Facile patterning of hierarchical ionic microstructures for a pressure-sensitive ionic capacitive interface

Abstract

The emulation of human tactile perception is pivotal in advancing electronic skin technologies, necessitating pressure sensors with wide dynamic range capabilities. Conventional approaches relying on multilayered architectures yield bulky devices that resist miniaturization, particularly problematic for users with foreign body sensitivity. Here, we present a pressure-sensitive ionic capacitive interface (PSICI) with hierarchical microstructures by employing an iontronic sensing mechanism, demonstrating enhanced low-pressure sensitivity sufficient for detecting subtle physiological signals such as radial artery pulsations. A conformal sensor array engineered for curved surfaces enables spatially resolved pressure mapping on anatomical geometries. PSICI's seamless epidermal integration improves human–device interfacial fidelity through sensitive tactile feedback. Proof-of-concept demonstrations in joint motion monitoring illustrate its versatility across various wearable applications, including healthcare diagnostics, virtual reality systems, and intelligent wearables. The architecture's intrinsic simplicity and reliability position the PSICI as a promising platform for scalable epidermal electronics.

---

1 Introduction

Human skin, the body's largest organ, possesses a sophisticated network of mechanoreceptors essential for tactile perception. These receptors function as active transducers, dynamically detecting pressure variations during physical interactions. Such pressure sensing constitutes a foundational mechanism for closed-loop physiological regulation, enabling homeostasis maintenance. Furthermore, pressure-derived signals encode critical biomarkers indicative of systemic health status, providing valuable insights into human pathophysiology.^1–3 Contemporary advancements have transformed wearable devices from speculative concepts into sophisticated consumer and medical applications. Global research efforts now span diverse domains including smart textiles,^4,5 biomechanical tracking,^6,7 continuous health monitoring,^8,9 and flexible optoelectronics.^10,11 Among these, deformable electronic modules demonstrate exceptional design versatility and are widely regarded as the next evolutionary stage in wearable technology.^12,13 However, existing epidermal sensors face two critical limitations: (1) mandatory encapsulation layers in trilayer architectures (electrode/dielectric/electrode) increase device thickness (>500 μm), compromising skin conformability;^14,15 (2) interfacial motion between skin and rigid electrodes generates electrical noise (>20% signal fluctuation), degrading detection fidelity.¹⁶

Recent advancements propose hybrid liquid conductors combining carbon nanomaterials, liquid metals, and room-temperature ionic liquids (RT-ILs) as next-generation platforms for stretchable bioelectronics. These systems leverage intrinsic deformability with quasi-infinite fatigue resistance, enabling conformal integration with dynamic biological surfaces. Carbon suspensions (e.g., carbon nanotubes or graphene dispersions) offer cost-efficient and eco-friendly solutions, yet suffer from nanoparticle aggregation and post-solidification embrittlement that compromise device longevity under cyclic strain.^17,18 In contrast, eutectic gallium–indium (EGaIn) alloys demonstrate metallurgical-grade conductivity (3.4–3.5 × 10⁶ S m⁻¹ at 298 K), serving dual roles as compliant electrodes and strain-responsive elements in epidermal sensors.^19,20 RT-ILs—molten salts exhibiting negligible vapor pressure, non-flammability, and ionic conductivities exceeding 0.1 S cm⁻¹—have enabled microfluidic sensing architectures for multiparametric physiological monitoring.^21,22 Despite these merits, persistent challenges remain in achieving deformation-invariant sensing fidelity, particularly regarding viscoelastic hysteresis suppression in elastomeric substrates and time-dependent signal drift mitigation during large-strain operations (>200%).²³

