large size (0.79 nm) tends to become immobilized within the crosslinked polymer networks due to steric hindrance. PSA-Li(TFSI) exhibited a remarkably improved tLi+ of 0.85, suggesting an amplified difference in cationic/anionic mobility. This significant increase of tLi+ compared with that of PSA-LiCl was due to a coupled ion-polymer transport dynamics of suppressed anion mobility and difference in ion association structure (details in Supplementary Note 4, Supplementary Fig. 12). The synergistic hydrogel-ion tuning in PSA-Li(TFSI), i.e., the cationic migration channel boosted by desolvation effect and the anionic migration channel impeded by steric hindrance, empowers a record-high piezoionic flex-sensitivity of 3.2 mV/degree.

To better understand the mechano-ionic-electrostatic coupling process in piezoionic sensing, we performed finite element simulations using COMSOL Multiphysics by combining poroelastic mechanics and Poisson-Nernst-Planck model. The simulations reproduced the physical process in a hydrogel to reveal the coupling relationship between deformation geometry and multiphysical responses including the distributions of pore pressure, ionic concentration, and electric potential (details in Supplementary Note 5, Supplementary Figs. 13 and 14). The simulation results in Fig. 1e revealed varying electrical potential difference between the bottom and top surfaces (VAB) of −2, −10.1 and 19.4 mV, respectively. The polarity reversal and amplitude increase agreed well with the proposed modulation in ion migration mobility and the resulting cationic/anionic transport imbalance (associated with ionic conductivity and ion transference number) across the three different systems. These simulation approaches constitute a cross-scale framework ranging from molecular scale to device scale, which illustrates how the polymer-ion interactions synergistically regulate ion migration to achieve an ultrahigh piezoionic sensitivity (detailed exposition in Supplementary Note 6): The molecular-level DFT calculation exhibited the energetically favorable binding of zwitterionic side chain to the Li+ ion over water molecules; These energetic parameters supported the mesoscopic-level MSD simulation of the ion transport dynamics, verifying the promoted Li+ diffusion coefficient; Device-level simulation employed FEM to transform the ion transport imbalance to the piezoionic output potential, explaining the varied sensing performance between different polymer-ion combinations. To analyze the correlation between the external mechanical excitation and the electrical signal generation, piezoionic equivalent circuit model coupling a mechanically driven current source and electrochemical impedance elements was established (Supplementary Note 7, Supplementary Figs. 15 and 16).

Unlike previous piezoionic sensors that primarily relied on enlarging ion-size disparity or introducing structural asymmetry, our polymer-ion cooperative modulations simultaneously united enhanced ionic conductivity through zwitterionic coordination and amplified ionic mobility imbalance owing to the adoption of bulky organic anions. Compared with previous piezoionic flex-sensors based on various polymer-ion combinations27,28,30–32,34,36 (Fig. 1g and Supplementary Table 1), the SPIM flex-sensor ranks among the highest in both flex-sensitivity and sensing range, owing to the synergistic hydrogel-ion interaction and robust hydrogel-electrode system discussed in the following section. Also, a comprehensive performance comparison between our piezoionic flex-sensor and reported flex-sensors based on other sensing mechanisms were conducted, including resistive, capacitive, piezoelectric, triboelectric, and fiber-optic types (Supplementary Table 2). Our SPIM flex-sensor shows a marked advantage in terms of the sensing range and cyclic stability (Supplementary Fig. 17).

Mechanical and electrical robustness of the hydrogel-electrode interface
The working range, signal reproducibility, and device robustness of mechanoelectrical sensors are highly dependent on the mechanical/electrical resilience and interfacial toughness43–46. Due to the ultra-softness of PSBMA hydrogel (Young’s modulus of 4 kPa), PAM was copolymerized to enhance its mechanical strength. When the molar ratio of monomers (AM to SBMA) was increased to 1:3, the resulting copolymerized zwitterionic hydrogel exhibited improved mechanical strength (Young’s modulus of 14 kPa), offering elastic resilience and free-standing capability (Fig. 2a). PSA-Li(TFSI) with a monomer ratio of 1:3 and a salt concentration of 1 mol/L was selected for further investigation. To characterize mechanical resilience, stretch-release events with stepwise tensile strains from 50% to 450% were conducted on the PSA-Li(TFSI) hydrogel. The recorded stress-strain curves indicated residual strains of 0.4% and 5.3% at tensile strains of 100% and 400%, suggesting negligible hysteresis loops during the stretch-release process (Fig. 2b). Cyclic test at tensile strain of 100% showed a low residual strain of 0.7% after 100 cycles, which further evidenced the superior elastic resilience (Fig. 2c). This rapid elastic recovery with low hysteresis may be related to the reformation of the entropy-driven zwitterionic-zwitterionic self-associations with fast crosslink dynamics47. The PSA-Li(TFSI) hydrogel was chosen as the piezoionic matrix as it possessed optimal balance in mechanical compliance, adhesive strength, and ionic conductivity (Figure Supplementary Figs. 18 and 19) compared with control hydrogels of PAM-LiCl, PAM-Li(TFSI), PSA-LiCl, resulting a highest piezoionic performance (Supplementary Figs. 20 and 21). The concentration of Li(TFSI) also influences the mechanical and electrical properties of the PSA-Li(TFSI) hydrogel. A systematic investigation was conducted to determine an optimal concentration of 1 M, which exhibited highest mechanical strength and electrical conductivity, leading to a highest piezoionic flex-sensitivity (detailed analysis in Supplementary Note 8, Supplementary Figs. 22 to 24).

Maintaining electromechanical stability in flexible electrodes is imperative for ensuring consistent electrical outputs. Three kinds of flexible electrodes were prepared as alternatives, as shown in Supplementary Fig. 25, including PDMS-AgNP film, TPU-AgNP film, and eTPU-AgNP nanomesh (preparation details in Methods). The eTPU-AgNP nanomesh refers to electrospun thermoplastic polyurethane (eTPU) nanofiber membrane coated with silver nanoparticles (AgNPs). The eTPU-AgNP nanomesh electrode was fabricated by combining the techniques of electrospinning and vacuum evaporation deposition to achieve a uniform nanofibrous network with conductive coating (Supplementary Fig. 26). We systematically investigated the influencing parameters in manufacturing the eTPU-AgNP nanomesh electrode with the purpose of promoting the electromechanical and sensing performance (Supplementary Figs. 27 to 29), and the optimized fabrication parameters were determined to be applied voltage of 12 kV and solution volume of 1 mL in electrospinning, and Ag usage amount of 0.474 g in AgNP deposition. The eTPU-AgNP nanomesh outperforms the TPU-AgNP film and PDMS-AgNP film as a flexible electrode in flex-sensor, as it exhibits not only a low initial resistance of 0.65 Ω cm⁻¹, but also a more stable resistance under bending deformation than those of the TPU-AgNP film and PDMS-AgNP film (Supplementary Fig. 30). Cyclic bending tests further confirmed the electromechanical robustness of the eTPU-AgNP nanomesh electrode (Fig. 2d), the eTPU-AgNP nanomesh electrode presented an extremely stable resistance (from 1.2 Ω/cm to 1.3 Ω/cm) after 10,000 bending cycles, while the PDMS-AgNP and TPU-AgNP electrodes both displayed a sharp rise in resistance leading to electric failure after several thousands of bending cycles. We attributed the superb electrical resilience to the porous microstructure of the nanofibrous eTPU scaffold and the anchoring of the percolated AgNPs network on the fiber surface (Figs. 2e and f), which could accommodate the bending strain via fiber deflection and reorientation, thereby preserving the percolation network on the fiber surface48. Besides, the eTPU-AgNP nanomesh exhibited a strongest adhesive strength to the hydrogel to facilitate a tight and gap-free bonding, which accounted for a low and stable interfacial impedance of the hydrogel-electrode interface (confocal microscopy images of electrode-hydrogel interfaces in Supplementary Fig. 31 and interfacial impedance spectra in Supplementary Fig. 32). These combined merits of the eTPU-AgNP nanomesh electrode collectively resulted in a more sensitive and stable piezoionic signal than those of TPU-AgNP film and PDMS-AgNP film (Supplementary Figs. 33 and 34). Comparative analysis between different electrodes in terms of equivalent circuit parameters was conducted to show the advantages of eTPU-AgNP nanomesh electrode in interfacial polarization, and piezoionic output (Supplementary Note 9, Supplementary Fig. 35).

An adhesive hydrogel is advantageous for achieving strong interfacial toughness with the attached electrodes for robust device operation. The PSA-Li(TFSI) gel could adhere to various substrates including glass, polyethylene terephthalate (PET), polypropylene (PP), porcine skin, and the eTPU-AgNP nanomesh electrode. The adhesive strength was measured by probe tack test (Supplementary Fig. 36), exhibiting strong adhesion across all substrates, with the highest adhesion strength of 27 kPa observed for the nanomesh electrode (Fig. 2g). This desirable interfacial robustness stems from the multiple interactions enabled by the polar and charged zwitterionic groups, such as hydrogen bonding, ion-polar interactions, electrostatic attraction (Fig. 2h). Compared with the PAM and PSA hydrogels, the PSA-Li(TFSI) exhibited markedly enhanced adhesion to different substrates owing to the electrostatic interaction offered by zwitterionic groups and the energy dissipation effect from the ion-dipole interaction (Supplementary Fig. 37).

Sensing properties of the SPIM flex-sensor
The sensing properties of the PSA-Li(TFSI) based flex-sensor were systematically investigated by sandwiching the hydrogel between two eTPU-AgNP nanomesh electrodes and recording the output open-circuit voltage (Voc) under varying bending angles (Supplementary Fig. 38 and Experimental section). The Voc increased with bending angle (θ), and the sensitivity (S), determined by linear fitting, was 3.2 mV/degree below 60 degrees and 8.6 mV/degree in the range of 60 to 180 degrees (Fig. 3a). Figure 3b records the change of output potential in response to a quasi-transient bending input of 6 degrees, with the response and recovery times measured to be 300 and 250 ms, respectively. Moreover, when bent upward or downward at a same bending degree (60 degrees as the case in Fig. 3c), the Li(TFSI) flex-sensor generated equal-magnitude voltages of opposite polarity, which not only verified the piezoionic sensing mechanism of unbalanced ion migration, but also provided the capacity to distinguish two opposite bending directions. Cyclic bending of the sandwiched PSA-Li(TFSI) flex-sensor at angles ranging from 6 to 180 degrees generated consistent potential responses, indicating superb signal reproducibility (Fig. 3d). In frequency response analysis, the sensor effectively captured bending stimuli across a wide frequency range from 0.05 to 4 Hz (Fig. 3e), covering the typical motion frequencies of human joints in daily life. A cyclic bending test at 40 degrees showed no obvious degradation of output potential throughout 10,000 cycles (Fig. 3f), indicating reliable sensing durability. The combinatory sensing parameters of ultrahigh sensitivity, wide sensing range and extended durability stem from the integrated effects of synergistic hydrogel-ion interactions, mechanical/electrical resilience and robust hydrogel-electrode interface.

SPIM flex-sensor enables digital-twin of free-pose hand motion
The human hand possesses complicated motion agility essential for manipulation tasks for daily functions49,50. The excellent flex-sensing performance of the SPIM device motivated the design of a flex-sensor composed of a square prism-shaped PSA-Li(TFSI) fiber (side length of 6 mm, height of 40 mm) and two pairs of eTPU-AgNP nanomesh electrodes (Fig. 4a). The wrist joint was selected as the target considering its multi-Dof motions (Fig. 4b), including flexion/extension, abduction/adduction, and clockwise/counterclockwise circumduction. The adhesive SPIM flex-sensor was attached to the carpal bone position to record multi-directional joint bending. Upon wrist flexion at increasing bending angles from 10 to 90 degrees, the SPIM flex-sensor generated potential signals with corresponding increasing magnitudes from 16 to 450 mV accordingly (Fig. 4c). During consecutive wrist extensions at a fixed bending angle of 40 degrees, performed at varying frequencies from 0.2 to 1 Hz, the SPIM flex-sensor produced potential signals of consistent amplitude matching the actuation frequencies (Fig. 4d).

The accurate differentiation of bending rate and amplitude allowed the integration of the SPIM flex-sensor into a wearable platform for a real-time digital-twin reconstruction of hand motions (Fig. 4e). The two pairs of electrodes, aligned along the X- and Y-axes directions, generated output potential signals upon wrist bending, with the signal polarity (positive or negative) and magnitude relating to the bending direction and amplitude. For axial bending or bending around arbitrary direction, the bending amplitude (or bending curvature) and direction can be deduced from the piezoionic potential output of the two pairs of electrodes in X- and Y-axis (Supplementary Note 10). Through data collection and processing via customized microcontroller modules (see Methods), the potential signals could be translated into free-pose hand motion animations in digital Unity interface. Figure 4f delineates the transformation of distinct signal patterns of X- and Y-axis electrodes to digital-twin representation of hand movements in VR space (Supplementary Movie 1), including flexion and reversion, abduction and reversion, clockwise circumduction, etc. For hand motions of flexion/extension or abduction/adduction, only one pair of the X- or Y-axis electrodes produced potential signals. In contrast, for hand motions of wrist circumduction, the SPIM flex-sensor gave rise to triangular wave potential patterns in X- and Y-axis electrodes, with a 1/4 cycle phase difference for anticlockwise motion or a 3/4 cycle phase difference for clockwise motion. To showcase the versatility of the SPIM-flex sensor in multi-joint use, we extended the deployment from wrist joint to elbow and shoulder joints, which also involved multi-Dof motions and even larger angular displacement. The deployment of SPIM flex-sensor, the typical output signal of multi-Dof motions, and the corresponding motion capture in Unity space were shown in Supplementary Fig. 39. This extensible approach allows the monitoring of different joints without interference, enabling synchronized and more complex motion capture applications, such as the “arm raising and hand waving” movements (Supplementary Fig. 40). The accurate reconstruction of joint kinematics is essential for biomechanical evaluation in rehabilitation and immersive experiences in VR/AR scenarios.

Single-joint VR control
The high-fidelity capturing of dexterous joint motion using a single SPIM flex-sensor is uniquely suitable for sophisticated yet intuitive VR control. The skin-integrated SPIM flex-sensor generates distinctive signal patterns from the combinations of X- axis (flexion/extension direction) and Y-axis (abduction/adduction direction) electrodes, which can be further translated into control commands for driving a virtual car in VR space. In this system, the amplitude and direction of joint bending are intuitively converted to the speed and direction of the virtual car (Fig. 5a). With X-axis signals assigned to forward/backward motion and Y-axis signals to left/right steering in the VR space, an operator wearing the self-adhesive SPIM flex-sensor on the wrist was able to issue diverse driving commands through static wrist postures and dynamic wrist motions. The mathematical model of the multi-Dof bending in VR control was investigated to obtain a quantitative mapping from mechanical input to electrical output, which lays the foundation for the technical implementation in transforming physical deformations to virtual movements (Supplementary Note 11, Supplementary Figs. 41 to 45). Figure 5b outlines eight representative driving commands, accompanied by the corresponding joint postures/motions with specific time-evolving bending angles in X- and Y-axis directions. Specifically, flexion or extension postures led to constant-speed driving forward or backward respectively; The bending angle increase or decrease in flexion angle resulted in car accelerating or decelerating and braking; When abduction or adduction was coupled with flexion, the virtual automobile turned left or right; Coupling mixed abduction-flexion or adduction-flexion motions with subsequent pure flexion posture translated into lane-changing to left or right. As a result, the SPIM flex-sensor enabled the steering of a virtual automobile in a VR space of traffic roads through a series of driving commands, as shown in Fig. 5c (Supplementary Movie 2). The ability to perform complicated VR control through a single joint qualified the skin-integrated SPIM flex-sensor as an efficient, intuitive, and versatile controlling solution for metaverse applications.

Discussion
Accurate human motion tracking relies on precise recording of the pertinent joint kinematics. We devised a soft, piezoionic, and skin-integrated flex-sensor capable of identifying joint bending states with exceptional sensitivity and multi-degree-of-freedom (multi-Dof) resolution. The synergistic hydrogel-ion interaction of PSA-Li(TFSI) promote cationic migration via boosted channels by desolvation effect and anionic migration channel impeded by steric hindrance, collectively enabling ultrahigh piezoionic sensitivity. A rational geometry of square prism-shaped hydrogel fiber with symmetric electrode pairs empowered the SPIM flex-sensor to detect and differentiate multi-Dof bending movements. Furthermore, the superior mechanical/electrical resilience and robust interface of the hydrogel-electrode system bestowed durable flex-sensing performance. The combinatory sensing parameters of the SPIM flex-sensor exhibits one of the highest-performing sets of sensing parameters among reported piezoionic flex-sensors. To prove the versatility of the SPIM flex-sensor in high-fidelity motion tracking, we showcased metaverse applications including digital-twin of free-pose hand motion and complex yet intuitive VR control through single-joint dexterity. The proposed SPIM flex-sensor holds great potential for future real-time and precise biomechanical analysis and next generation immersive entertainment scenarios.

Methods
Materials
The raw materials used in the experiments were: sulfobetaine methacrylate (SBMA), acrylamide (AM), lithium chloride (LiCl), lithium trifluoromethylsulfonimide (Li(TFSI)), potassium persulphate (KPS), N, N’-methylenebisacrylamide (MBAA), N, N-dimethylformamide (DMF), thermoplastic polyurethane (TPU), Polydimethylsiloxane (PDMS), silver particles (Ag), copper-nickel conductive adhesive tapes, and silver paint.

eTPU-AgNP nanomesh
The detailed fabrication procedure of the eTPU-AgNP nanomesh electrode is as follows: (i) Thermoplastic polyurethane (TPU) was dissolved in the solvent of N, N-dimethylformamide (DMF), with the polymer concentration and viscosity adjusted to achieve stable jet formation. (ii) The prepared solution was loaded into a syringe and ejected through a metallic needle under a high-voltage electric field. Electrospinning parameters were controlled, including applied voltage (10-16 kV), solution feeding volume (0.2-2 mL), flow rate (0.5-1.0 mL h⁻¹), and tip-to-collector distance (15-20 cm). (iii) Electrospun nanofibers were collected on a rotating drum collector, where the rotation speed (100-300 rpm) was adjusted to control fiber alignment and network density. (iv) AgNPs were deposited onto the fibrous mats by vacuum evaporation deposition. The deposition thickness was precisely controlled to achieve an optimal balance between high electrical conductivity and mechanical flexibility.

TPU-AgNP film
A solution with a mass fraction of 20% was prepared by dissolving 5 g of TPU in 20 g of DMF and stirred at 70°C for 5 h. After that, the solution was drop-coated on a glass template and dried in a vacuum oven at 70°C for 9 h to obtain a TPU film. Finally, metallic silver particulate material was deposited on the TPU film.

PDMS-AgNP film
Droplets of PDMS precursor were applied to the glass template and cured in a vacuum oven at 80°C for 30 minutes to obtain the PDMS film. Finally, metallic silver particulate material was deposited on the PDMS film.

Preparation of the hydrogels
For control experiments, three different gels were experimentally prepared, including PAM and P(SBMA-AM) hydrogels without salt and P(SBMA-AM) ionic hydrogels containing LiCl or Li(TFSI), respectively. In this work, the ionic hydrogels were prepared by a one-pot method as follows: 0.5 g of PAM, 1.5 g of SBMA (or none in the case of pure PAM) were homogeneously mixed with 10 ml of deionized water, then 2.87 g of Li(TFSI), or 0.42 g of LiCl were added and stirred for half an hour, followed by the addition of 4 mg of MBAA and 20 mg of KPS and stirred for one hour to obtain a transparent solution, which was finally injected into a homemade glass mold and removed after 9 hours of heat curing to obtain a hydrogel.

Fabrication of the ionic flexible sensor
Ionic sensor with sandwich structure: the electrode material was cut into a rectangle of 7 cm (length) × 1 cm (width), copper-nickel conductive tape was applied as a lead at one of its ends and fixed using silver paste at the joint. The ionic gel was cut into long strips of 4 cm (length) × 1 cm (width) × 2 mm (thickness) size. Finally, the electrodes were affixed to the top and bottom sides of the ionic hydrogel, assembled into an ionic sensor with a sandwich structure and encapsulated using silicone rubber.

Strip-structured ionic sensor
the electrode material was cut into a rectangle of 7 cm (length) × 0.5 cm (width), which was cut to obtain the topology-optimized structure of the electrodes, and Cu-Ni conductive adhesive tape was applied to one end as a lead, and the joint was fixed using silver paste. The ionic gel was cut into long strips of 4 cm (length) × 6 mm (width) × 6 mm (thickness) size. Finally, the electrodes were attached to the four sidewalls of the ionic hydrogel to assemble the ionic sensor in a strip structure and encapsulated using silicone rubber.

Characterization
Morphology characterization was conducted using a field emission scanning electron microscope (FESEM, SU8010, Hitachi, Japan). The transmittance spectrum was measured by an ultraviolet-visible spectrophotometer (LAMBDA1050, PerkinElmer Instruments, USA). The attenuated-total-reflection (ATR) FTIR spectrum was collected using an attenuated-total-reflection Fourier transform infrared spectrometer (PerkinElmer, Spotlight 400, USA). The mechanical tests were conducted on a universal material testing machine (CMT6103, Metus Industrial Systems China). The resistance of the electrodes was characterized by measuring the voltammetric (VA) curves using an electrochemical workstation (Autolab, Germany), and the slopes are resistance values.

EIS test
Two clean Pt sheets were used as electrodes, and 1.4 cm (radius) × 2 mm (thickness) gel electrolyte was filled between the two stainless steel sheets to ensure good contact, then the sample was subjected to an AC impedance test, with the frequency range set from 0.01 Hz to 100,000 Hz. The test results show a Nyquist impedance curve with an intercept on the x-axis, which is the solution resistance R. The ionic conductivity σ (mS/cm) of the electrolyte is determined by the following equation.
σ= 1 / R * L / S (1)
Where L is the gel electrolyte thickness (m); S is the gel electrolyte effective area (m2).

Ion mobility number test
Lithium ion transference number (tLi+) of Li||PAM-LiCl||Li, Li||PSA-LiCl||Li, and Li||PSA-Li(TFSI)||Li symmetric cells were tested by the steady current method (i-t) at a constant polarization potential of 10 mV. According to the following equation.
tLi+ = I s (ΔV - I 0 R 0) / I 0 (ΔV - I s R s) (2)
where I0 is the initial current, Is is the steady-state current, ΔV is the applied polarization voltage, R0 is the initial resistance, and Rs is the steady-state resistance.

Adhesion test
The adhesion strength was determined by a manometer (M5-20, Mark-10, America) by first applying a compressive pre-stress of 20 N between the electrode material and the gel, and then the manometer was separated at a travelling rate of 50 mm/min after 20 s. The tensile adhesion strength was determined by the following equation:
P= F / S (3)
Where F is the peak force that can be read by the dynamometer during the movement (the tension is negative), indicating the instant of separation of the gel film from the electrode film. S is the area of the electrode film in contact with the gel film, the size of the area is a 1.2 cm (diameter) circle.

Bending sensing performance test
The sensor was placed on a PET membrane and the PET membrane with the sensor was fixed between a motorized translation table (CMT6103, MTS Systems, China) using a fixture with an initial free length of the membrane of 5 cm. A computer-controlled stepper motor was used to control the displacement and speed of the membrane. When the device is bent, the change in open-circuit voltage at the upper and lower ends of the electrodes of the device can be tested by using an electrochemical workstation (Autolab, Germany).
The thin film sensor has a thickness of h and a length of L. The film bends when the displacement stage is moved by x. We assume that the center angle of the curved arc is θ, and the radius is R. According to the geometric relationship, we can list the equation.
θ * R = L (4)
2R * sin(θ/2) = L - x (5)
Knowing L and x, we can obtain θ and R from the equations. Finally, the bending strain ε can be calculated.
ε= h / 2R (6)
Finally, the bending strain and angle were calculated using the software MATLAB based.
Data processing process from the sensor to the user interface:
1. Sensor Input:
Flex sensors are fixed on the wrist and are capable of monitoring the bending of hand joints along the x and y axes. The sensors convert physical movements into electrical signals.
2. Sensor Output:
The output signal is typically analog. Oscilloscope graphs display the signal waveforms over time across different channels, representing various hand movements.
3. Analog-to-Digital Converter (ADC Converter):
The ADC converter transforms the continuously varying analog signals from the sensors into discrete digital signals that computers can handle, facilitating processing by microcontrollers (Arduino).
4. Arduino Signal Processing and Data Collection:
(1) Signal Processing
Inside the microcontroller system, the signal first undergoes filtering through a moving average filter, followed by real-time angle data calculation using an angular voltage linear regression model based on the voltage signals.
(2) Data Collection:
Arduino is responsible for collecting the real-time angle data and communicating it to the computer via a serial port connection.
5. Serial Port Connection:
Data is transferred from the Arduino to the PC using a serial port (USB).
6. C# Script:
On the PC, a C# script is used for further data processing and analysis. The script controls virtual objects for visualization based on the data received.
7. Unity Interface:
Unity, a powerful game engine, is used here to develop the user interface (UI), visualize the processed data, and facilitate interaction with virtual objects.
8. Virtual Objects:
In Unity, users can see direct feedback of hand movements, such as a virtual hand on the screen mimicking the user’s actual hand movements, or controlling the movement of a car through wrist motion.