In this study, we present a bioinspired hierarchical ionic architecture (HIM) platform engineered for synergistic resolution of the aforementioned challenges in tactile sensing. The innovation core comprises a pressure-modulated ionic capacitive interface (PSICI) with epidermal impedance coupling, where multi-scale elastomer microstructures enable graded mechanical transduction over 0–400 kPa. The prepared ionogel achieves long-term stability (>10 hours, <3% ion loss) through covalent polymer crosslinking, effectively resolving dehydration issues inherent to conventional hydrogels. By integrating Cu-EGaIn electrodes (σ > 3 × 10⁶ S m⁻¹) with the HIM dielectric layer, the PSICI platform demonstrates ultrahigh sensitivity (2.648 kPa⁻¹ at <15 kPa) for detecting subtle physiological signals, enabling precise detection of radial artery pulse waveforms, while maintaining excellent durability (±4% variation over 5000 cycles). The proposed PSICI exhibits competitive performance metrics in sensitivity, stability, and dynamic range relative to state-of-the-art ionic capacitive sensors (Table S1†), with experimental data further validating its practical advantages. Extended implementations further showcase HIM-based tactile sensors capable of discriminating complex kinematic patterns (e.g., finger flexion angles with 1.8° resolution) and wearable strain sensors with robust cycling stability. The architectural simplicity—requiring only two electrodes for multiplexed array sensing—enables seamless integration into wearable systems for applications spanning biophysical monitoring, telerehabilitation interfaces, and smart wearable systems. This ultra-simplified architecture not only achieves high pressure sensitivity but also underscores the PSICI platform's multifunctional applicability across healthcare diagnostics, human–machine interaction, and intelligent wearable development.

2 Results and discussion

The PSICI operates as an epidermal sensing platform for multimodal biomechanical signal acquisition (Fig. 1a). This iontronic system comprises three functionally distinct components (shown in Fig. 1b): a deformable sensing capacitive electrode (SCE) with HIMs, a flat reference capacitive electrode (RCE), and a biological impedance element (with stratum corneum resistance R_skin ∼100–500 kΩ cm²)²⁴ serving as a native circuit component. The geometrically invariant design of the RCE ensures baseline capacitance stability, while the microstructured sensing electrode promises high pressure sensitivity through dynamic contact area variation. In the PSICI sensing system, skin resistance remains approximately constant, thereby inducing a systematic relative error in pressure sensing measurements. However, comprehensive evaluation of skin resistance's impact on total capacitance differential remains imperative. Through application of the impedance analysis formula (formula (S1); see the ESI† for derivation), we demonstrate that skin resistance contributes a fixed and negligible error component to PSICI capacitance. This systematic error can be either compensated for or eliminated through calibration protocols to ensure accurate capacitive quantification. The optimization of the pyramid-shaped HIM enhances both the specific capacitance (0.03 μF cm⁻²) and the interfacial adhesion. The capacitance modulation mechanism operates through dynamic adjustment of the interfacial contact area between the ionic structure and cutaneous surface under applied mechanical stress, enabling reliable pressure-dependent signal acquisition. These hierarchical ionic microstructures demonstrate dual functionality of optimized capacitance density per unit area while maintaining exceptional biocompatibility and mechanical compliance for seamless epidermal integration.^25,26 Furthermore, the skin's inherent resistive properties in PSICI systems create an ionic-dominant circuit pathway. This characteristic significantly reduces the dependence of capacitance on interelectrode distance, thereby enabling simplified electrode configuration in practical applications.²⁴

Fig. 1 The mechanism and sensing performance of the PSICI. (a) Schematic of the PSICI array sensor on the skin and its simplified equivalent circuit. (b) Detailed view of the Sensing Capacitive Electrode (SCE) and Reference Capacitive Electrode (RCE) of the PSICI, along with their simplified equivalent circuits. (c) Structural diagrams of the RCE and SCE, accompanied by scanning electron microscope images of the corresponding ionic gel films. (d) Chemical structures of the three components used in the ionic elastomer: P(VDF-HFP) (elastomer, top left), [EMIM][TFSI] (ionic liquid, middle left), and HMDA (crosslinker, bottom left). (e) Photograph of the skin without the RCE patch connected to the PSICI. (f) Photograph of the skin immediately after connecting the RCE patch to the PSICI. (g) Photograph of the skin 8 hours after connecting the RCE patch to the PSICI. (h) Photograph of the skin after removing the RCE patch from the PSICI.