Data availability
The data associated with all simulations and experimental results discussed in the main text of this paper are available. Source data are provided with this paper.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This paper presents an intriguing design of a soft piezoionic multi-degree-of-freedom (Multi-Dof) flex-sensor via a rational pairing of zwitterionic hydrogel and organic salt. To resolve the long-standing issue of low piezoionic output, the SPIM flex-sensor tactfully integrates ion-dipole interactions and steric hindrance effects to enable fast ion channels and amplified cation/anion mobility, synergistically contributing to a record-high flex-sensitivity of up to 8.6 mV/degree. In addition, it devised a unique square prism-shaped sensor structure with engineered nanofibrous electrodes to decouple multi-Dof joint motions using a single compact sensor. Such high sensitivity and multi-Dof recognition ability of the SPIM flex sensor allowed the demonstration of a soft, self-powered and wearable motion-capture platform, which showcased metaverse applications including a digital-twin of free-pose hand motions and VR control via single-joint dexterity through a single sensor. This work contains high scientific novelty as it for the first time fused intricate hydrogen-ion interactions in a piezoionic sensing system to boost piezoionic sensitivity, and corroborated the synergistic modulation through theoretical simulations combing poroelastic mechanics and Poisson-Nernst-Planck model. Considering the ingenious cation/anion mobility manipulations and the wearable motion-capture VR applications of wide interest, I here recommend the publication of this work in Nature Communications after some revision. Please see as following:\n1. The manuscript does not provide sufficient details on the fabrication of the electrospun electrodes. What are the key parameters that dominate the electromechanical performance of the stretchable electrodes?\n2. The author should provide quantitative adhesion testing data of the PSA-Li(TFSI) system to support a comprehensive comparison with those of PAM and PSA. Also, the underlying mechanism for the observed adhesion optimization should be clarified.\n3. The manuscript employs a range of simulation methods, including Density Functional Theory, Radial Distribution Function and Mean Square Displacement analyses from Molecular Dynamics, as well as finite element modeling using COMSOL. While each part individually offers useful insights, the overall presentation currently appears fragmented. To improve clarity and coherence, I suggest the author to provide a concise and comprehensive summary of the purpose, scope, and logical sequence of these simulations.\n4. Although this work proposed a new strategy to realize a record-high piezoionic sensitivity, I recommend to add discussion of previously reported high-output ionic flex-sensors to illustrate the difference in ionic modulation.\n5. Figure S10 presented three types of flexible electrodes including PDMS-AgNP film, TPU-AgNP film, and eTPU-AgNP nanomesh. What are the structural or material features that account for the markedly superior performance of the eTPU-AgNP nanomesh electrode compared with the others?\n6. The author showed the single-sensor efficacy in multi-Dof motion-capture of a single joint for metaverse applications. How does this approach perform when extended to multi-joints use, e.g., multiple sensors equipped to full-hand or full-body for motion tracking?\n7. Please check the inconsistencies in the labeling and captions of Figures 2, S3 and S5.", "reviewer_2": "This manuscript presents a piezoionic soft flex sensor composed of a zwitterionic hydrogel integrated with an ionic liquid (Li(TFSI)). The sensor exploits ion size–induced charge imbalance under mechanical deformation (i.e., bending) to achieve a sensitive piezoionic response. The primary contributions include: (1) enhanced flexural sensitivity through synergistic interactions between the hydrogel matrix and mobile ions, as well as robust electrostatic coupling at the hydrogel–electrode interface, and (2) multi-degree-of-freedom (multi-DOF) sensing capability enabling hand motion detection and integration with virtual reality (VR) control systems. However, the manuscript lacks sufficient mechanistic depth and comprehensive performance validation. In addition, the central claims are not adequately supported by experimental evidence, and the manuscript contains several technical and interpretational errors. Detailed comments are provided below.\n1. The manuscript lacks sufficient mechanism explanation of the fundamental principles governing the sensor operation. Specifically, the mechanism of ion migration under multi-directional mechanical deformation, which is central to the multi-DOF piezoionic sensing behavior, is insufficiently described. Moreover, the fundamental roles and performance characteristics of flexible electrodes are not clearly discussed. To improve scientific clarity, the authors should provide a detailed mechanism supported by schematic illustrations and a comprehensive explanation.\n2. The authors mention that the PSA matrix forms zwitterionic dipole-ion interactions with Li+ ion (Figure 1b(ii)). However, despite the same PSA environment, the Li⁺ transference number of PSA-Li(TFSI) is more than twice that of PSA-LiCl. This discrepancy requires clarification, especially since weaker PSA–Li⁺ interactions in PSA–Li(TFSI) would theoretically result in a higher transference number.\n3. In Figure 2, the authors highlight robust hydrogel–electrode adhesion, yet they do not sufficiently explain the function of the flexible electrode, which is a critical design element. The manuscript should include a detailed discussion on the mechanical compatibility between the hydrogel and electrode materials. The term \"eTPU\" must be clearly defined, and a comparative analysis of the tensile stress–strain behavior of PDMS, TPU, and eTPU is recommended to justify electrode selection.\n4. The manuscript does not provide sufficient performance evaluation of the PSA–Li(TFSI) flex sensors. Comparative data using control materials (e.g., PAM–LiCl, PAM–Li(TFSI), PSA–LiCl) are needed to substantiate claims of material superiority.\n5. The concentration of Li(TFSI) may significantly influence both ion migration under mechanical deformation and electrostatic interactions with the polymer matrix. The authors are encouraged to investigate the optimization of ion concentration and its impact on mechanical sensitivity and overall device performance.\n6. All quantitative data should include error bars and appropriate statistical analysis to demonstrate data reliability and reproducibility.\n7. The application of the sensor for VR control lacks sufficient technical validation. Although multi-DOF sensing is claimed, the directional sensing mechanisms along different axes are not clearly described. The relationship between mechanical inputs (e.g., bending angle, motion speed) and signal outputs should be clarified. The authors should include a detailed signal mapping process, accompanied by kinematic diagrams or response maps to illustrate how physical deformations translate into virtual movements.\n8. Multiple inconsistencies are found between figures and their corresponding captions. The authors should carefully review and revise all figures and captions for consistency. In addition, several figures and tables are not sufficiently discussed in the main text. The manuscript should be revised to ensure all visuals are explicitly referenced and clearly interpreted.\n- Page 3, Line 74: Reference 33 is not relevant to TPU.\n- Page 4, Line 133: Reference 40 does not pertain to Li⁺ diffusion kinetics.\n- Figure 1b(iii) is not explained in the manuscript.\n- Figure S3: The graph legend shows LiCl, while the caption refers to Li(TFSI).\n- Figure 2: Mismatch exists between the figure content and caption.\n- Page 10, Line 271 (caption of Figure 3): The label ‘c’ should be corrected to ‘e’.\n- Figure 4: Caption does not correspond accurately to the figure.\n- Figures 5b(i) to 5b(viii): No explanation is provided in the main text.\n- Table S2 is not discussed in the manuscript.\n- In Table S2, \"Srange\" should be corrected to \"Range\"."}, "author_response": "Response to Reviewers’ Comments\nWe would like to appreciate the editor and reviewers for their highly constructive\nremarks in concerning our manuscript. In response to these comments and suggestions,\nwe supplemented additional experiments, calculations, and discussions to deepen our\nunderstanding and improve the quality of the manuscript. We believe that all the\nconcerns raised by the editor and reviewers have been addressed appropriately in the\nrevision.\nPoint-by-point response\nReviewer 1:\nComments: This paper presents an intriguing design of a soft piezoionic multi-degree-\nof-freedom (Multi-Dof) flex-sensor via a rational pairing of zwitterionic hydrogel and\norganic salt. To resolve the long-standing issue of low piezoionic output, the SPIM flex-\nsensor tactfully integrates ion-dipole interactions and steric hindrance effects to enable\nfast ion channels and amplified cation/anion mobility, synergistically contributing to a\nrecord-high flex-sensitivity of up to 8.6 mV/degree. In addition, it devised a unique\nsquare prism-shaped sensor structure with engineered nanofibrous electrodes to\ndecouple multi-Dof joint motions using a single compact sensor. Such high sensitivity\nand multi-Dof recognition ability of the SPIM flex sensor allowed the demonstration of\na soft, self-powered and wearable motion-capture platform, which showcased metaverse\napplications including a digital-twin of free-pose hand motions and VR control via\nsingle-joint dexterity through a single sensor. This work contains high scientific novelty\nas it for the first time fused intricate hydrogel-ion interactions in a piezoionic sensing\nsystem to boost piezoionic sensitivity, and corroborated the synergistic modulation\nthrough theoretical simulations combing poroelastic mechanics and Poisson-Nernst-\nPlanck model. Considering the ingenious cation/anion mobility manipulations and the\nwearable motion-capture VR applications of wide interest, I here recommend the\npublication of this work in Nature Communications after some revision. Please see as\nfollowing:\nReply: We sincerely appreciate your recognition of the scientific innovation in our work\nby commenting, “This work contains high scientific novelty as it for the first time fused\nintricate hydrogel-ion interactions in a piezoionic sensing system to boost piezoionic\nsensitivity”. More importantly, the detailed questions and suggestions are highly\nconstructive, accordingly we have supplemented more experiments or clarifications to\nsupport our work, briefly listed below:\n(1) the key parameters that dominate the electromechanical performance of the\nstretchable electrodes;\nquantitative adhesion testing data of the PSA-Li(TFSI) system and the underlying\nmechanism of the adhesion optimization;\n(2) integrating simulations into a coherent cross-scale framework ranging from\nmolecular scale to device scale to elucidate the synergistic polymer-ion interactions;\n(3) discussion about the difference in ionic modulation between our SPIM flex-sensor\nand other reported piezoionic sensors, and performance comparison with other sensing\nmechanisms;\n1\n(4) the structural and material features that account for the markedly superior\nperformance of the eTPU-AgNP nanomesh electrode;\n(5) extending from single-joint monitoring to multi-joint deployment (wrist, elbow, and\nshoulder) and the monitoring of more complex combination of movements, such as the\n“arm raising and hand waving” motion.\nWhat’s more, we also have supplemented:\n(1) a detailed and in-depth mechanism explanation of the fundamental principles\ngoverning the sensor operation;\n(2) comprehensive performance validation including hydrogel-electrode compatibility,\nhydrogel species/concentrations optimization, electrode choice;\n(3) a detailed technical validation to illustrate how physical deformations translate into\nvirtual movements.\nWe hope these supplemented experiments and mechanism analysis can make our\npaper more suitable for publication in this journal.\nQuestion 1: The manuscript does not provide sufficient details on the fabrication of the\nelectrospun electrodes. What are the key parameters that dominate the electromechanical\nperformance of the stretchable electrodes?\nReply: Thank you for this good question.\nFor the question of “The manuscript does not provide sufficient details on the\nfabrication of the electrospun electrodes”, we have supplemented a brief discussion of\nthe electrode preparation in the revised Manuscript (paragraph 1, page 9), and also a\nschematic diagram of the fabrication process (Figure R1) with detailed description in\nthe experimental section of the revised Manuscript (paragraph 3, page 17) as below:\nRevised Manuscript (paragraph 1, page 9): “The eTPU-AgNP nanomesh electrode was\nfabricated by combining the techniques of electrospinning and vacuum evaporation\ndeposition to achieve a uniform nanofibrous network with conductive coating (Figure\nS19, fabrication details in Experimental section).”\nRevised Manuscript (paragraph 3, page 17): “The detailed fabrication procedure of the\neTPU-AgNP nanomesh electrode is as follows: (i) Thermoplastic polyurethane (TPU)\nwas dissolved in the solvent of N, N-dimethylformamide (DMF), with the polymer\nconcentration and viscosity adjusted to achieve stable jet formation. (ii) The prepared\nsolution was loaded into a syringe and ejected through a metallic needle under a high-\nvoltage electric field. Electrospinning parameters were controlled, including applied\nvoltage (10-16 kV), solution feeding volume (0.2-2 mL), flow rate (0.5-1.0 mL h⁻¹), and\ntip-to-collector distance (15-20 cm). (iii) Electrospun nanofibers were collected on a\nrotating drum collector, where the rotation speed (100-300 rpm) was adjusted to control\nfiber alignment and network density. (iv) AgNPs were deposited onto the fibrous mats by\nvacuum evaporation deposition. The deposition thickness was precisely controlled to\nachieve an optimal balance between high electrical conductivity and mechanical\nflexibility.”\nRevised Supporting Information (Figure S19)\nelectrode combing electrospinning and vacuum evaporation deposition.\nFor the question of “What are the key parameters that dominate the\nelectromechanical performance of the stretchable electrodes”, we have implemented\ncomprehensive investigation on how the fabrication parameters affect the\nelectromechanical performance of the stretchable electrodes, and the experimental\nresults showed that three key parameters in fabrication determined the electromechanical\nperformance and the resulting sensing performance, including the applied voltage, the\nsolution volume for electrospinning, and the amount of Ag for deposition. The\ndetailed influences of these parameters on the electromechanical/sensing performance\nwere discussed as below:\n(1) The applied voltage in electrospinning\nThe applied voltage during electrospinning directly regulates the stretching and\nsolidification of the polymer jet, thereby determining fiber diameter and network density.\nAs the voltage increased from 10 to 16 kV, the fiber diameter gradually decreased from\n~2.0 to ~0.7 µm as shown in Figure R2a. At 10 kV, insufficient stretching produced\ncoarse and loosely packed fibers. At 16 kV, it yielded excessively thin fibers with more\njunctions between fibers. According to the electromechanical and sensing performance,\nan optimal condition was at 12 kV, wherein fibers with uniform diameters of ~1.6 µm\nwere produced, forming a dense and continuous network. The eTPU-AgNP nanomesh\nelectrode exhibited a low resistance of ~0.65 Ω·cm-1 (Figure R2b), a minimal\nresistance increase upon bending deformation (Figure R2c), and the highest potential\noutput compared with those of other applied voltages (Figure R2d).\nelectromechanical/sensing performance. (a) SEM images of electrospun nanofibers\nunder varying applied voltages: (i) 10 kV, (ii) 12 kV, (iii) 14 kV, and (iv) 16 kV. (b)\nElectrical resistance of the eTPU-AgNP nanomesh electrodes at different voltages. (c)\nRelative resistance change (ΔR/R₀) of eTPU-AgNP nanomesh electrodes under cyclic\nbending. (d) Output sensing potential of eTPU-AgNP nanomesh electrodes under cyclic\nbending.\n(2) The solution volume for electrospinning\nThe solution volume primarily determines the overall thickness and packing density\nof the nanofibrous film. As shown in Figure R3a, when the volume increased from 0.2\nto 2 mL, the resulting film thickness increased from ~10 to ~30 µm. For the thin film\n(0.2 mL, ~10 µm), the sparse nanofibrous network resulted in fragile networks under\nmechanical deformation, leading to poor electromechanical stability. For thick films (2\nmL, ~30 µm), the adhesion of the eTPU-AgNP nanomesh electrode to the ionic gel\ndeteriorated, which reduced the output potential. An optimal solution volume was found\nat 1 mL with a thickness of ~25 µm. The corresponding eTPU-AgNP nanomesh\nelectrode provided the highest electrical conductivity (Figure R3b) and a minimal\nresistance increase upon cyclic bending deformation (Figure R3c), resulting in the\nhighest output potential in sensing (Figure R3d).\nelectromechanical/sensing performance. (a) Thickness of electrospun electrodes at\ndifferent precursor solution volume (0.2, 0.5, 1, and 2 mL). (b) Electrical resistance of\nthe eTPU-AgNP nanomesh electrodes with varying solution volumes. (c) Relative\nresistance change (ΔR/R₀) of eTPU-AgNP nanomesh electrodes under cyclic bending.\n(d) Output sensing potential of eTPU-AgNP nanomesh electrodes under cyclic bending.\n(3) The amount of Ag for deposition\nThe amount of the Ag in vacuum evaporation deposition plays a decisive role in\nforming uniform and continuous conductive coating at the surface of nanofibrous\npolymer network. With a low deposition amount (1 Ag particle, each particle weighs\n0.158 g), the AgNP coating was insufficient to form a highly percolated conductive\nnetwork, giving a high electrical resistance of ~1000 Ω·cm-1. With a large deposition\namount (4 Ag particles), the thickness of the conductive coating increased (Figure R4a)\nand the electrical resistance decreased to ~0.5 Ω·cm-1 (Figure R4b). However, the\nagglomeration of AgNPs became pronounced, which increased surface roughness and\nstiffness of the conductive coating, leading to degraded electromechanical/sensing\nperformance. An optimal Ag amount of 3 particles was determined based on a minimal\nresistance increase upon cyclic bending deformation (Figure R4c), and a highest output\npotential in sensing (Figure R4d).\nelectromechanical/sensing performance. (a) Thickness of AgNP coating layer at\ndifferent usage amount of Ag particle (2, 3, and 4 Ag particles). (b) Electrical resistance\nof the eTPU-AgNP nanomesh electrodes at different usage amount of Ag particle. (c)\nRelative resistance change (ΔR/R₀) of eTPU-AgNP nanomesh electrodes under cyclic\nbending. (d) Output sensing potential of eTPU-AgNP nanomesh electrodes under cyclic\nbending.\nAs suggested, we have added a discussion about the optimized parameters in\nmanufacturing eTPU-AgNP nanomesh electrodes to improve the electromechanical and\nsensing performance in the revised Manuscript (paragraph 1, page 9) and the revised\nSupporting Information (Figure S20 to Figure S22, page 40 to 42):\nRevised Manuscript (paragraph 1, page 1): “We systematically investigated the\ninfluencing parameters in manufacturing the eTPU-AgNP nanomesh electrode with the\npurpose of promoting the electromechanical and sensing performance (Figure S20 to\nFigure S22), and the optimized fabrication parameters were determined to be applied\nvoltage of 12 kV and solution volume of 1mL in electrospinning, and Ag usage amount\nof 0.474 g in AgNP deposition.”\nRevised Supporting Information (Figure S20 to Figure S22, pages 40 to 42).\nQuestion 2: The author should provide quantitative adhesion testing data of the PSA-\nLi(TFSI) system to support a comprehensive comparison with those of PAM and PSA.\nAlso, the underlying mechanism for the observed adhesion optimization should be\nclarified.