The structure of the RCE and SCE is shown in Fig. 1c. The functional SEC integrates a micropatterned ionic elastomer composite with EGaIn-based liquid metal circuitry, where the conductive phase is embedded in medical-grade polyurethane adhesive tape through wire-bonding interconnects. The RCE shares structural homology while substituting the textured elastomer with a conformal 200 μm-thick ionic film (Young's modulus < 5 kPa) optimized for epidermal compliance.²⁷ As illustrated in Fig. 1c, the elastomeric microstructure is fabricated through solvent casting of a poly(vinylidene fluoride-co-hexafluoropropylene) P(VDF-HFP) matrix plasticized with ionic liquid (1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide [EMIM][TFSI]) (Fig. 1d), which is an elastomer with a relatively high dielectric constant and promoted degree of ionization.²⁸ During the 8-hour occlusive patch testing, the skin's hydration is maintained, and there is essentially no occurrence of erythema or allergic reactions on the skin (Fig. 1e and f). The absence of ionic liquid residue further validates the P(VDF-HFP) matrix's ion-locking capability, which confines the [EMIM][TFSI] molecules within the ionogel while permitting pressure-responsive ion redistribution.

The capacitive sensing system comprises dual functional electrodes fabricated from ionically conductive elastomer composites ([EMIM][TFSI] matrixed with P(VDF-HFP)) and the EGaIn alloy. As detailed in Fig. 2a, a 3D printer is utilized to construct polylactic acid (PLA) molds with controlled microtopographies. The precursor formulation involves sequential dissolution of P(VDF-HFP) in acetone (99.8% purity) followed by incorporation of [EMIM][TFSI] ionic liquid and the hexamethylenediamine (HMDA) crosslinker under mechanical stirring (500 rpm, 25 °C, 4 h) as per Fig. 2b (see the Experimental section for detailed steps). Mold infiltration is achieved through centrifugal casting (200 rpm, 2 min) to ensure complete template surface coverage (Fig. 2c), with subsequent ambient curing (25 °C, 60% RH, 30 min). Following complete polymerization, the ionic gel film is demolded from the 3D-printed template using precision tweezers. As illustrated in Fig. S1,† scanning electron microscopy (SEM) characterization confirm that the template possesses vertically stepped microstructures with 30–50 μm height differentials, enabling replication of corresponding surface topography on the gel substrate. During precursor infiltration (Fig. S2†), localized air entrapment occurs predominantly at right-angle step regions, resulting in curved edge profiles (radius: 12.5 ± 3.2 μm) rather than sharp geometric transitions. The viscoelastic nature of the ionic gel induces biaxial strain during demolding, generating multi-lamellar wrinkle architectures (average wavelength: 8.7 ± 1.5 μm) that enhance epidermal conformality. For liquid metal electrode integration, medical-grade breathable tape serves as the dielectric substrate, achieving 63% size reduction while maintaining 85% vapor transmission rate. Fig. 2d details the fabrication sequence: (1) substrate lamination on the mask surface; (2) liquid metal (Cu-EGaIn alloy) brush-coating with controlled thickness (120 ± 15 μm); (3) interconnection stabilization using Kapton tape; (4) a single ion-capacitor interface is completed by attaching P(VDF-HFP)/[EMIM][TFSI] to a substrate.

Fig. 2 The fabrication process and ionic gel microstructures of the PSICI. (a) Flowchart for the process of using a 3D printer to create templates for the ionic gel film of the PSICI. (b) Flowchart for the preparation of the ionic gel prepolymer for the PSICI. (c) Flowchart illustrating the fabrication process of the ionic gel film with microstructures. (d) Schematic of the process for manufacturing the capacitive electrodes of the PSICI. (e) Photograph of the PSICI film. (f–h) Scanning electron microscope (SEM) images of the ionic gel microstructures, showing top views, side views, and a 45° angled top view.