\nReply: Thanks for this good suggestion.\nFor the suggestion of “The author should provide quantitative adhesion testing\ndata of the PSA-Li(TFSI) system to support a comprehensive comparison with those\nof PAM and PSA”, we have conducted adhesion tests of PAM, PSA, and PSA-Li(TFSI)\nhydrogels on five representative substrates, including eTPU-Ag, glass, PET, PP, and\npigskin. Figure R5 compares the adhesive strength of the hydrogels to different\nsubstrates.\nFigure R5. Comparison of adhesive strength of PAM, PSA, and PSA-Li(TFSI) hydrogels\non various substrates.\nFor the suggestion of “Also, the underlying mechanism for the observed\nadhesion optimization should be clarified”: Specifically, PAM exhibits consistently low\nadhesion strength (~10 kPa) on all tested substrates due to its neutral polymer network.\nBy contrast, PSA shows markedly enhanced adhesion (10~20 kPa), primarily due to the\nelectrostatic interactions and hydrogen bonding between the sulfonate (-SO₃-) groups\nand polar surfaces. Remarkably, PSA-Li(TFSI) achieves the highest adhesion strength\n(20~30 kPa). The introduction of Li⁺ and TFSI- ions into the zwitterionic PSA matrix\nenables ion-dipole interactions, which may generate a stronger cohesive network with\nmore efficient energy dissipation during adhesive peeling.\nAs suggested, we have added a discussion about the comparison of the adhesive\nperformance between the PAM, PSA, and PSA-Li(TFSI) hydrogels in the revised\nManuscript (paragraph 1, page 10) and the revised Supporting Information (Figure S29,\npage 45):\nRevised Manuscript (paragraph 1, page 10): “Compared with the PAM and PSA\nhydrogels, the PSA-Li(TFSI) exhibited markedly enhanced adhesion to different\nsubstrates owing to the electrostatic interaction offered by zwitterionic groups and the\nenergy dissipation effect from the ion-dipole interaction (Figure S29).”\nRevised Supporting Information (Figure S29, page 45).\nQuestion 3: The manuscript employs a range of simulation methods, including Density\nFunctional Theory, Radial Distribution Function and Mean Square Displacement\nanalyses from Molecular Dynamics, as well as finite element modeling using COMSOL.\nWhile each part individually offers useful insights, the overall presentation currently\nappears fragmented. To improve clarity and coherence, I suggest the author to provide a\nconcise and comprehensive summary of the purpose, scope, and logical sequence of\nthese simulations.\nReply: We appreciate this highly constructive suggestion.\nresults, we here clarified the purpose, scope, and logical sequence of each\ncomputational/simulation approach. By integrating these simulations into a cross-scale\nframework ranging from molecular scale to device scale, these simulations collectively\nserve a unified objective: elucidating how polymer-ion interactions synergistically\nregulate cation/anion migration imbalance to achieve an ultrahigh bending sensitivity of\nthe piezoionic flex-sensor.\n1. Molecular scale (Density Functional Theory, DFT):\nMolecular level simulation was aimed at understanding how Li⁺ exhibits migration\nadvantages in a zwitterionic polymer environment. DFT calculations quantified the\nbinding energies between Li⁺ and either SBMA side groups or water molecules, showing\nstronger Li⁺-SBMA interactions (-4.3 eV vs. -1.6 eV). Electrostatic potential mapping\nrevealed a more homogeneous charge distribution and lower energy barrier for Li⁺-\nSBMA complexes, energetically rationalizing the facilitated desolvation of Li⁺.\nThese results provided the essential parameters and mechanistic basis for subsequent dynamic\nsimulations.\n2. Mesoscopic scale (Molecular Dynamics, RDF and MSD):\nMesoscopic level simulation was to analyze ion transport dynamics by relating to\nenergetic origins. Radial distribution functions showed that O atoms from SBMA partly\nreplaced water molecules in the first solvation shell of Li⁺, directly validating the DFT\npredictions. Mean square displacement analysis further quantified this structural\nrearrangement: the Li⁺ diffusion coefficient increased from 4.3×10⁻⁶ to 5.5×10⁻⁶ cm²·s⁻¹.\nTemperature-dependent impedance spectroscopy experimentally confirmed the lowered\napparent activation energy for desolvation (56.2 vs. 73.1 kJ·mol⁻¹). Moreover, replacing\nLiCl with the bulkier LiTFSI restricted TFSI⁻ mobility within the porous cross-linked\nnetwork, thereby elevating the Li⁺ transference number from 0.40 to 0.85. This\nsignificantly amplified ion transport imbalance.\n3. Device scale (Finite Element Modeling, COMSOL Multiphysics):\nDevice level simulation was intended to directly link ionic transport imbalance to\nsensing performance. We employed finite element modeling that coupled poroelastic\nmechanics with Poisson-Nernst-Planck transport equations under bending deformation.\nThe simulated potential differences built across the hydrogel (-2, -10.1, and 19.4 mV)\nshowed excellent consistency with experimental measurements, both in polarity reversal\nand voltage amplitude enhancement. This verified a complete manipulating pathway:\nfrom the modulation of conductivity and transference number to ionic flux imbalance\nand finally the piezoionic sensing output.\nIn summary, the simulation approaches were integrated into a logically connected\ncross-scale framework: DFT revealed energetic origins, MD (RDF and MSD) analyzed\nionic transport dynamics, and finite element modeling explained the device-level output\nenhancement. Combined with the consistent experimental results, this cross-scale\nframework provides a clear explanation towards how the synergistic polymer-ion\nregulation significantly enhances the bending sensitivity of the piezoionic SPIM flex-\nsensor.\nAs suggested, we have added a concise discussion about the logical framework of\nthese simulation approaches in the revised Manuscript (paragraph 2, page 6) and a\n8\ndetailed exposition in the revised Supporting Information (Supplementary Note 6, page\n14):\nRevised Manuscript (paragraph 2, page 6): “These simulation approaches constitute a\ncross-scale framework ranging from molecular scale to device scale, which illustrates\nhow the polymer-ion interactions synergistically regulate ion migration to achieve an\nultrahigh piezoionic sensitivity (detailed exposition in Supplementary Note 6): The\nmolecular-level DFT calculation exhibited the energetically favorable binding of\nzwitterionic side chain to the Li+ ion over water molecules; These energetic parameters\nsupported the mesoscopic-level MSD simulation of the ion transport dynamics, verifying\nthe promoted Li+ diffusion coefficient; Device-level simulation employed FEM to\ntransform the ion transport imbalance to the piezoionic output potential, explaining the\nvaried sensing performance between different polymer-ion combinations.”\nRevised Supporting Information (Supplementary Note 6, page 14).\nQuestion 4: Although this work proposed a new strategy to realize a record-high\npiezoionic sensitivity, I recommend to add discussion of previously reported high-output\nionic flex-sensors to illustrate the difference in ionic modulation.\nReply: Thank you for this valuable suggestion.\nPreviously reported high-output ionic flex-sensors were mostly based on hydrogels\ncontaining inorganic salts, or polymer network with asymmetric ion-transport\narchitecture. Although these approaches improved potential output to some extent, there\nsensitivity were generally limited either by the insufficient ionic conductivity or the\ninsignificant mobility imbalance between cations and anions. Unlike previous piezoionic\nsensors that primarily relied on enlarging ion-size disparity or introducing structural\nasymmetry, our polymer-ion modulation simultaneously united enhanced ionic\nconductivity through zwitterionic coordination and amplified ionic mobility imbalance\nowing to the adoption of bulky organic anions. The proposed synergistic strategy\nrepresents a mechanistic distinction in ionic modulation, highlighting the originality of\nour work.\nTo further underscore the ingenious ionic modulation for sensing performance, we\nalso added a comprehensive performance comparison (sensitivity, sensing range,\nstability, degree of freedom) between our piezoionic flex-sensor and reported flex-sensors based on other sensing mechanisms,\nincluding resistive, capacitive, piezoelectric, triboelectric, and fiber-optic types\n(Table S2 in the revised Supporting Information). Our\nSPIM flex-sensor shows a marked advantage considering the sensing range and cyclic\nstability (Figure R6).\nand flex-sensors of other mechanisms.\nAs suggested, we have added a discussion about the difference in ionic modulation\nbetween our SPIM flex-sensor and other reported piezoionic sensors, and also a\ncomprehensive performance comparison with flex-sensors of other sensing mechanisms\nin the revised Manuscript (paragraph 3, page 6) and the revised Supporting Information\n(Figure S10, page 35):\nRevised Manuscript (paragraph 3, page 6): “Unlike previous piezoionic sensors that\nprimarily relied on enlarging ion-size disparity or introducing structural asymmetry, our\npolymer-ion cooperative modulation simultaneously united enhanced ionic conductivity\nthrough zwitterionic coordination and amplified ionic mobility imbalance owing to the\nadoption of bulky organic anions.”\n“A comprehensive performance comparison between our piezoionic flex-sensor and\nreported flex-sensors based on other sensing mechanisms were conducted, including\nresistive, capacitive, piezoelectric, triboelectric, and fiber-optic types (Table S2). Our\nSPIM flex-sensor shows a marked advantage in terms of the sensing range and cyclic\nstability (Figure S10).”\nRevised Supporting Information (Figure S10, page 35).\nQuestion 5: Figure S10 presented three types of flexible electrodes including PDMS-\nAgNP film, TPU-AgNP film, and eTPU-AgNP nanomesh. What are the structural or\nmaterial features that account for the markedly superior performance of the eTPU-AgNP\nnanomesh electrode compared with the others?\nReply: Thanks for this insightful question.\nThere are three structural or material features of the eTPU-AgNP nanomesh\nelectrode which account for the superior performance: (i) the nanofibrous porous eTPU\nscaffold; (ii) the anchoring of the percolated AgNP network; (iii) the strong hydrogel-\nelectrode interfacial bonding. We extend the discussion of these features as below:\n(i) The nanofibrous porous eTPU scaffold\nThe mechanical softness and compliance of electrode is critical to accommodate\nthe bending deformation for the flex-sensor. In terms of mechanical properties, tensile\nstress-strain measurement (Figure R7) shows that eTPU-AgNP nanomesh electrode\npossesses a low elastic modulus of 1.6 MPa and large stretchable range of 100% strain.\nlocal stress by adaptive fiber rotation, effectively endowing the electrode with\nmechanical softness and compliance. This design ensures long-term structural integrity\nunder repeated bending, stretching, or twisting.\nFigure R7. Tensile stress-strain curve of eTPU-AgNP nanomesh electrode.\n(ii) The anchoring of the percolated AgNP network\nTo ensure a stable piezoionic output, the electrode should provide a stable electrical\nperformance during the frequent bending deformation. We compared the initial\nresistance and the electromechanical robustness among the three alternative electrodes.\nThe initial resistance of eTPU-AgNP nanomesh electrode is 0.65 Ω cm⁻¹, much lower\nthan those of TPU-AgNP film (1.13 Ω cm⁻¹) and PDMS-AgNP film (2.9 Ω cm⁻¹). Cyclic\nbending tests (Figure R8) showed that the relative resistance change (ΔR/R₀) of eTPU-\nAgNP nanomesh electrode was minimal and highly stable, indicating superior electrical\nreliability. For the eTPU-AgNP nanomesh electrode, AgNPs were uniformly deposited\nand anchored on surface of the 3D interconnected porous scaffold, forming a stable\npercolated conductive network. This structure not only forms continuous electrical\npathways, but also improves electrical robustness by accommodating enforced\ndeformation through fiber rotation and reorientation. Such adaptive mechanics\nsuppresses crack initiation and propagation, while also preventing AgNPs detachment\nduring cyclic deformation, which is a common failure mode observed in dense film\nelectrodes (TPU-AgNP film and PDMS-AgNP film).\nfilm and PDMS-AgNP film. (b) The resistance variation of eTPU-AgNP nanomesh\nelectrode, TPU-AgNP film and PDMS-AgNP film under cyclic bending deformation.\n(iii) The strong hydrogel-electrode interfacial bonding\nA strong hydrogel-electrode interfacial bonding is an essential prerequisite for the\nstable ionic-to-electronic signal transformation at the electrical double layer interface.\nThe open nanomesh architecture of eTPU-AgNP nanomesh electrode provides a\nmarkedly increased surface area and surface roughness, which facilitates the infiltration\nof soft hydrogel into the porous electrode surface to form geometric interlocking, thereby\ngreatly enhancing interfacial adhesion. As shown in confocal microscopy interfacial\nimages (Figure R9), there were pronounced gaps at the interfaces between hydrogel and\nPDMS-AgNP or the TPU-AgNP films due to the poor mechanical compliance. By\ncontrast, the eTPU-AgNP nanomesh electrode provided a tight interface with the\nhydrogel owing to the softness and nanofibrous porous electrode surface. Frequency-\ndependent impedance spectra (Figure R10(a)) revealed that eTPU-AgNP nanomesh\nelectrode consistently exhibited the lowest impedance across the full frequency range,\nindicating a significant reduction in interfacial impedance. This behavior reflects a more\nefficient charge transport pathway across the hydrogel-electrode interface. Interfacial\nadhesion strength tests (Figure R10(b)) showed that eTPU-AgNP nanomesh electrode\ndisplayed much higher interfacial strength than TPU-AgNP and PDMS-AgNP films.\nThis enhanced adhesion strength effectively prevents the bucking of the electrode, which\nallows a tight bonding at the hydrogel-electrode interface.\nAgNP electrode, (b) TPU-AgNP electrode and (c) eTPU-AgNP nanomesh electrode.\nFigure R10. (a) Interfacial impedance spectra of eTPU-AgNP nanomesh, TPU-AgNP\nfilm, and PDMS-AgNP film. (b) Interfacial adhesion strength tests of PSA-Li(TFSI)\nhydrogel attached to electrodes of eTPU-AgNP nanomesh, TPU-AgNP film, and PDMS-\nAgNP film.\nThese combined merits of the eTPU-AgNP nanomesh electrode collectively\nresulted in a more sensitive and stable piezoionic signal than those of TPU-AgNP film\nand PDMS-AgNP film. The piezoionic output in the bending range of 0-180 degree in\nFigure R11 and the time-resolved potential output under a step bending deformation in\nFigure R12 verified a highest flex-sensitivity and most stable signal of the eTPU-AgNP\nnanomesh electrode in comparison with TPU-AgNP film and PDMS-AgNP film.\nnanomesh, TPU-AgNP film, and PDMS-AgNP film. The flex-sensor using eTPU-AgNP\nnanomesh electrode shows a highest flex-sensitivity across the 0-180 bending degree.\nFigure R12. The time-resolved potential output under a step bending deformation of (a)\neTPU-AgNP nanomesh, (b) TPU-AgNP film, and (c) PDMS-AgNP film. The flex-\nsensor using eTPU-AgNP nanomesh electrode shows a most stable signal under a step\ninput of bending deformation.\nTo underscore the structural and material features that account for the markedly\nsuperior performance of the eTPU-AgNP nanomesh electrode, we supplemented a\ndiscussion to analyze the advantages of the nanofibrous film electrode in the revised\nManuscript (paragraph 1, page 9) and the revised Supporting Information (Figure S23-\nS27, page 43-45):\nRevised Manuscript (paragraph 1, page 9): “The eTPU-AgNP nanomesh outperforms\nthe TPU-AgNP film and PDMS-AgNP film as a flexible electrode in flex-sensor, as it\nexhibits not only a low initial resistance of 0.65 Ω cm⁻¹, but also a more stable resistance\nunder bending deformation than those of the TPU-AgNP film and PDMS-AgNP film\n(Figure S23). Cyclic bending tests further confirmed the electromechanical robustness\nof the eTPU-AgNP nanomesh electrode (Figure 2e). We attributed the superb electrical\nresilience to the porous microstructure of the nanofibrous eTPU scaffold and the\n14\nanchoring of the percolated AgNPs network on the fiber surface (Figure 2e and 2f),\nwhich could accommodate the bending strain via fiber deflection and reorientation,\nthereby preserving the percolation network. Besides, the eTPU-AgNP nanomesh\nexhibited a strongest adhesive strength to the hydrogel to facilitate a tight and gap-free\nbonding, which accounted for a low and stable interfacial impedance of the hydrogel-\nelectrode interface (confocal microscopy images of electrode-hydrogel interfaces in\nFigure S24 and interfacial impedance spectra in Figure S25). These combined merits of\nthe eTPU-AgNP nanomesh electrode collectively resulted in a more sensitive and stable\npiezoionic signal than those of TPU-AgNP film and PDMS-AgNP film (Figure S26 and\nFigure S27).”\nRevised Supporting Information (Figure S23-27, page 43-45).\nQuestion 6: The author showed the single-sensor efficacy in multi-Dof motion-capture\nof a single joint for metaverse applications. How does this approach perform when\nextended to multi-joints use, e.g., multiple sensors equipped to full-hand or full-body for\nmotion tracking?\nReply: Thanks for this good question.\nTo demonstrate the versatility in multi-joint use when extended to full-hand/full-\nbody motion tracking, we further extended the deployment of our SPIM flex-sensors\nfrom wrist joint to elbow and shoulder joints, which involved larger angular\ndisplacement and multi-directional motions. As the proposed sensing strategy of multi-\njoint motion capture is inherently a modular design, which means each flex-sensor\noperates independently to monitor a given joint by providing stable and high-fidelity\npiezoionic potential signal. Figure R13 shows the multi-joint deployment (wrist, elbow,\nand shoulder) of SPIM flex-sensor, the typical output signal of multi-Dof motions, and\nthe motion capture in Unity space. More importantly, the combined multi-joint\ndeployment could empower the monitoring of more complex combination of movements,\nsuch as the “arm raising and hand waving” motion as shown in Figure R14. This modular\ndeployment allows the system to monitor different joints without interference, enabling\nsynchronized and extensible motion capture scenarios.\nFigure R13. The multi-joint deployment of SPIM flex-sensors at (a) wrist, (b) elbow,\nand (c) shoulder joints, the typical output signal of multi-Dof motions, and the motion\ncapture in Unity space.\ncapture of “arm raising and hand waving” movements combination in Unity space.\nAs suggested, we have supplemented the discussion about the multi-joints\nmonitoring in the revised Manuscript (paragraph 2, page 13) and the revised Supporting\nInformation (Figure S31-S32, page 46-47):\nRevised Manuscript (paragraph 2, page 13): “To showcase the versatility of the SPIM-\nflex sensor in multi-joint use, we extended the deployment from wrist joint to elbow and\nshoulder joints, which also involved multi-Dof motions and even larger angular\ndisplacement. The deployment of SPIM flex-sensor, the typical output signal of multi-\nDof motions, and the corresponding motion capture in Unity space were shown in Figure\nS31. This extensible approach allows the monitoring of different joints without\ninterference, enabling synchronized and more complex motion capture applications,\nsuch as the “arm raising and hand waving” movements (Figure S32).”\nRevised Supporting Information (Figure S31, Figure S32, page 46-47).\nQuestion 7: Please check the inconsistencies in the labeling and captions of Figures 2,\nS3 and S5.\nReply: Thank you for the careful reminding of the inconsistencies.\nWe have checked the labels and captions of Figure 2, Figure S3 and Figure S5, and\nmade corrections accordingly. We also thoroughly went through the Manuscript and the\nSupporting Information to check all the labels and captions of other figures to make sure\nthere are no mislabeling.