Fig. 2e displays an optical micrograph of the ionic gel film, revealing an array of pyramid-shaped HIMs on its surface. These microstructures dynamically adapt their contact area with the skin in response to applied pressure. Furthermore, the minimized dimensions and thickness of both SCE and RCE components substantially mitigate foreign-body sensations during epidermal contact. Fig. 2f–h present the SEM images of the three-dimensional PSICI film with 0.6 mm height pyramid-shaped HIMs, demonstrating uniform structural integrity across multiple viewing angles. As shown in Fig. S3,† SEM analysis further confirms the successful fabrication of PSICI films with four distinct microstructure heights (0.4–1.0 mm) using 3D-printed templates, all exhibiting submillimeter-scale features with homogeneous spatial distribution. High-magnification SEM imaging of the 0.8 mm HIM surface (Fig. S4†) reveals micrometer-sized porous protrusions, which collectively enhance the pressure detection range through increased surface compliance and stress distribution.^9,29,30

The microstructural architecture and dielectric properties of ionic gel films with HIMs critically determine the performance of epidermal capacitive sensors. As demonstrated in Fig. 3a, ionic gel films contain mobile cation–anion pairs that align under operational conditions. Upon application of external bias, electron–counterion pairs accumulate at the electrode–gel interface within nanoscale proximity. Mechanical compression enhances this interfacial aggregation, thereby proportionally increasing capacitance through electrical double layer (EDL) formation at both gel–skin and gel–electrode interfaces. The sensing mechanism of the sensor depends on the change in interfacial capacitance.^22,31–33 The sensing mechanism fundamentally relies on dynamic EDL capacitance changes governed by two key parameters at constant temperature: the contact area of the HIMs and the ionic concentration within the compressed region. Microstructural stiffness, a critical factor controlling pressure sensitivity in capacitive transducers, can be systematically tuned through geometric parameter engineering. To validate the advantages of HIMs for epidermal applications, we conducted finite element analysis using COMSOL Multiphysics (Fig. S5†) to characterize their mechanical behavior under applied loads. In contrast to conventional microstructured interfaces, HIMs demonstrate a multistage deformation mechanism when subjected to three-dimensional surface forces: the hierarchical conical layers undergo sequential deformation phases, resulting in localized stress concentration at the apex regions. This distinct deformation characteristic enables uniform interfacial compression, thereby promoting the formation of a homogeneous EDL contact area—a crucial determinant of capacitive sensing stability. Comparatively, conventional non-hierarchical architectures (e.g., pyramidal or cylindrical arrays) exhibit non-uniform stress distribution, inducing spatial EDL heterogeneity that compromises measurement stability. By maintaining a constant microstructure diameter (D = 0.4 mm) while varying HIM height (H = 0.4–1.0 mm), we achieve precise control of the H/D ratio (1.0–2.5), enabling optimization of the ionic gel film compliance for specific pressure sensing ranges. We have systematically evaluated the capacitance variations in four-layered HIMs with different heights (0.4–1.0 mm) across multiple pressure ranges (Fig. S6–S9†). Statistical analysis of their pressure sensing characteristics reveals that the 0.6 mm microstructure demonstrates optimal sensitivity and operational range (Fig. 3b). The PSICI device exhibits three distinct response phases of low-pressure sensitivity (23.86 MPa⁻¹ at <1 kPa), enhanced sensitivity of 73.62 MPa⁻¹ at 10–82 kPa, and progressive signal saturation (12.26 MPa⁻¹ at 82–320 kPa) (Fig. S7d†). These operational phases emerge from the synergistic coupling between ionic concentration gradients and micromechanical properties. When formulated with suboptimal IL content (0.2 g), restricted ion mobility impedes EDL development, compromising sensitivity. Excessive IL loading (1.5 g) induces phase separation in the P(VDF-HFP) matrix, precipitating structural instability (Fig. S10†). The 1.0 g IL composition demonstrates optimal equilibrium: its microphase-separated architecture enables pressure-modulated ionic redistribution while maintaining mechanical integrity. Our theoretical model (eqn (S2), ESI†) provides a quantitative framework elucidating the triphasic sensitivity response observed in EDL-based capacitive sensors. During low-pressure operation (<10 kPa), capacitive output predominantly arises from concurrent microstructural deformation and interfacial ion accumulation. At moderate pressures (10–50 kPa), ionic migration approaches transport limitation, decelerating Debye length contraction while matrix stiffening attenuates Stern-layer compression efficacy. Under high compressive loads (>50 kPa), synergistic ionic saturation and microstructural densification terminate interfacial expansion, culminating in capacitance plateau formation. This mechanoelectrical coupling model quantitatively reconciles pressure-dependent EDL evolution with sensor nonlinearity, demonstrating strong congruence with experimental characterization (Fig. S11†).

Fig. 3 PSICI sensor performance and characterization on the skin. (a) Schematic diagram of the PSICI array sensor on the skin and the elastic contact between the stratified pyramidal structure of the ionic gel film and the skin, where the corresponding COMSOL simulation is shown. (b) Normalized variation of capacitance with tip pressure for PSICIs with different microstructure heights measured at 5 × 10⁴ Hz. (c) Signal stability of the PSICI over 5000 loading/unloading cycles at a peak pressure of 150 kPa. (d) Photographs of the PSICI under stretching (approximately 5%) and compressing conditions. (e) Capacitance change graph when touching the SCE and RCE. Negligible output when touching the RCE. (f) Capacitance variation of the sensing electrode at 0, 2, 4, 6, 8, 10, and 12 hours during rest and under ∼50 kPa pressure. (g) Normalized capacitance change at different angle variations. (h) Distribution diagram of the SCE and RCE on the skin and capacitance change values of the SCE at different distances from the RCE.

These multi-stage sensing behaviors originate from the hierarchical surface architecture of the ionic gel in the PSICI. Dynamic characterization reveals rapid response capabilities with 70 ms loading and 140 ms unloading cycles (Fig. S12†). Extended cyclic compression testing (5000 cycles at 5 N loading) demonstrated exceptional durability of the PSICI films, with capacitance maintained within a ±4% variation envelope and minimal baseline drift (Fig. 3c). The initial <3% capacitance elevation originates from mechanical conditioning-induced material densification, while transient mid-test fluctuations subside through optimized ionic redistribution, validating the system's autonomous stress adaptation capability during sustained mechanical loading. This may be attributed to the continuously applied pressure, resulting in further contact between the ionic gel and the skin and leading to a slight increase in the capacitance of the SCE. The epidermal-integrated PSICI demonstrates superior conformability, maintaining structural integrity under 30% tensile strain and cyclic compression (Fig. 3d). Substrate breathability was systematically evaluated through performance benchmarking (Fig. S13†). Micropatterned polyethylene terephthalate (PET) substrates (500 μm pore diameter) demonstrated sustained sensing stability (<2% baseline drift/12 h) through synergistic integration of mechanical reinforcement and transdermal moisture regulation. In contrast, silk-based substrates exhibited compromised interfacial integrity despite superior breathability due to progressive delamination. For acute clinical monitoring scenarios demanding signal fidelity prioritization, a non-porous medical-grade adhesive substrate was implemented to guarantee interfacial adhesion stability and baseline signal preservation. This comprehensive evaluation establishes micropatterned PET as the optimal substrate selection for long-term epidermal applications.

Notably, the RCE exhibits negligible signal interference (ΔC < 0.8 pF) during tactile interactions (Fig. 3e), confirming PSICI's selective pressure sensing capability. Long-term monitoring over 12 hours (Fig. 3f) reveals a gradual baseline drift (∼0.3 nF fluctuation) caused by sweat-induced ion accumulation at the skin-sensor interface, while active pressure signals under 0.2 N loading exhibit a distinguishable capacitance shift (∼0.8 nF), ensuring minimal impact on sensing fidelity. This may be attributed to the increased ion concentration in the EDL caused by sweating on the skin surface.³² Conversely, focusing on the capacitance variations of the PSICI during joint flexion, we have further explored its behavior across a wide range of bending angles (0–135°). Our findings indicate that within a 30° bend at the RCE, the capacitance change remains minor (capacitance variation < 2%). Beyond this angle, the rate of capacitance variation for the RCE escalates, culminating in a 6.5-fold increase in capacitance at a 135° bend compared to the baseline scenario (as shown in Fig. 3g). Considering that the SCE is distributed across different parts of the body, we have also measured the effect of the SCE and RCE on capacitance at different distances. Distance-dependent tests (20–200 cm) reveal the minimal resistance impact (ΔC < 50 pF) on PSICI signal acquisition (Fig. 3h).