\nComments: This manuscript presents a piezoionic soft flex sensor composed of a\nzwitterionic hydrogel integrated with an ionic liquid (Li(TFSI)). The sensor exploits ion\nsize-induced charge imbalance under mechanical deformation (i.e., bending) to achieve\na sensitive piezoionic response. The primary contributions include: (1) enhanced\nflexural sensitivity through synergistic interactions between the hydrogel matrix and\nmobile ions, as well as robust electrostatic coupling at the hydrogel-electrode\ninterface, and (2) multi-degree-of-freedom (multi-DOF) sensing capability enabling hand motion\ndetection and integration with virtual reality (VR) control systems. However, the\nmanuscript lacks sufficient mechanistic depth and comprehensive performance\nvalidation. In addition, the central claims are not adequately supported by experimental\nevidence, and the manuscript contains several technical and interpretational errors.\nDetailed comments are provided below.\nReply: Thank you for the insightful and informative comments, and we really appreciate\nthese critical yet highly valuable suggestions of “lacks sufficient mechanistic depth and\ncomprehensive performance validation”, “the central claims are not adequately\nsupported by experimental evidence”, and “contains several technical and\ninterpretational errors”.\nTo address these concerns, we have supplemented more experiments or conducted\nin-depth mechanistic analysis to solidify and improve this work, briefly including:\n(1) a detailed and in-depth mechanistic analysis of the multi-Dof piezoionic sensing,\nincluding (i) the physical process of the cross-scale mechano-ionic-electrostatic\ncoupling, (ii) piezoionic equivalent circuit model, (iii) comparative analysis of\ndifferent electrodes in terms of equivalent circuit parameters, (iv) finite element\nsimulations of the mechano-ionic-electrostatic coupling, and (v) analysis of the\nmulti-Dof bending (direction and amplitude) based on piezoionic output;\n(2) mechanistic analysis and supplementary experiment to explain the significantly\nhigher Li+ transference number of PSA-Li(TFSI) compared with that of PSA-LiCl;\n(3) comprehensive experimental data to justify the electrode selection, including the\nmechanical properties, the interfacial adhesion between the hydrogel and the\nelectrode, the interfacial impedance at the hydrogel-electrode interface, and the\npiezoionic sensing performance using different electrodes.\n(4) comprehensive experimental data to substantiate the hydrogel superiority of PSA-\nLi(TFSI) compared with control hydrogels of PAM-LiCl, PAM-Li(TFSI), and PSA-\nLiCl), including the mechanical properties, adhesive strength, ionic conductivity,\npiezoionic sensitivity and dynamic response.\n(5) the optimization of Li(TFSI) concentration in PSA-Li(TFSI) and its impact on\nmechanical, electrical, and overall device performance.\n(6) technical validation of the multi-Dof VR control, including (i) physical mechanism\nand mathematical model of the multi-Dof bending sensing, (ii) calibration of the\nquantitative mapping from mechanical input to electrical output, and (iii) workflow\ndescription from physical deformations to virtual movements.\nBesides, we have also supplemented:\n17\n(1) investigation of the key parameters that dominate the electromechanical\nperformance of the eTPU-AgNP nanomesh electrode;\n(2) quantitative adhesion testing data of the PSA-Li(TFSI) and the underlying\nmechanism of the adhesion optimization;\n(3) a coherent cross-scale framework ranging from molecular scale to device scale to\nelucidate the synergistic polymer-ion interactions;\n(4) discussion about the difference in ionic modulation between our SPIM flex-sensor\nand other reported piezoionic sensors, and performance comparison with other\nsensing mechanisms;\n(5) the structural and material features that account for the markedly superior\nperformance of the eTPU-AgNP nanomesh electrode;\n(6) extending from single-joint monitoring to multi-joint deployment (wrist, elbow, and\nshoulder) and the realization of more complex combination of movements, such as\nthe “arm raising and hand waving” motion.\nThe detailed revisions were elaborated in the point-to-point responses. Again, we\nreally appreciate these highly constructive questions, and hope the revisions can make\nour work suitable for publication in this journal.\nQuestion 1: The manuscript lacks sufficient mechanism explanation of the fundamental\nprinciples governing the sensor operation. Specifically, the mechanism of ion migration\nunder multi-directional mechanical deformation, which is central to the multi-DOF\npiezoionic sensing behavior, is insufficiently described. Moreover, the fundamental roles\nand performance characteristics of flexible electrodes are not clearly discussed. To\nimprove scientific clarity, the authors should provide a detailed mechanism supported\nby schematic illustrations and a comprehensive explanation.\nReply: Thank you for this good question and we sincerely appreciate these critical\nsuggestions. To provide sufficient mechanism explanation of the fundamental\nprinciples governing the sensor operation, we supplemented a detailed and in-depth\ndiscussion of the multi-Dof piezoionic sensing mechanism, and the items are briefly\nlisted here (detailed content followed in next section):\n(1) Sensing mechanism of the piezoionic SPIM flex-sensor, which describes the\ncross-scale mechano-ionic-electrostatic coupling process: a strain gradient field first\ndrives an imbalanced ionic flux due to the polymer-ion modulation and ion size\ndifference, and then the ionic flux transforms into electronic output signal at the\nhydrogel-electrode interface via the electrical double layer (EDL) charging.\n(2) Piezoionic equivalent circuit model: to analyze the correlation between the\nexternal mechanical excitation and the electrical signal generation, piezoionic equivalent\ncircuit model coupling a mechanically driven current source and electrochemical\nimpedance elements was established.\n(3) Comparative analysis between different electrodes in terms of equivalent\ncircuit parameters: the analysis of the equivalent circuit parameters for alternative\nelectrodes (eTPU-AgNP nanomesh, TPU-AgNP film, and PDMS-AgNP film) and the\neffects on the ionic conduction, interfacial polarization, and piezoionic output.\n(4) Finite element simulations of the mechano-ionic-electrostatic coupling: to\nbetter understand the mechano-ionic-electrostatic coupling process, we performed finite\nelement simulations by combining poroelastic mechanics and Poisson-Nernst-Planck\n18\nmodel, revealing the coupling relationship between deformation geometry and\nmultiphysical responses, including the distributions of pore pressure, ionic concentration,\nand electric potential.\n(5) Analysis of the multi-Dof bending (direction and amplitude) based on\npiezoionic output: for axial bending or bending around arbitrary direction, the bending\namplitude (or bending curvature) and bending direction can be deduced from the\npiezoionic potential output of the two pairs of electrodes in X- and Y-axis.\nThe above discussions have addressed the concerns raised in Question 1,\nincluding “sufficient mechanism explanation of the fundamental principles governing\nthe sensor operation”, “the mechanism of ion migration under multi-directional\nmechanical deformation”, and “the fundamental roles and performance characteristics\nof flexible electrodes”.\nThe detailed and in-depth discussion of the multi-Dof piezoionic sensing\nmechanism are as below:\n1. Sensing mechanism of the piezoionic SPIM flex-sensor\nThe piezoionic signal generation can be described as a cross-scale process: a strain\ngradient field across the zwitterionic hydrogel thickness first generates a hydrostatic\nstress gradient to drive an imbalanced ionic flux due to the polymer-ion modulation and\nion size difference, and then the ionic flux (i.e., ionic current/potential) transforms into\nelectronic output signal at the hydrogel-electrode interface via the electrical double layer\n(EDL) charging.\n1.1 Bending deformation generates strain gradient in the zwitterionic hydrogel\nDuring bending deformation, for the homogeneous and isotropic zwitterionic\nhydrogel, we can reasonably assume a linearly varying strain field across the thickness\nof the hydrogel by adopting the Euler-Bernoulli beam theory[1], which makes a plane-\nsection assumption that plane cross-sections remain plane and perpendicular to the\nneutral axis after deformation and bending deformation is dominated by axial stretching\nand compression, while shear deformation is negligible.\nAs shown in Figure R1(a), z denotes the coordinate along the beam thickness, x the\nbeam axis, and w(x) the transverse deflection. The axial displacement located at z is u(x,\nz), and the axial strain is defined as\n𝜕𝑢\n𝜀 = \n𝑥 𝜕𝑥\nWhen the hydrogel bends, the cross-section rotates by an infinitesimal angle θ(x).\nFrom the geometric relation illustrated in Figure R1(b), the infinitesimal elongation or\nshortening at position z is\n𝑑𝑢 = −𝑧𝑑𝜃\nHence, the axial strain becomes\n𝜕𝑢 𝑑𝜃\n𝜀 = = −𝑧\n𝑥 𝜕𝑥 𝑑𝑥\nThe rotation angle θ is related to the slope of the deflection curve w(x) by\n𝑑𝑤\n𝑡𝑎𝑛𝜃 = ≈ 𝜃\n𝑑𝑥\n19\nand the curvature is defined as\n𝑑𝜃 𝑑2𝑤\n𝜅 = = \n𝑑𝑥 𝑑𝑥2\nSubstituting the curvature definition into equation gives the linear strain\ndistribution along the hydrogel thickness:\n𝜀 (𝑧,𝑡) = −𝜅(𝑡)𝑧\n𝑥\nWhere 𝜀 (𝑧,𝑡) is the axial strain at position z and time t, 𝜅(𝑡) is the curvature at\n𝑥\ntime t, z is the vertical coordinate measured from the neutral axis. Consequently, bending\ndeformation generates a strain gradient along the thickness direction which develops\nfrom compressive to tensile strain. This strain gradient within the poroelastic hydrogel\nmatrix results in a hydrostatic stress gradient along the thickness direction, which serves\nas the primary driving source for ionic migration.\nFigure R1. (a) Navier’s hypothesis for the bending of zwitterionic hydrogel. (b) Rotation\nof the cross-section of hydrogel element.\n1.2 Strain gradient drives ionic migration\nWhen an external bending or stretching deformation is applied, a bending\ncurvature-dependent strain gradient is established across the hydrogel thickness. This\nstrain gradient gives rise to a spatially varying hydrostatic stress field σ , which in turn\nhyd\ninduces coupled gradients of chemical potential (∇ ) and electrostatic potential (∇𝜙). At\n𝜇𝑖\nthe continuum level, the migration of ionic species is governed by the coupled Poisson-\nNernst-Planck (PNP) and poroelastic framework2:\n𝑧 𝐷𝐹\n𝐽 =−𝐷∇𝑐 −\n𝑖 𝑖\n𝑐 ∇𝜙+𝑐 𝑣\n𝑖 𝑖 𝑖 𝑅𝑇 𝑖 𝑖\n∇ ≈𝑘 𝑇∇𝑙𝑛𝑐 +𝑧 𝜹𝜙+Ω ∇σ\n𝜇𝑖 𝐵 𝑖 𝑖 𝑖 hyd\nWhere J is the molar flux, D the diffusion coefficient, z the ionic valence, c the\ni i i i\nionic concentration, F the Faraday constant, R the universal gas constant, T the absolute\ntemperature in Kelvi, 𝜙 the electrostatic potential, v the convection velocity of the\nsolvent flux induced by poroelastic deformation, k B the Boltzmann constant, and 𝛺 𝑖 the\npartial molar volume that couples mechanical stress to chemical potential.\nWithin this framework, each term embodies a distinct driving force: (i) diffusion\n(−𝐷∇𝑐 ) driven by concentration gradients originating from strain-induced local density\n𝑖 𝑖\nchanges; (ii) electromigration (− 𝑧𝑖𝐷𝑖𝐹 𝑐 ∇𝜙) caused by the evolving internal electric field;\n𝑖\n𝑅𝑇\n20\nand (iii) convective transport (𝑐 𝑣) reflecting solvent flow and matrix deformation. The\n𝑖\nadditional mechanical term Ω ∇σ captures the direct coupling of hydrostatic pressure\n𝑖 hyd\nto the ionic chemical potential, linking mechanical strain to electrochemical potential\ndifferences.\nIn the specific PSA-Li(TFSI) hydrogel system, cation-anion transport asymmetry\nplays a decisive role. Lithium ions (Li⁺) experienced facile desolvation and strong\nzwitterionic coordination, yielding a significantly larger transference number than that\nof the sterically hindered TFSI⁻ anions (t(Li⁺)>t(TFSI⁻)). Consequently, bending-\ninduced strain gradient and hydrostatic stress gradient drive imbalanced ionic fluxes,\nleading to a net space-charge separation across the membrane.\n1.3 Ionic-to-electronic signal transformation at the EDL interface\nThe sensing principle of the piezoionic sensor is fundamentally governed by a\npiezoionic mechanism modulated by EDL coupling, in which mechanical deformation\ninduces ionic redistribution and dynamic reconfiguration of the interfacial EDL. This\nprocess represents a strong multiphysics coupling among mechanical stress, ionic\ntransport, and interfacial polarization, enabling cross-scale energy conversion from\nmechanical stimuli to electrical output.\nIn the undeformed state (Figure R2(a)), cations and anions are uniformly distributed\nwithin the hydrogel or ion-conducting polymer matrix. Stern type EDLs are established\nat both hydrogel-electrode interfaces. Each EDL consists of two characteristic regions,\nincluding the Stern layer (a compact, non-diffusive layer formed by specifically\nadsorbed counterions directly adjacent to the electrode surface, determining the\ninterfacial potential difference) and the diffuse layer (a thermally driven diffusion region\nextending into the electrolyte, where the ionic concentration decays exponentially\naccording to the Poisson-Boltzmann distribution). Under static equilibrium, the two\nEDLs are mirror-symmetric, and each electrode carries equal and opposite surface\ncharge densities. The system as a whole is electrostatically neutral, producing zero net\npotential difference and no measurable output potential.\nWhen the SPIM flex-sensor is bent (Figure R2(b)), a strain gradient develops across\nthe film thickness, tensile on the convex side and compressive on the concave side. This\nnonuniform strain distribution generates a spatial gradient of hydrostatic stress and\nchemical potential, which drives directional migration of mobile ions along the thickness\ndirection. Because cations and anions differ in mobility, ionic radius, solvation shell\nconfiguration, and coordination interactions with the polymer matrix, their responses to\nthe stress field are imbalanced. Consequently, a space-charge region forms within the\nhydrogel, breaking the initial electrochemical symmetry and perturbing the originally\nbalanced EDL configuration.\nDuring bending deformation, the EDLs at the hydrogel-electrode interfaces go\nthrough dynamic reconfiguration. Considering the low measured voltage and the\nmaterials choice in the SPIM flex-sensor, no faradaic charge transfer or electrochemical\nreactions occurred. On the convex (tensile) side, the tensile stress causes localized\nexpansion and promoted cations accumulation at the electrode surface. As a result,\nopposite charges appear at the electrode surface and build a negative potential which is\ndictated by the electrostatic interaction (Coulombic force). On the concave (compressive)\nside, the compressive stress leads to localized contraction and results in anions\nenrichment near the electrode interface. Similarly, a positive potential is established at\nthe nearby electrode. Consequently, the opposite charge densities on the two electrodes\n21\nproduce a measurable current or potential at the external circuit. When the external stress\nis released, ions diffuse back under concentration and potential gradients, restoring the\nuniform ion distribution and symmetric EDL structure. The entire process is reversible,\nnon-faradaic, reflecting the dynamic and recoverable nature of ion-electron interactions\nunder mechanical stimuli. In other words, the dynamic reconfiguration of EDLs gives\nrise to the ionic-to-electronic signal transformation at the capacitive interface.\nFigure R2. Schematic illustration of dynamic EDL reconstruction and the piezoionic\nsignal generation, including the (a) undeformed and (b) bending states.\n2. Piezoionic equivalent circuit model\nA piezoionic equivalent circuit model (shown in Figure R3) coupling a\nmechanically driven current source and electrochemical impedance elements was\nestablished to analyze the correlation between the external mechanical excitation and the\nelectrical signal generation[2].\nUnder bending deformation, pressure driven flow acts as ionic current source and\nthe induced ionic current 𝐼 is expressed as:\n𝑝\n𝐼 =𝛼𝜎∇𝑃∙𝐴\n𝑝\nwhere 𝛼 is the piezoionic coefficient, 𝜎 is the ionic conductivity, ∇𝑃 is the bending\ncurvature-induced pressure gradient along the hydrogel thickness, and A is the bending\nregion area perpendicular to the ionic flux direction.\nR (ionic resistance) represents the overall resistance to ion transport through the\ns\nhydrogel. C (ionic capacitance) denotes the capacitance for charge separation within the\ns\nhydrogel. The parallel configuration (R ∥C ) implies R governs the dissipative (resistive)\ns s s\nresponse, while C determines the electrostatic energy storage (capacitive) component.\ns\nAt the hydrogel-electrode interfaces, an EDL with double layer capacitance C develops.\ne\nFor Faradaic reactions at the electrodes, charge transfer resistance R represents the\ne\nactivation barrier of electrochemical reactions[3].\narea A. R is the ionic resistance of the sensor. C is the capacitance across the hydrogel.\nS S\nThe electrodes can charge, with capacitance C , and there can also be an charge transfer\ne\nresistance R .\ne\nThe equivalent circuit model represents the multi-physics coupling which bridges\nthe gap between the mechanical deformation and the piezoionic output signal. In practice,\nhowever, the capacitance across the hydrogel (C ) and the electrode capacitances (C )\ns e\nare strongly coupled and cannot be clearly separated, which both exhibit frequency-\ndependent, non-ideal capacitive behavior. Therefore, these two capacitive components\ncan be merged into a constant phase element (CPE), while the charge transfer resistance\n(R ) remains in parallel with it, resulting in a Randles-like equivalent circuit. The\ne\nRandles circuit is one of the most classical equivalent models in electrochemical\nimpedance spectroscopy (EIS) to describe the impedance characteristics of the\nelectrode-electrolyte interface[4]. Its basic configuration includes four main components:\n𝑍(𝜔) = 𝑅 +(𝑅 ∥ 𝐶 +𝑍 )\n𝑠 𝑐𝑡 𝑑𝑙 𝑊\nZ(ω) represents the total complex impedance of the system; R denotes the solution\ns\nresistance of the electrolyte; C is the double-layer capacitance formed at the electrode-\ndl\nelectrolyte interface; R represents the charge-transfer resistance; Z is the Warburg\nct w\nimpedance, describing diffusion-controlled process.\nIn EIS analysis, the Randles circuit was originally developed for systems involving\nsignificant charge-transfer reactions, typically producing semicircular Nyquist plots with\na diffusion tail at low frequencies. However, in the piezoionic sensing system, the ionic-\nto-electronic transformation follows a non-Faradaic process dominated by electrostatics,\nmeaning that no charge transfer or chemical reaction at the electrode-electrolyte\ninterface. Instead, mechanical deformation (such as bending or compression)\nredistributes mobile ions within the hydrogel, leading to a dynamic reconstruction of the\ninterfacial EDL. The rearranged ions generate a potential difference between electrodes,\nenabling mechanical-to-electrical signal conversion via ion-electron electrostatic\ninteractions rather than Faradaic electron transfer.\nIn impedance spectroscopy, such non-Faradaic systems exhibit capacitive behavior.\nHowever, the ideal capacitance assumption fails in real electrode-electrolyte systems\nbecause the interface is not an ideal planar capacitor, but deviates from ideal capacitive\n23\nbehavior due to various structural and physical factors, such as the electrode surface\nroughness and porosity, the nonuniform ion diffusion pathways, material\ninhomogeneities and local defects. These heterogeneities collectively give rise to a\nfrequency-dependent impedance response. To describe this non-ideal capacitive\nresponse, a Constant Phase Element (CPE) is introduced, with impedance defined as[5]:\n1\n𝑍 (𝜔) = ,0 < 𝑝 ≤ 1\n𝐶𝑃𝐸 𝑇(𝑗𝜔)𝑝\nwhere T is a pseudo-capacitance factor related to the magnitude of the capacitive\nresponse, and p is the phase exponent quantifying the deviation from ideal capacitance.\nWhen p = 1, the CPE behaves as an ideal capacitor; when p < 1, it represents distributed\nrelaxation or interfacial heterogeneity. This manifests as a depressed semicircle in the\nNyquist plot and a phase angle smaller than -90° in the Bode plot. Therefore, in\npiezoionic systems, the CPE effectively characterizes the combined effects of surface\nroughness, ion diffusion, or structural heterogeneity at the interface.\nThe conventional Randles circuit can be rationally modified to better represent the\nnon-Faradaic behavior of the system. First, C and C deviate from ideal capacitive\ns e\nbehavior due to interfacial heterogeneity and distributed ion transport. These effects are\ncollectively described by introducing a Constant Phase Element (CPE) to represent the\nnon-ideal capacitive response. Second, the Warburg impedance term is omitted, as no\nsignificant diffusion limitation occurs in this system. Finally, in the absence of Faradaic\ncharge transfer, the charge transfer resistance (R ) is substituted by a R , which serves\nct p\nas an analogue to R and represents the charge transfer resistance arising from non-\nct\nFaradaic. The equivalent circuit fitting was performed using ZView software, and the\nsimulated impedance curves all fit well with the experimental data (Figure R4(a)),\nthereby confirming the rationality and validity of this equivalent simplification.\nAccordingly, the impedance model is simplified to a modified Randles circuit (Figure\nR4(b)):\n𝑍(𝜔) = 𝑅 +(𝑅 ∥ 𝐶𝑃𝐸)\n𝑠 𝑝\nFigure R4. (a) Experimental and fitting Nyquist plots of the piezoionic sensors using\ndifferent flexible electrodes of eTPU-AgNP nanomesh, TPU-AgNP flim, and PDMS-\nAgNP film. (b) The modified Randles circuit to fit the EIS behavior of the piezoionic\nsensor.\ns\nthe conduction path of the piezoionic sensor. It consists of three main components: (1)\nBulk hydrogel resistance. This term arises from ionic migration through the hydrogel\nmatrix. It depends on factors such as ionic concentration, hydration level, and the\nmicrostructure of the polymer network. (2) Electrode-hydrogel contact resistance. At\nthe interface between the hydrogel and the electrode, imperfect adhesion, surface\nroughness, or trapped air bubbles introduce additional resistive components. This part\nreflects the interfacial coupling quality and the uniformity of current distribution. (3)\nElectrode resistance. The finite conductivity of the electrode material also contributes\nto R , particularly in flexible or stretchable electrodes which can increase significantly\ns\nunder tensile strain. In the Nyquist plot, R corresponds to the high-frequency intercept\ns\non the real axis. A larger R indicates lower ionic conductivity or poor interfacial contact,\ns\nleading to reduced piezoionic signal amplitude.\nIn piezoionic systems, part of the accumulated interfacial charge gradually\ndissipates through ionic migration and leakage. Hence, R can be understood as charge\np\nretention capability. A larger R indicates a higher resistance to charge leakage, implying\np\nthat interfacial charges are more effectively trapped, leading to stronger polarization\nstability and longer charge retention time.\nThe CPE is an empirical component used to represent non-ideal capacitive behavior\nat the electrode-electrolyte interface. In piezoionic sensors, it reflects interfacial double-\nlayer polarization, charge accumulation, and relaxation dynamics, serving as the main\ncontributor to the capacitive response. T is the magnitude factor, representing the\neffective capacitance intensity. T parameter represents the strength of interfacial\npolarization and charge storage capability. A larger T indicates stronger capacitive\nbehavior and higher charge accumulation, depending on interfacial area, dielectric\nconstant, and ionic concentration. p is the phase exponent to reflect the degree of\ninterfacial nonideality (0 < p≤1). As p approaches 1, the interface behaves more like an\nideal capacitor; smaller p values indicate enhanced roughness, porosity, or ionic\ndiffusion heterogeneity.\n3. Comparative analysis between different electrodes in terms of equivalent circuit\nparameters\nBased on the fitted experimental data (Figure R5), the piezoionic sensors using\ndifferent alternative electrodes (eTPU-AgNP nanomesh, TPU-AgNP film, and PDMS-\nAgNP film) exhibit distinct variations in their equivalent circuit parameters, reflecting\ndifferences in ionic conduction and interfacial polarization behavior.\nThe series resistance (R ) mainly represents the combined resistance of the bulk\ns\nhydrogel resistance, the hydrogel-electrode contact resistance, and the electrode\nresistance. Among the three electrodes, eTPU-AgNP nanomesh shows the lowest Rs\n(≈109.7 Ω), indicating a minimal voltage drop across the internal resistance to provide\na high piezoionic output. The charge transfer resistance (R ) reflects the ability of the\np\ninterface to resist leakage and retain charge. eTPU-AgNP nanomesh shows a moderate\nRp (≈6.4×103 Ω), indicating an intermediate charge retention ability. The constant phase\nelement (CPE) parameters T and p describe the interfacial polarization intensity and its\ndeviation from ideal capacitive behavior. eTPU-AgNP nanomesh exhibits a largest T\n(≈1×10-3 F·s(p-1)), indicating strongest interfacial energy-storage capability. Besides,\nthe three electrodes showed quite close p values varying from 0.59 to 0.63, reflecting a\nsimilar degree of interfacial structural homogeneity. Considering the general effects,\n25\neTPU-AgNP nanomesh electrode with the lowest R , highest T and moderate R ,\ns p\ndemonstrates the most efficient ionic conduction and ionic-to-electronic signal\ntransformation.\nFigure R5. EIS parameters including R , R , T and p by fitting with the modified Randles\ns p\ncircuit for piezoionic sensors using different electrodes of eTPU-AgNP nanomesh, TPU-\nAgNP film, and PDMS-AgNP film.\n4. Finite element simulations of the mechano-ionic-electrostatic coupling\nTo better understand the mechano-ionic-electrostatic coupling process in piezoionic\nsensing, we performed finite element simulations using COMSOL Multiphysics 6.3 by\ncombining poroelastic mechanics and Poisson-Nernst-Planck model. The simulations\nreproduced the physical process in a hydrogel relating to two representative bending\nscenarios (as shown in Figure R6): (i) bending around the X-axis (or Y-axis), denoted\nas axial bending; (ii) bending around a non-axis direction (taking 45 degrees as an\nexample), denoted as 45° oblique bending. The numerical simulations revealed the\ncoupling relationship between deformation geometry and multiphysical responses\nincluding the distributions of pore pressure, ionic concentration, and electric potential\n(shown in Figure R7).\naxial bending state (middle), and under 45° oblique bending state (right).\n4.1 COMSOL calculation\nThe finite element solver based on COMSOL (6.3) Multiphysics software is used\nto simulate the electric field in hydrogels under bending deformation. The geometry of\nthe hydrogel is set to 5 cm × 0.8 cm × 0.2 cm. In fact, we coupled poroelastic mechanics\nwith the Poisson-Nernst-Planck equations to calculate the transient distributions and\nelectric potential within the hydrogel. The governing equations were given as\n𝛻2𝜑 = −𝐹∑𝑧 𝐶\n𝑖 𝑖\n𝜕𝐶 𝐷 𝐶\n𝑖 𝑖 𝑖\n= 𝛻 ⋅[𝐷 𝛻𝐶 + 𝑧 𝛻𝜑]\n𝜕𝑡 𝑖 𝑖 𝑅𝑇 𝑖\nBending deformation creates high pore pressure in the inner layer and low pore\npressure in the outer layer of the hydrogel. As a result, water and ions migrate along the\npressure gradient following Darcy's law. The different migration rates of ions cause\nanions to accumulate in the outer layer, generating an electric potential difference\nbetween the two layers.\nThe model the processed electrochemistry model was imported into the finite\nelement software COMSOL® (Stockholm, Sweden), where the material properties were\ndefined and the relevant problems were calculated and analyzed. To solve the discretized\ntransport and kinematics equations, the Parallel Direct Sparse Solver (PARDISO) was\nemployed. Time stepping was handled using 2nd order backward Euler differentiation.\nThe simulation integrated three strongly coupled physical domains: poroelastic\nmechanics, ion transport, and electrostatics.\n(a) Governing equations\n(i) Poroelastic mechanics (Darcy’s law)\nThe solvent flow within the polymeric porous matrix was described by Darcy’s law:\n𝜅\n𝑢 = − 𝛻𝑃. Where u represents the superficial velocity, P denotes the pore pressure, 𝜅\n𝜂\nthe permeability of the hydrogel, and 𝜂 the fluid viscosity.\n(ii) Ion transport (modified Nernst-Planck equation)\nIon transport was governed by a modified Nernst-Planck equation accounting for\ndiffusion, electromigration, and convection: 𝑁 = −𝐷 𝛻𝐶 −𝑧 𝐶 𝜇 𝛻𝑉+𝑣 𝐶 . where N\n𝑖 𝑖 𝑖 𝑖 𝑖 𝑖 𝑖 𝑖 i\ndenotes the flux of ion species i, D the diffusion coefficient, C is the concentration of\ni i\nion species i, 𝜇 the ionic mobility, V the electric potential, and v the solvent velocity\n𝑖 i\nvector.\n(iii) Electrostatics (Poisson equation)\nThe Poisson equation establishes the fundamental relationship between the spatial\ndistribution of electric potential and the local charge density within the hydrogel system:\n𝛻2𝑉 = −\n𝜌𝑓.\nHere, V denotes the electric potential, 𝜌 represents the space charge\n𝑓\n𝜀\ndensity arising from the imbalance between cation and anion concentrations, and 𝜀 is the\ndielectric permittivity of the medium.\n(b) Coupling mechanism\nThe mechanical deformation induced a volumetric strain that generated a pore\npressure gradient, which drove solvent convection within the gel according to Darcy’s\nlaw. The resulting solvent flow, in turn, triggered ionic convection and charge separation.\nThe accumulated charge formed a localized electric field that further modulated ion\nmigration. Meanwhile, the deformation dynamically altered the permeability and\n27\neffective diffusion pathways, thereby modifying the ion and potential distributions. This\nfeedback loop establishes a complete mechanical-fluid-ionic-electrical coupling within\nthe hydrogel system.\n(c) Geometry and boundary conditions\nThe simulated hydrogel had dimensions of 4 cm × 0.5 cm × 0.5 cm and consisted\nof PSA-Li(TFSI) material. In the mechanical domain, symmetric bending moments were\napplied at both ends of the hydrogel to induce a curvature of approximately 45°, ensuring\na well-defined and reproducible deformation profile. In the ionic transport domain, the\ninitial ion concentration was assumed to be spatially uniform, while all external\nboundaries were specified as no-flux surfaces to prevent mass transfer across the\ninterfaces. In the electrostatic domain, the potential difference between the working and\nreference electrodes was continuously monitored to evaluate the evolution of the electric\nfield during deformation.\n4.2 Pore-pressure gradient under bending deformation\nFor the axial bending and the 45° oblique bending, the simulated maximal pore-\npressure gradients (peaked in the bending direction) both reaches approximately 1.1×104\nPa. For the 45° oblique bending, the ratio between the pore pressure gradients at the X-\naxis direction ( ∆P ) and the 45° oblique bending direction ( ∆P ) is\n45°X 45°\nr = \n∆P45°X= 0.565\n≈0.52 , which is close to the geometric projection coefficient\np\n∆P45° 1.0848\ncos45°≈0.71, indicating that the pressure difference under oblique bending can be\nregarded as the superposition of two orthogonal components projected onto the 45°\ndirection. This correlation confirms the consistency between numerical simulation\nresults and projection principle in classical bending theory.\n4.3 Ionic concentration redistribution\nThe simulated maximal cationic concentration differences (peaked in the bending\ndirection) are 1×103 mol m-3 for axial bending and 0.27×103 mol m-3 for 45° oblique\nbending. Although lower than the ideal projection value, the trend remains consistent\nwith the effective stress component along each principal axis. Also, for the 45° oblique\nbending, the ratio between the cationic concentration differences at the X-axis direction\n(∆c ) and the 45° oblique bending direction (∆c ) is r = \n∆c45°X= 0.141\n≈0.52, which\n45°X 45° 𝑐\n∆c45° 0.27\nis close to the geometric projection coefficient cos45°≈0.71, indicating that the ionic\nconcentration difference under oblique bending can be regarded as the superposition of\ntwo orthogonal concentration components projected onto the 45° direction.\n4.4 Electric potential distribution\nFor the axial bending and the 45° oblique bending, the simulated maximal potential\ndifferences (peaked in the bending direction) both reach a value of about 1.09 V, with\nnegative potential at the compressive side and positive potential at the tensile side. Under\naxial bending, the potential gradient is normal to the neutral surface, whereas in oblique\n45° bending the equipotential lines are rotated. For the 45° oblique bending, the ratio\nbetween the potential differences at the X-axis direction (∆𝑉 ) and the 45° oblique\n45°X\nbending direction (∆𝑉 ) is r = \n∆𝑉45°X= 0.57\n≈0.52 , which is close to the geometric\n45° 𝑉\n∆𝑉45° 1.09\nprojection coefficient cos45°≈0.71, indicating that the potential difference generated\nunder oblique bending can be regarded as the superposition of two orthogonal potential\ncomponents projected onto the 45° direction.\n4.5 Quantitative correlation and analytical consistency\nmechanical properties, the curvatures at X and Y axis directions for the 45° oblique\nbending state can be expressed as:\n𝜅 = 𝜅𝑐𝑜𝑠45𝑜,𝜅 = 𝜅𝑠𝑖𝑛45𝑜\n𝑥 𝑦\nAccordingly, for strain gradient-induced physical quantity difference in the 45°\nbending direction Δ o (e.g., pore pressure, ionic concentration, or potential difference),\n45\na first-order geometric approximation yields:\n∆ ≈ (∆ 𝑐𝑜𝑠45°+∆ 𝑠𝑖𝑛45°)\n45° 𝑥 𝑦\nFigure R7. Simulated pore pressure, cationic/anionic concentration, and potential output\nunder (a) axial bending and (b) 45° oblique bending.\n5. Analysis of the multi-Dof bending (direction and amplitude) based on piezoionic\noutput\n5.1 Bending around X-axis and Y-axis\nThe curvatures in the X- and Y-axial directions were denoted as κ and κ . The\nx y\npotential difference at the X- and Y-axial electrodes were denoted as V and V . Under\nAB CD\nbending around the X-axis, V increases monotonically with the absolute curvature |κ |\nAB x\n29\nand reverses polarity between concave and convex bending states. For small curvature,\nthe relationship can be approximated as[2]\n𝑉 ≈𝛼 𝑘\n𝐴𝐵 𝑥 𝑥\nwhere 𝛼 is a piezoionic coefficient, determined by intrinsic transport parameters\n𝑥\n(D, t, c, ε), structural geometry, and interfacial boundary conditions, as validated and\ni i i\nparameterized through COMSOL simulations.\nFor bending around Y-axis, V can be similarly expressed as\nCD\n𝑉 ≈𝛼 𝑘\n𝐶𝐷 𝑦 𝑦\nwhere 𝛼 reflects the Y-axis piezoionic coefficient.\n𝑦\n5.2 Bending around arbitrary direction (angle θ relative to X-axis)\nFor bending around arbitrary direction θ, the bending curvature k can be\ndecomposed to the two axes as\n𝑘 =𝑘𝑐𝑜𝑠𝜃, 𝑘 =𝑘𝑠𝑖𝑛𝜃\n𝑥 𝑦\nLeading to electrical outputs at the two electrodes as:\n𝑉 ≈𝛼 𝑘𝑐𝑜𝑠𝜃, 𝑉 ≈𝛼 𝑘𝑠𝑖𝑛𝜃\n𝐴𝐵 𝑥 𝐶𝐷 𝑦\nBy calculating the coefficients 𝛼 and 𝛼 from preliminary experiments, both the\n𝑥 𝑦\nbending magnitude and direction can be obtained:\n𝑘 =√( 𝑉𝐴𝐵) 2 +( 𝑉𝐶𝐷) 2 , 𝜃 ≈𝑎𝑟𝑐𝑡𝑎𝑛( 𝑉𝐶𝐷/𝛼𝑦)\n𝛼𝑥 𝛼𝑦 𝑉𝐴𝐵/𝛼𝑥\nThis intrinsic orthogonal-voltage response enables direct calculation of multi-Dof\nbending magnitude and direction, without no need of complex algorithm or multiple\nsensors integration.\nReferences\n1. ÖCHSNER A. Euler–Bernoulli Beams and Frames. Computational Statics and\nDynamics. 2016: 89-185.\n2. Dobashi Y, Yao D, Petel Y, et al. Piezoionic mechanoreceptors: Force-induced\ncurrent generation in hydrogels. Science, 2022, 376(6592): 502-507.\n3. Lou F, Chen D. Aligned carbon nanostructures based 3D electrodes for energy\nstorage. Journal of Energy Chemistry, 2015, 24(5): 559-586.\n4. Randles J E B. Kinetics of rapid electrode reactions. Discussions of the Faraday\nSociety, 1947, 1: 11-19.\n5. Lasia A. The Origin of the Constant Phase Element. The Journal of Physical\nChemistry Letters, 2022, 13(2): 580-589.\nAs suggested, we have added the discussions of the multi-Dof piezoionic sensing\nmechanism in the revised Manuscript (paragraph 1, page 4, 6, 9 and 13) and the extended\ndetails in the revised Supporting Information (Supplementary Note 1, 5, 7, 9 and 10,\npage 2, 10, 16, 21 and 22):\nRevised Manuscript (paragraph 2, page 4): “The piezoionic signal generation can be\ndescribed as a cross-scale process: a strain gradient field across the hydrogel thickness\n30\nfirst generates a hydrostatic stress gradient to drive an imbalanced ionic flux due to the\npolymer-ion modulation and ion size difference, and then the ionic flux transforms into\nelectronic output signal at the hydrogel-electrode interface via the electrical double\nlayer (EDL) charging (Supplementary Note 1).”\nRevised Manuscript (paragraph 2, page 6): “To better understand the mechano-ionic-\nelectrostatic coupling process in piezoionic sensing, we performed finite element\nsimulations using COMSOL Multiphysics by combining poroelastic mechanics and\nPoisson-Nernst-Planck model. The simulations reproduced the physical process in a\nhydrogel to reveal the coupling relationship between deformation geometry and\nmultiphysical responses including the distributions of pore pressure, ionic concentration,\nand electric potential (details in Supplementary Note 5)”\nRevised Manuscript (paragraph 2, page 6): “To analyze the correlation between the\nexternal mechanical excitation and the electrical signal generation, piezoionic\nequivalent circuit model coupling a mechanically driven current source and\nelectrochemical impedance elements was established (Supplementary Note7).”\nRevised Manuscript (paragraph 1, page 9): “Comparative analysis between different\nelectrodes in terms of equivalent circuit parameters was conducted to show the\nadvantages of eTPU-AgNP nanomesh electrode in interfacial polarization, and\npiezoionic output (Supplementary Note 9)”\nRevised Manuscript (paragraph 2, page 13): “For axial bending or bending around\narbitrary direction, the bending amplitude (or bending curvature) and direction can be\ndeduced from the piezoionic potential output of the two pairs of electrodes in X- and Y-\naxis (Supplementary Note 10).”\nRevised Supporting Information (Supplementary Note 1, 5, 7, 9 and 10, page 2, 10, 16,\n21 and 22)\nQuestion 2: The authors mention that the PSA matrix forms zwitterionic dipole-ion\ninteractions with Li+ ion (Figure 1b(ii)). However, despite the same PSA environment,\nthe Li⁺ transference number of PSA-Li(TFSI) is more than twice that of PSA-LiCl.\nThis discrepancy requires clarification, especially since weaker PSA-Li⁺ interactions in PSA–\nLi(TFSI) would theoretically result in a higher transference number.\nReply: Thank you for this insightful question. We fully agree that the Li⁺-PSA\ninteraction is weaker in the PSA-Li(TFSI) system compared to PSA-LiCl. However, the\nobserved increase in the Li⁺ transference number (t⁺) does not contradict this fact. Instead,\nit primarily arises from (i) suppressed anion mobility and (ii) differences in ion\nassociation structures, as well as (iii) coupled ion-polymer transport dynamics. We have\nnow elaborated this explanation in the revised manuscript and Supporting Information.\n1. Theoretical background\nThe cation transference number is defined as[1]:\n𝐼 𝑢\n+ +\n𝑡 = = \n+ 𝐼 +𝐼 𝑢 +𝑢\n+ − + −\nwhere 𝑢 and 𝑢 are the mobilities of cations and anions, respectively. Within the\n+ −\nStefan-Maxwell and Poisson-Nernst-Planck frameworks, these mobilities are strongly\ninfluenced by ion-polymer friction coefficients and ion-ion associations[2]:\n31\n𝐷\n𝑖\n𝑢 ∝ \n𝑖 𝑘 𝑇\n𝐵\n𝑘 𝑇\n𝐵\n𝐷 = \n𝑖 𝜁\n𝑖\nwhere 𝜁 is the friction coefficient between ion i and the polymer matrix. Thus, even\n𝑖\nif the Li⁺-polymer friction slightly decreases, a substantial increase in anion-polymer\nfriction will elevate 𝑡 .\n+\n2. Mechanistic explanation\n(a) Suppressed anion migration\nTFSI- is bulky, highly delocalized, and strongly polarizable. In the zwitterionic PSA\nmatrix, it engages in multiple weak interactions with cationic sites (-NR₃⁺) and\nhydrophobic domains, leading to partial trapping and reduced mobility. By contrast, Cl-\nis small and strongly hydrated. Its limited binding with PSA allows much faster\nmigration, thereby reducing 𝑡 .\n+\n(b) Ion association structures\nIn PSA-LiCl, Li⁺ often forms strong cross-chain coordination with sulfonate groups\n(-SO₃-), while Cl- remains relatively free. This immobilizes Li⁺ and results in a low 𝑡 .\n+\nIn PSA-Li(TFSI), Li⁺ is partially “shielded” by TFSI- in local coordination, which\nweakens Li⁺- SO₃- binding. Meanwhile, TFSI- itself is hindered by multipoint PSA\ninteractions. The net effect is that Li⁺ becomes less immobilized while TFSI- is strongly\nretarded, leading to a significant increase in the lithium-ion transference number 𝑡 .\n+\n(c) Coupled transport\nAccording to Stefan-Maxwell theory, ion migration is not independent but coupled.\nThus, reduced TFSI⁻ mobility increases the relative contribution of Li⁺ to charge\ntransport, elevating 𝑡 . In the formalism 𝑢 ∝ 1/𝜁 , where 𝜁 , is the ion-polymer friction\n+ 𝑖 𝑖 𝑖\ncoefficient. Even if Li⁺-polymer friction only slightly decreases, a large increase in\nTFSI⁻-polymer friction 𝜁 shifts conductivity balance toward Li⁺, elevating 𝑡 .\n𝑎𝑛𝑖𝑜𝑛 +\n3. Supporting evidence\nTo substantiate the proposed mechanism, the manuscript has been revised to\nelaborate the transport model and to incorporate supporting spectroscopic evidence.\nRaman spectroscopy provides key structural and dynamic insight (Figure R8). The\nvibration at 2930 cm⁻¹, arising from PSA backbone and side-chain C-H and N-H\nstretching[3], serves as an internal reference and is essentially insensitive to ion\ncoordination. In contrast, the 1043 cm⁻¹ band, assigned to the symmetric stretch of -SO₃-\ngroups[4], exhibits a marked dependence on the counter-anion: in PSA-LiCl it is sharp\nand intense, signifying strong Li⁺- SO₃- cross-linking and substantial Li⁺ immobilization,\nwhereas in PSA-Li(TFSI) it becomes broader and weaker, indicating attenuated Li⁺-SO₃-\nbinding. Additional evidence comes from TFSI- specific modes. The 1250 cm⁻¹ band[5],\nassociated with S=O and C-F vibrations of TFSI⁻, is strong and slightly broadened in\nPSA-Li(TFSI) but absent in PSA-LiCl, demonstrating that TFSI⁻ interacts extensively\nwith the PSA matrix and experiences restricted mobility. Likewise, the 743 cm⁻¹ band[6],\nassigned to the S-N-S skeletal vibration of TFSI⁻, is negligible in PSA–LiCl (I₇₄₃/I₂₉₃₀ =\n0.08) but markedly enhanced in PSA–Li(TFSI) (I₇₄₃/I₂₉₃₀ = 1.40), providing direct\nevidence of partial TFSI⁻ immobilization within the PSA network.\nincreases from 0.53 in LiCl to 2.82 in Li(TFSI), indicating a greater participation of\nTFSI⁻ accompanied by weaker Li⁺-SO₃⁻ coordination, while I₇₄₃/I₂₉₃₀ rises from 0.08 to\n1.40, demonstrating pronounced anion immobilization. Taken together, the sharpening\nof the 1043 cm⁻¹ band in PSA-LiCl and the strong 743 and 1250 cm⁻¹ features in PSA-\nLi(TFSI) provide a coherent mechanistic picture: Li⁺ is less immobilized whereas\nTFSI⁻ is strongly hindered in the TFSI- containing electrolyte. This asymmetric\nmobility shift increases the relative contribution of Li⁺ to total ionic conductivity\nand thereby raises the lithium-ion transference number 𝒕 .\n+\nIn conclusion, the enhanced lithium-ion transference number observed in PSA-\nLi(TFSI) is consistent with the weaker Li⁺-PSA coordination revealed by Raman\nspectroscopy. It arises from the synergistic interplay of suppressed TFSI⁻ transport,\nreduced Li⁺ immobilization, and coupled ion-polymer migration as described by Stefan-\nMaxwell transport theory. This mechanistic understanding, now fully incorporated into\nthe revised manuscript and supported by quantitative Raman analysis, provides a clear\nand experimentally grounded explanation of the superior Li⁺ transport properties in\nPSA-Li(TFSI).\nFigure R8. Comparative Raman spectra of 1 M PSA-LiCl and 1 M PSA-Li(TFSI)\nelectrolytes highlighting characteristic vibrational modes of S-N-S, S-O, and S=O/C-F\ngroups.\nReferences\n1. Shao Y, Gudla H, Brandell D, et al. Transference Number in Polymer Electrolytes:\nMind the Reference-Frame Gap. Journal of the American Chemical Society, 2022,\n144(17): 7583-7587.\n2. Jia L , Li L , Guo Z H ,et al.Giant Iontronic Flexoelectricity in Soft Hydrogels Induced\nby Tunable Biomimetic Ion Polarization. Advanced Materials, 2024, 36(31): 2403830.\n3. Bolun Peng, Miaomiao L, Peng Wang; et al. Polyzwitterionic Hydrogels with Tunable\nInterconnected Pore Structures Enabled by Ion‐Induced Phase Separation. Advanced\nFunctional Materials, 2025: e12551.\n33\n4. Sun L, Feng W, Liu Y, et al. Elastic and ultra stable ionic conductors for long-life-time\nsoft robots working at extreme environments. Nature Communications, 2025, 16(1):\n6004.\n5. Lai Z , You D , Wei W ,et al. Conformation-assisted solid-solid phase transition of\nLiTFSI electrolyte salt and the lithium ion coordination. Giant, 2024, 20.\n6. Lv Xu, Meng Yao, Liyu Du,et al. Accelerating lithium ion conduction via activated\ninterfacial dipole layer for long-life and high-voltage solid-state lithium-metal battery.\nJournal of Energy Chemistry, 2025,108: 92-100.\nAs suggested, we have added a discussion to explain the significant increase of Li⁺\ntransference number of PSA-Li(TFSI) compared with that of PSA-LiCl in the revised\nManuscript (paragraph 1, page 6) and the revised Supporting Information\n(Supplementary Note 4, page 8):\nRevised Manuscript (paragraph 1, page 6): “This significant increase of t + compared\nLi\nwith that of PSA-LiCl was due to a coupled ion-polymer transport dynamics of\nsuppressed anion mobility and difference in ion association structure (details in\nSupplementary Note 4).”\nRevised Supporting Information (Supplementary Note 4, page 8)\nQuestion 3: In Figure 2, the authors highlight robust hydrogel-electrode adhesion,\nyet they do not sufficiently explain the function of the flexible electrode, which is a\ncritical design element. The manuscript should include a detailed discussion on the\nmechanical compatibility between the hydrogel and electrode materials. The term \"eTPU\"\nmust be clearly defined, and a comparative analysis of the tensile stress-strain behavior of PDMS,\nTPU, and eTPU is recommended to justify electrode selection.\nReply: Thank you for these valuable comments.\nFor the question of “yet they do not sufficiently explain the function of the\nflexible electrode, which is a critical design element”, we have supplemented detailed\ndiscussions about the piezoionic sensing mechanism, including the mechano-ionic-electrostatic\ncoupling process (Supplementary Note 1 in the revised Supporting\nInformation), the piezoionic equivalent circuit model (Supplementary Note 7 in the\nrevised Supporting Information), and the comparative analysis between different\nelectrodes in terms of equivalent circuit parameters (Supplementary Note 9 in the\nrevised Supporting Information). The function of the flexible electrode was clearly\nillustrated to serve as an ionic-to-electronic interface via the dynamic reconstruction of\nthe electric double layer, and the detailed contributions to the overall Ohmic resistance\nalong the conduction path, the interfacial polarization, and the piezoionic output were\nexpounded in the abovementioned supplementary notes.\nFor the question of “The term \"eTPU\" must be clearly defined”, we have added\na definition to the eTPU-AgNP nanomesh in the revised Manuscript (paragraph 1, page\n1): “The eTPU-AgNP nanomesh refers to electrospun thermoplastic polyurethane (eTPU)\nnanofiber membrane coated with silver nanoparticles (AgNPs). The eTPU-AgNP\nnanomesh electrode was fabricated by combining the techniques of electrospinning and\nvacuum evaporation deposition to achieve a uniform nanofibrous network with\nconductive coating (Figure S1, fabrication details in Supplementary Text S3).”\nmechanical compatibility between the hydrogel and electrode materials” and “a\ncomparative analysis of the tensile stress-strain behavior of PDMS, TPU, and eTPU\nis recommended to justify electrode selection”, we have supplemented comprehensive\nexperimental data to justify the electrode selection, including the mechanical properties\nof the electrodes, the interfacial adhesion between the hydrogel and the electrode, the\ninterfacial impedance at the hydrogel-electrode interface, and the piezoionic sensing\nperformance using the different electrodes. Detailed discussion as below:\nFigure R9(a) shows the PSA-Li(TFSI) hydrogel exhibits a Young’s modulus of ~14\nkPa, while the moduli of PDMS-AgNP, eTPU-AgNP, and TPU-AgNP are approximately\n0.8 MPa, 1.8 MPa, and 11.6 MPa, respectively. From the mechanical matching\nstandpoint, a smaller modulus disparity with the hydrogel favors stress transfer and\ndeformation compatibility at the interface. The relatively lower moduli of PDMS-AgNP\nand eTPU-AgNP are more suitable for the hydrogel electrode. Figure R9(b) shows the\ninterfacial adhesion strength tests of the three electrodes. eTPU-AgNP nanomesh\nexhibited highest adhesion strength with the hydrogel, far exceeding that of PDMS-\nAgNP. The open nanomesh architecture of eTPU-AgNP nanomesh electrode provides a\nmarkedly increased surface area and surface roughness, which facilitates the infiltration\nof soft hydrogel into the porous electrode surface to form geometric interlocking, thereby\ngreatly enhancing interfacial adhesion. Considering the softness and interfacial\ncompatibility, the eTPU-AgNP nanomesh stands out from the alternatives to serve as a\nrobust piezoionic electrode. As shown in confocal microscopy interfacial images (Figure\nR10), there were pronounced gaps at the interfaces between hydrogel and PDMS-AgNP\nor the TPU-AgNP films due to the poor mechanical compliance. By contrast, the eTPU-\nAgNP nanomesh electrode provided a tight interface with the hydrogel owing to the\nsoftness and nanofibrous porous electrode surface. This enhanced adhesion strength\neffectively prevents the bucking of the electrode, which allows a tight bonding at the\nhydrogel-electrode interface.\nFigure R9. (a) Tensile stress-strain curve of different electrodes. (b) Interfacial adhesion\nstrength tests of PSA-Li(TFSI) hydrogel attached to electrodes of eTPU-AgNP\nnanomesh, TPU-AgNP film, and PDMS-AgNP film.\nbending. (a) PDMS-AgNP electrode, (b) TPU-AgNP electrode and (c) eTPU-AgNP\nnanomesh electrode.\nFrequency-dependent impedance spectra (Figure R11) revealed that eTPU-AgNP\nnanomesh electrode consistently exhibited the lowest impedance across the full\nfrequency range at static state, indicating a significant reduction in interfacial impedance.\nMore importantly, at bending state, eTPU-AgNP nanomesh displayed negligible\nimpedance variation. The interfacial impedance of PDMS-AgNP and TPU-AgNP\nelectrodes both increased markedly, suggesting aggravated interfacial debonding.\nApparently, the eTPU-AgNP nanomesh enabled a more efficient and stable charge\ntransport pathway across the hydrogel-electrode interface.\nand PDMS-AgNP film under static and bending conditions.\nThese combined merits of the eTPU-AgNP nanomesh electrode collectively\nresulted in a more sensitive and stable piezoionic signal than those of TPU-AgNP film\nand PDMS-AgNP film. The piezoionic output in the bending range of 0-180 degree in\nFigure R12 and the time-resolved potential output under a step bending deformation in\nFigure R13 verified a highest flex-sensitivity and most stable signal of the eTPU-AgNP\nnanomesh electrode in comparison with TPU-AgNP and PDMS-AgNP electrodes.\nFigure R12. The piezoionic output in the bending range of 0-180 degree of eTPU-AgNP\nnanomesh, TPU-AgNP film, and PDMS-AgNP film. The flex-sensor using eTPU-AgNP\nnanomesh electrode shows a highest flex-sensitivity across the 0-180 bending degree.\nFigure R13. The time-resolved potential output under a step bending deformation of (a)\neTPU-AgNP nanomesh, (b) TPU-AgNP film, and (c) PDMS-AgNP film. The flex-\nsensor using eTPU-AgNP nanomesh electrode shows a most stable signal under a step\ninput of bending deformation.\nAs suggested, we have added a discussion about the comprehensive comparison of\nthe mechanical/electrical/adhesive/piezoionic sensing properties between the eTPU-\nAgNP nanomesh, TPU-AgNP film, and PDMS-AgNP film in the revised Manuscript\n(paragraph 1, page 9) and the revised Supporting Information (Figure S23 to S27, page\n43 to 45):\nthe TPU-AgNP film and PDMS-AgNP film as a flexible electrode in flex-sensor, as it\nexhibits not only a low initial resistance of 0.65 Ω cm⁻¹, but also a more stable resistance\nunder bending deformation than those of the TPU-AgNP film and PDMS-AgNP film\n(Figure S23). Cyclic bending tests further confirmed the electromechanical robustness\nof the eTPU-AgNP nanomesh electrode (Figure 2e). We attributed the superb electrical\nresilience to the porous microstructure of the nanofibrous eTPU scaffold and the\nanchoring of the percolated AgNPs network on the fiber surface (Figure 2e and 2f),\nwhich could accommodate the bending strain via fiber deflection and reorientation,\nthereby preserving the percolation network. Besides, the eTPU-AgNP nanomesh\nexhibited a strongest adhesive strength to the hydrogel to facilitate a tight and gap-free\nbonding, which accounted for a low and stable interfacial impedance of the hydrogel-\nelectrode interface (confocal microscopy images of electrode-hydrogel interfaces in\nFigure S24 and interfacial impedance spectra in Figure S25). These combined merits of\nthe eTPU-AgNP nanomesh electrode collectively resulted in a more sensitive and stable\npiezoionic signal than those of TPU-AgNP film and PDMS-AgNP film (Figure S26 and\nFigure S27).”\nRevised Supporting Information (Figure S23 to S27, page 43-45).\nQuestion 4: The manuscript does not provide sufficient performance evaluation of the\nPSA-Li(TFSI) flex sensors. Comparative data using control materials (e.g., PAM-LiCl,\nPAM-Li(TFSI), PSA-LiCl) are needed to substantiate claims of material superiority.\nReply: Thank you for this valuable suggestion, we agree that although the previous\nManuscript provided the design advantages of the PSA-Li(TFSI), comparative data\nusing control materials (PAM-LiCl, PAM-Li(TFSI), PSA-LiCl) are requisite for\nclaiming superiority of the PSA-Li(TFSI). A comprehensive comparison was\nconducted across the mechanical properties, adhesive strength, ionic conductivity,\npiezoionic sensitivity and dynamic response. Detailed discussion as below:\nFigure R14 (a) shows the mechanical tensile tests of PAM-Li(TFSI), PAM-LiCl,\nPSA-LiCl, and PSA-Li(TFSI) hydrogels, and Figure R14 (b) displays the tensile\nloading-unloading tests under 100% strain. The PSA-Li(TFSI) achieved an optimal\nbalance between the mechanical strength and the stretchable strain range. More\nimportantly, the PSA-Li(TFSI) exhibited a smallest residual strain compared with other\nhydrogels, being only 0.4% after tensile loading-unloading test at 100% strain. The PSA\nbackbone, rich in zwitterionic -N⁺(CH₃)₃/-SO₃⁻ groups, can form reversible ionic\nassociations with fast crosslink dynamics, which endows the polymer network with high\nresilience. For adhesive properties, PSA-Li(TFSI) exhibits the highest interfacial\nadhesive strength as shown in Figure R14 (c). The zwitterionic structure in the PSA\nbackbone enables multiple ion-dipole interactions, enhancing interfacial bonding. The\nTFSI⁻ anion, with high polarizability and fluorinated groups, improves interfacial\nwettability and polarity matching.\nLi(TFSI), and PAM-LiCl hydrogels. (b) Tensile-recovery stress-strain curves at 100 %\nstrain and comparison of residual strain among PSA-Li(TFSI), PSA-LiCl, PAM-\nLi(TFSI), and PAM-LiCl hydrogels. (c) Comparison of adhesive strength of PAM-LiCl,\nPAM-Li(TFSI), PSA-LiCl, and PSA-Li(TFSI) hydrogels to the nanomesh electrode.\nAs shown in Figure R15, PSA-LiCl exhibits the highest bulk ionic conductivity\n(30.2 mS/cm), followed by PSA-Li(TFSI) (26.7 mS/cm), and both are markedly higher\nthan that of PAM-LiCl (19.3 mS/cm) and PAM-Li(TFSI) (13.0 mS/cm). This trend can\nbe rationalized by the classical relationship σ =Σ cᵢ zᵢ μᵢ. Here cᵢ is the number of charge\ncarriers, zᵢ is the charge number, μᵢ is the mobility. In PSA-LiCl, despite the suppression\nof salt dissociation caused by strong Li⁺-Cl⁻ coordination, the intrinsically high mobility\nof the small Cl⁻ anion results in high ionic conductivity. However, this strong binding\nalso increases the network crosslink density, which limits microscopic ion mobility μᵢ\nand can induce nonlinear responses under strain. By contrast, PSA-Li(TFSI) combines\na high free-ion concentration with markedly enhanced ion mobility: the weak Li+-TFSI-\ncoordination facilitates salt dissociation, while the zwitterionic PSA backbone provides\ncontinuous polar nanochannels that substantially raise μᵢ. PAM, with fewer polar groups\nand a more rigid backbone, restricts μᵢ regardless of the anion type. More importantly,\nthe superior sensing sensitivity of PSA-Li(TFSI) originates from a much larger\ndifference between cation and anion transference numbers (Li+ transference number\nreaches 0.85 for PSA-Li(TFSI)). In summary, although PSA-LiCl has a highest\nconductivity, PSA-Li(TFSI) maintains a comparable σ while achieving a larger\ncation/anion transference-number difference, which provides higher piezoionic flex-\nsensitivity and more stable electromechanical coupling. Together with its superior\nmechanical compliance and interfacial stability, PSA-Li(TFSI) delivers a more\nadvantageous performance over that of PSA-LiCl and other controls.\nPAM-LiCl, PSA-Li(TFSI), and PSA-LiCl hydrogels. (b) Comparison of ionic\nconductivity of different hydrogels.\nThe piezoionic potential output versus the bending angle was tested for the different\nhydrogels, as shown in Figure R16. The responses of the four hydrogels can be fitted to\ntwo-segment linear curves. The PSA-Li(TFSI) shows highest piezoionic sensitivity,\nwhich is consistent with its high conductivity and large Li+ transference number. Figure\nR17 shows the comparison of response/recovery time of the piezoionic sensors using\ndifferent hydrogels under a bending input. Clearly, the PSA-Li(TFSI) exhibited not only\nfast response and recovery times of ~200 and ~100 ms, but also a most stable potential\nresponse upon a step bending deformation.\nFigure R16. Piezoionic potential response versus bending angle of different hydrogels.\nThe responses of the four hydrogels can be fitted to two-segment linear curves. The\nsensitivities of the linear segments were extracted as S and S for each hydrogel.\n1 2\nLiCl, (c) PAM-Li(TFSI) and (d) PAM-LiCl hydrogels.\nAs suggested, we have added comparative data using control materials (PAM-LiCl,\nPAM-Li(TFSI), PSA-LiCl) to substantiate claims of material superiority, including the\nmechanical properties, adhesive strength, ionic conductivity, piezoionic sensitivity and\ndynamic response in the revised Manuscript (paragraph 1, page 1) and the revised\nSupporting Information (Figure S14 to S17, page 37 to 39):\nRevised Manuscript (paragraph 2, page 8): “The PSA-Li(TFSI) hydrogel was chosen as\nthe piezoionic matrix as it possessed optimal balance in mechanical compliance,\nadhesive strength, and ionic conductivity (Figure S14 to S15) compared with control\nhydrogels of PAM-LiCl, PAM-Li(TFSI), PSA-LiCl, resulting a highest piezoionic\nperformance (Figure S16 and S17).”\nRevised Supporting Information (Figure S14 to S17, page 37 to 39)\nQuestion 5: The concentration of Li(TFSI) may significantly influence both ion\nmigration under mechanical deformation and electrostatic interactions with the polymer\nmatrix. The authors are encouraged to investigate the optimization of ion concentration\nand its impact on mechanical sensitivity and overall device performance.\nReply: Thank you for this insightful question. We agree the concentration of Li(TFSI)\ncan play an important role in regulating the mechanical sensitivity and overall device\nperformance.\nIn our previous trials of different Li(TFSI) concentrations for piezoionic sensors,\nwe found that the 1M concentration yielded a highest piezoionic flex-sensitivity, which\nwas adopted in the previous Manuscript. We here supplemented a more detailed\ninvestigation about how the Li(TFSI) concentration affects the mechanical and electrical\nproperties of the PSA-Li(TFSI) hydrogel, to unveil the underlying causes of the optimal\npiezoionic performance at the 1M concentration. Detailed discussion as below:\nWe have prepared PSA-Li(TFSI) hydrogels with Li(TFSI) concentrations of 0.2,\n0.5, 1.0, 1.5, and 2.0 M. The tensile properties (Figure R18a) show a non-monotonic\ntrend with salt concentration. The tensile strength first increased and then decreased,\npeaking at concentration of 1 M. This was primarily determined by the crosslink density\nwithin the polymer network. Initially, the increase of Li(TFSI) concentrations provides\nmore Li+ and TFSI- ions to coordinate with the zwitterionic groups (zwitterionic dipoles)\non the polymer chains, and these ion-dipole interactions bridge the adjacent polymer\nchains, acting as dynamic, reversible physical crosslinks, leading to a stronger gel. At\nhigh Li(TFSI) concentrations, the number of free ions overwhelms the hydrogel, the\ncharged zwitterionic groups can be surrounded by plenty of counter-ions, this strong\nionic atmosphere screens the dipoles of the zwitterion itself, weakening its ability to\nform strong ion-dipole bonds with other chains. Similarly, the 1 M sample also showed\na smallest residual strain of 0.4% at loading-unloading strain of 100% (Figure R18b),\nowing to the fast crosslink dynamics of the ionic associations.\nLi(TFSI) concentrations. (b) Tensile recovery behavior and comparison of residual strain\nof PSA-Li(TFSI) hydrogels at different Li(TFSI) concentrations.\nFigure R19 shows the Nyquist plots and the extracted electrical conductivities of\nthe PSA-Li(TFSI) hydrogels at different Li(TFSI) concentrations. The conductivity\nincreases firstly, reaching a maximum of ~27 mS cm⁻¹ at a concentration of 1 M, then\ndecreases at higher concentrations. Initially, adding more Li(TFSI) salt directly increases\nthe number of mobile Li+ and TFSI- ions available to carry current, which dominates the\nearly increase in conductivity. At high concentrations, the Li+ and TFSI- ions tend to\nassociate to form neutral ion pairs or larger aggregates, which reduces the mobile charge\nnumber. Also, high concentrated mobile ions have to compete with the immobilized\nzwitterionic dipoles and other ions for solvation and hopping sites, which could further\ndecelerate their transport.\nFigure R19. (a) Impedance Nyquist plots of PSA-Li(TFSI) hydrogels at different\nLi(TFSI) concentrations. (b) Ionic conductivity of PSA-Li(TFSI) hydrogels as a function\nof Li(TFSI) concentration.\nThe piezoionic sensing performance of piezoionic sensors with different Li(TFSI)\nconcentrations was compared by recording the potential output versus bending angle in\nFigure R20. The flex-sensitivity of the samples experienced a similar trend to the\nmechanical/electrical properties with the increase of Li(TFSI) concentration. The 1 M\nconcentration sample produced a highest flex-sensitivity. The optimal sensing\n42\nperformance could be attributed to not only the highest conductivity, but also the highest\nmechanical strength, which favors the stress transfer in the soft hydrogel matrix.\nFigure R20. Potential response versus bending angle of PSA-Li(TFSI) hydrogels at\ndifferent Li(TFSI) concentrations.\nAs suggested, we have added a discussion about the impact of the Li(TFSI)\nconcentrations on the mechanical, electrical, and piezoionic sensing performance in the\nrevised Manuscript (paragraph 2, page 8) and the revised Supporting Information (page\n20):\nRevised Manuscript (paragraph 2, page 8): “The concentration of Li(TFSI) also\ninfluences the mechanical and electrical properties of the PSA-Li(TFSI) hydrogel. A\nsystematic investigation was conducted to determine an optimal concentration of 1 M,\nwhich exhibited highest mechanical strength and electrical conductivity, leading to a\nhighest piezoionic flex-sensitivity (detailed analysis in Supplementary Note 8, Figure\nS15 to S17).”\nRevised Supporting Information (Supplementary Note 8, Figure S15 to S17)\nQuestion 6: All quantitative data should include error bars and appropriate statistical\nanalysis to demonstrate data reliability and reproducibility.\nReply: We appreciate this good suggestion. In the revised manuscript, we have carefully\nchecked all the quantitative data and now provided error bars representing the standard\ndeviation from at least three independent measurements, including Figure 1f, Figure 2b,\nand Figure 2d.\nQuestion 7: The application of the sensor for VR control lacks sufficient technical\nvalidation. Although multi-DOF sensing is claimed, the directional sensing mechanisms\nalong different axes are not clearly described. The relationship between mechanical\ninputs (e.g., bending angle, motion speed) and signal outputs should be clarified.\nThe authors should include a detailed signal mapping process, accompanied by kinematic\ndiagrams or response maps to illustrate how physical deformations translate into virtual\nmovements.\nReply: Thank you for this highly constructive suggestion. We totally agree the original\nmanuscript lacks sufficient technical validation for the VR control.\nsensing mechanisms along different axes are not clearly described”, we have\nsupplemented a detailed and in-depth mechanistic analysis about the piezoionic sensing\nprocess and the multi-Dof bending sensing in response to Question 1.\nFor the questions of “The relationship between mechanical inputs (e.g., bending\nangle, motion speed) and signal outputs should be clarified” and “The authors should\ninclude a detailed signal mapping process, accompanied by kinematic diagrams or\nresponse maps to illustrate how physical deformations translate into virtual\nmovements.”, we here supplemented a detailed analysis of the physical and\nmathematical model of the multi-Dof bending sensing, and the workflow description\nfrom physical deformations to virtual movements. Detailed content as below:\n1. Physical mechanism and mathematical model of the multi-Dof bending sensing\nTo quantitatively describe the electrical responses of the SPIM sensor under multi-\ndirectional bending, we established a coupling-matrix model that systematically maps\nmulti-axis mechanical inputs to electrical outputs. The physical basis of this model lies\nin strain-driven ionic migration and dynamic reconstruction of the interfacial electric\ndouble layer (EDL). External bending generates a strain distribution of 𝜀(𝑧,𝑡) = 𝑘(𝑡)𝑧,\nhere 𝑘(𝑡) is the curvature and z is the thickness distance from the neutral plane, driving\ndirectional ion migration to induce charge accumulation and dynamic\ncharging/discharging at the electrode interface. This coupled process of ionic transport\nand interfacial charge transfer produces voltage outputs that correspond closely to\nbending direction, amplitude, and rate, providing a solid physical foundation for the\ninput-output model.\nUnder the linear-response approximation, the voltage response can be expressed as\na two-dimensional linear system:\n𝑉(𝑡) = 𝑆𝑘(𝑡)+𝜂(𝑡)\n𝑇\nwhere 𝑉(𝑡) = [𝑉(𝑡),𝑉 (𝑡)] denotes the output voltages and 𝑘(𝑡) = \n𝑥 𝑦\n𝑇\n[𝑘 (𝑡),𝑘 (𝑡)] represents the instantaneous curvature components along the X and Y\n𝑥 𝑦\naxes. S is the 2×2 sensitivity matrix containing the principal sensitivities (𝑆 ,𝑆 ) and\n𝑥𝑥 𝑦𝑦\nthe cross-coupling coefficients (𝑆 ,𝑆 ) that describe geometric asymmetry, electrode\n𝑥𝑦 𝑦𝑥\nmisalignment, and material anisotropy. 𝜂(𝑡) represents random noise or minor non-\nidealities.\nThis formulation provides a compact physical and mathematical description of the\ndual-axis mechano-ionic-electrostatic coupling, serving as the foundation for subsequent\ncalibration and signal-decoupling analysis.\n2. Calibration of the quantitative mapping from mechanical input to electrical\noutput\nAs shown in Figures R21-R25, the flexible multi-Dof sensor establishes a clear\nquantitative mapping between three mechanical input dimensions-direction, magnitude,\nand frequency-and its electrical output.\nFigure R21 reveals a monotonic relationship between bending amplitude angle and\noutput potential amplitude. When the wrist bending angle gradually increases from 10o\nto 90o, the output voltage rises almost linearly and maintains a stable periodic waveform.\nBy calibrating the relationship between the peak output voltage 𝑉𝑝𝑘 and the bending\namplitude angle 𝜃, an analytical model of the form 𝑉𝑝𝑘 = 𝑘𝜃 +𝑏 can be constructed,\nenabling quantification of the bending amplitude and direction.\nFigure R21. Potential signals generated by the SPIM flex-sensor upon wrist flexion at\nincreasing bending amplitude angles from 10 to 90 degrees.\nFigure R22-23 demonstrates the bending directional angle dependence of the\nvoltage vector. The peak voltages of the X and Y axes vary with the bending directional\nangel over the full 0-360o range in an approximately sinusoidal manner. When bending\noccurs along the X-axis (0°/180°), the X-axis voltage reaches a maximum while the Y-\naxis voltage remains near zero; when bending occurs along the Y-axis (90o/270o), the Y-\naxis voltage reaches a maximum while the X-axis voltage remains near zero. At oblique\nbending directions (angles such as 30o or 45o), their voltage amplitude ratios correspond\nto the bending directional angles. Consequently, the bending directional angle 𝜃̂ can be\ncalculated from the voltage vector 𝑉 = [𝑉 ,𝑉 ]:\n𝑋 𝑌\n𝑎𝑟𝑐𝑡𝑎𝑛(\n𝑉𝑌),𝑉\n> 0\n𝑋\n𝑉𝑋\n𝑎𝑟𝑐𝑡𝑎𝑛(\n𝑉𝑌)+𝜋,𝑉\n< 0,𝑉 ≥ 0\n𝑋 𝑌\n𝑉𝑋\n𝜃̂ = 𝜆(𝑉\n𝑌\n,𝑉\n𝑋\n), 𝜆(𝑉\n𝑌\n,𝑉\n𝑋\n) = \n𝑎𝑟𝑐𝑡𝑎𝑛(\n𝑉𝑌)−𝜋,𝑉\n< 0,𝑉 < 0\n𝑉𝑋\n𝑋 𝑌\n+\n𝜋\n,𝑉 = 0,𝑉 > 0\n𝑋 𝑌\n2\n𝜋\n− ,𝑉 = 0,𝑉 < 0\n{ 𝑋 𝑌\nphysical basis for real-time bending motion reconstruction.\nbending directional angles. (a) 0o, (b) 30o, (c) 45o, (d) 90o, (e) 180o and (f) 270o.\nFigure R23. (a) Bending directional angle-dependent X/Y-axis potential responses. (b)\nSchematic of corresponding bending directions.\nFigure R24 verifies the sensor’s dynamic response and bandwidth capability. Under\nbending frequencies ranging from 0.05 to 4 Hz, the period of the output voltage matches\nthe externally applied mechanical load precisely, and the waveform remains stable\nwithout significant attenuation of amplitude. This finding indicates that the combined\nrate of interfacial ion migration and ionic-to-electronic transfer is sufficient to support\ndynamic operations. Frequency information can therefore be extracted directly from\npeak-to-peak intervals or by fast Fourier transform (FFT), enabling accurate real-time\ndetermination of motion frequency and ensuring the high fidelity and responsiveness\nrequired for human-machine interaction and complex motion monitoring.\nmechanical inputs and electrical outputs: (𝜃 ,𝜃 ,𝑓 ) →\n𝑑𝑖𝑟𝑒𝑐𝑡𝑖𝑜𝑛 𝑚𝑎𝑔𝑛𝑖𝑡𝑢𝑑𝑒 𝑓𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦\n[𝑉 (𝑡),𝑉 (𝑡)] . Through vector-angle analysis, magnitude regression, and period or\n𝑋 𝑌\nspectral analysis, the device can simultaneously resolve bending direction, bending\namplitude, and frequency of mechanical deformations in real time on a single sensing\nplatform. This integrated multi-dimensional sensing capability offers a highly compact\nand real-time solution for motion capture, virtual-reality interaction, and soft-robot\ncontrol.\nFigure R24. Potential responses at different bending frequencies (0.05-4 Hz).\n3. Workflow description from physical deformations to virtual movements\nIn general, the SPIM flex-sensor generated piezoionic voltage output through\nmechano-ionic-electrostatic coupling, and the measurable analogue voltage signals can\nbe converted into bending inputs of different amplitude and direction, which lays the\nphysical foundation for data processing and motion reconstruction. The data workflow\nin Figure R25 depicts the process from physical deformations to virtual movements:\n(1) Analogue-to-digital conversion (ADC) of piezoionic signal\nThe sensor is attached to the body position of interest (e.g., wrist joint).\nOscilloscope monitoring captures real-time voltage waveforms from the two pairs of\nelectrodes during the motion of hand. These continuously varying analogue voltages are\ndigitized by an ADC converter, ensuring that subtle differences in bending amplitude\nand angle are faithfully preserved for further processing.\n(2) Arduino signal processing\nInside the microcontroller, the signal first passes through a moving-average filter\nto remove high-frequency noise. Linear-regression modelling for both axes: The X- and\nY-axis voltage responses are each fitted using a linear regression model, which\nestablishes a rigorous one-to-one mapping between voltage amplitude and bending angle.\nLarger bending amplitudes correspond to higher voltage magnitudes, while the relative\ncontributions of the X and Y channels encode the bending direction. This procedure\nenables facile quantification of bending amplitude and direction information. The\nprocessed direction and amplitude data are collected in real time by Arduino and\ntransmitted to the computer via a serial port.\n(3) Processed data to VR control commands\nArduino to the host computer via USB, ensuring millisecond-level synchronization of\nbending amplitude and direction information.\nC# Script processing and control: On the computer, a C# script further analyses\nand interprets the amplitude and directional data, converting them into control\ncommands for virtual objects, guaranteeing that the magnitude of virtual motion matches\nthe physical deformation.\nUnity interface and virtual movements: Finally, the data drive a three-\ndimensional visualization in Unity, where a virtual hand can reproduce the user’s real\nwrist motions or control other virtual objects (e.g., steering of a virtual car).\nFigure R25. Integrated workflow for single-joint VR control based on multi-parameter\nsensor characterization, real-time data processing, and Unity-based virtual interaction.\nAs suggested, we have added a discussion about the multi-Dof bending sensing\nmechanism and the technical validation of transforming mechanical input into virtual\nmovements in the revised Manuscript (paragraph 1, page 15) and the revised Supporting\nInformation (Supplementary Note 11, page 23):\nRevised Manuscript (paragraph 1, page 15): “The mathematical model of the multi-Dof\nbending in VR control was investigated to obtain a quantitative mapping from\nmechanical input to electrical output, which lays the foundation for the technical\nimplementation in transforming physical deformations to virtual movements\n(Supplementary Note 11)”.\nRevised Supporting Information (Supplementary Note 11, page 23).\ncaptions. The authors should carefully review and revise all figures and captions for\nconsistency. In addition, several figures and tables are not sufficiently discussed in the\nmain text. The manuscript should be revised to ensure all visuals are explicitly\nreferenced and clearly interpreted.\nReply: We really appreciate these meticulous and important reminding. We have\ncarefully examined the figures, captions, tables and made corrections.\n- Page 3, Line 74: Reference 33 is not relevant to TPU.\nReply: Thank you for this reminding. We removed the original Ref. 33 and replaced it\nwith a TPU-relevant reference, revising the sentence to fit the new citation and updating\nthe reference list accordingly.\n- Page 4, Line 133: Reference 40 does not pertain to Li⁺ diffusion kinetics.\nReply: Thank you for pointing this out. The citation was replaced with a reference that\nspecifically addresses Li+ diffusion kinetics, and the text was revised accordingly.\n- Figure 1b(iii) is not explained in the manuscript.\nReply: Thanks for this kind suggestion. We added an explicit explanation of panel b(iii)\nin the revised manuscript (paragraph 3, page 5).\n- Figure S3: The graph legend shows LiCl, while the caption refers to Li(TFSI).\nReply: Thank you for this careful reminding. We have made correction in the revised\nSupporting Information.\n- Figure 2: Mismatch exists between the figure content and caption.\nReply: Thanks for this kind suggestion. We have made corrections to Figure 2 in the\nrevised Manuscript.\n- Page 10, Line 271 (caption of Figure 3): The label ‘c’ should be corrected to ‘e’.\nReply: Thank you for pointing out this mistake. We have made correction in the revised\nManuscript.\n- Figure 4: Caption does not correspond accurately to the figure.\nReply: Thank you for this reminding. We have missed out the caption of Figure 4b in\nthe previous Manuscript. We have made correction in the revised Manuscript.\n- Figures 5b(i) to 5b(viii): No explanation is provided in the main text.\nReply: Thank you for this good suggestion. We agree these panels need detailed\nexplanation. We have added a new paragraph to discuss these figures in the revised\nManuscript (paragraph 1, page 15).\n- Table S2 is not discussed in the manuscript.\nReply: Thank you for pointing out this mistake. We have added a discussion in the\nrevised Manuscript ( paragraph 3, page 4) that explicitly cites and explains Table S2.\n- In Table S2, \"Srange\" should be corrected to \"Range\".\nReply: Thank you for pointing out this mistake. We have made correction in the revised\nManuscript.\n1. A detailed and in-depth mechanistic analysis of the multi-Dof piezoionic sensing,\nincluding (i) the physical process of the cross-scale mechano-ionic-electrostatic\ncoupling, (ii) piezoionic equivalent circuit model, (iii) comparative analysis of\ndifferent electrodes in terms of equivalent circuit parameters, (iv) finite element\nsimulations of the mechano-ionic-electrostatic coupling, and (v) analysis of the multi-\nDof bending (direction and amplitude) based on piezoionic output;\n2. Mechanistic analysis and supplementary experiment to explain the significantly\nhigher Li+ transference number of PSA-Li(TFSI) compared with that of PSA-LiCl;\n3. Comprehensive experimental data to justify the electrode selection, including the\nmechanical properties, the interfacial adhesion between the hydrogel and the electrode,\nthe interfacial impedance at the hydrogel-electrode interface, and the piezoionic\nsensing performance using different electrodes.\n4. Comprehensive experimental data to substantiate the hydrogel superiority of PSA-\nLi(TFSI) compared with control hydrogels of PAM-LiCl, PAM-Li(TFSI), and PSA-\nLiCl), including the mechanical properties, adhesive strength, ionic conductivity,\npiezoionic sensitivity and dynamic response.\n5. The optimization of Li(TFSI) concentration in PSA-Li(TFSI) and its impact on\nmechanical, electrical, and overall device performance.\n6. Technical validation of the multi-Dof VR control, including (i) physical mechanism\nand mathematical model of the multi-Dof bending sensing, (ii) calibration of the\nquantitative mapping from mechanical input to electrical output, and (iii) workflow\ndescription from physical deformations to virtual movements.\n7. Investigation of the key parameters that dominate the electromechanical performance\nof the eTPU-AgNP nanomesh electrode;\n8. Quantitative adhesion testing data of the PSA-Li(TFSI) and the underlying\nmechanism of the adhesion optimization;\n9. A coherent cross-scale framework ranging from molecular scale to device scale to\nelucidate the synergistic polymer-ion interactions;\n10. Discussion about the difference in ionic modulation between our SPIM flex-sensor\nand other reported piezoionic sensors, and performance comparison with other\nsensing mechanisms;\n11. The structural and material features that account for the markedly superior\nperformance of the eTPU-AgNP nanomesh electrode;\n12. Extending from single-joint monitoring to multi-joint deployment (wrist, elbow, and\nshoulder) and the realization of more complex combination of movements, such as\nthe “arm raising and hand waving” motion.\n13. Some other minor revisions were highlighted in yellow in the revised manuscript\nWe would like to appreciate the reviewers for their highly constructive\nremarks in concerning our manuscript. We believe that all the c oncerns raised by the\nreviewers have been addressed appropriately.\nResponse to Reviewers\nReviewer #1 (Remarks to the Author):\nThe authors have well addressed all my concerns. This work can be accepted.\nResponse: We sincerely appreciate your support and recognition of our work.\nReviewer #2 (Remarks to the Author):\nAuthors addressed all the comments raised by the reviewers.\nResponse: We sincerely appreciate your insightful and highly constructive suggestions\nfor the improvement of your work."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have well addressed all my concerns. This work can be accepted.", "reviewer_2": "Authors addressed all the comments raised by the reviewers"}, "author_response": "Response: We sincerely appreciate your support and recognition of our work.\nResponse: We sincerely appreciate your insightful and highly constructive suggestions\nfor the improvement of your work."}]

Decision letter