The PSICI, specifically designed for human skin, represents a promising alternative to a smart wristband for pulse signal measurement. Following a comprehensive analysis of its fundamental performance, we have devised three fundamental application schemes for the PSICI: pulse detection, pressure sensing arrays, and sliding motion sensation. The SCE component of the PSICI exhibits an exceptionally low detection threshold for pressure sensing, enabling it to detect subtle vibrations on the skin and facilitating the measurement of pulse signals at the wrist. Fig. 4a shows the position of the SCE at the wrist pulse point. To investigate the influence of sweating on pulse measurements, subjects are instructed to measure changes in pulse capacitance before, immediately after, and 30 minutes post-exercise, involving in-place running for 15 minutes. As depicted in Fig. 4b, the baseline capacitance increases by approximately 100 pF post-exercise compared to pre-exercise levels and gradually plateaus 20 minutes after exercise cessation, yet remains elevated by roughly 15 pF compared to the baseline. Post-exercise perspiration residues (e.g., Na⁺, K⁺, and Cl⁻) transiently elevate the ionic concentrations at the interface between the PSICI and the skin, thereby increasing baseline capacitance magnitude. Crucially, pulse waveform characteristics maintain temporal consistency across pre-exercise, exercise, and 30-minute recovery phases (Fig. 4c), exhibiting <7% peak-to-peak variation regardless of the perspiration state. Extended characterization (11-hour protocol, Fig. S14†) demonstrated baseline capacitance perturbations during sequential perspiration episodes (three exercise cycles), with spontaneous restoration achieving 95% pre-exercise level recovery. A slight baseline drift (∼2%) during long-term analysis was noted following multiple sweating cycles, originating from interfacial ionic adsorption accumulation. The ionogel's inherent design—comprising hydrophobic P(VDF-HFP) matrices and non-aqueous [EMIM][TFSI] ionic liquid—establishes an electrochemical barrier preventing sweat-borne ion penetration into the active sensing domain or EDL modulation.³² Capacitance response dynamics remain governed by stress-mediated ionic reorganization within the elastomeric network, independent of surface-adsorbed electrolytes. Post-evaporation, the material's autonomous recovery mechanism leverages thermal-driven ionic redistribution to restore original EDL configurations (recovery efficiency >95%). While residual ions may cause negligible long-term drift (∼2%), this has minimal impact on pressure sensitivity or real-time signal fidelity throughout operational cycles. The pulse capacitance signal depicted in Fig. 4d demonstrates that the wrist pulse sensor of the PSICI can precisely capture the P-, T-, and D-waves in the pulse electrocardiogram (ECG).³³ Consequently, the PSICI is capable of reliably measuring the radial artery's pulse ECG signal. Furthermore, as previously noted, the ionic nature of the skin ensures that the inter-electrode distance has negligible influence on capacitance, significantly facilitating electrode deployment on the skin and enabling multiple SCEs to share a single RCE for sensing array configurations.

Fig. 4 PSICI for pulse monitoring and pressure mapping on the skin. (a) Photograph of the SCE of the PSICI on the pulse point of the hand. (b) Capacitance changes of the PSICI recorded from a subject during a 15-minute exercise period, followed by a return to a stationary state after 30 minutes. (c) Pulse waveform and pulse rate variation diagrams before exercise, after exercise, and 30 minutes post-exercise. (d) Magnified pulse waveform diagram. (e) Schematic diagram and (ii) pressure distribution of the PSICI's 4 × 4 array with a weight placed on it. (f) Schematic diagram of the PSICI's 1 × 4 array. (g) Normalized capacitance change when tapping each of the 4 SCEs individually. (h) Normalized capacitance change diagram as a finger slides sequentially over the 4 SCEs.

The PSICI exhibits a simplified bilayer architecture with a co-planar electrode configuration, enabling enhanced signal transmission range compared to conventional capacitive sensors. This structural advantage facilitates the development of epidermal sensor arrays comprising a single RCE and multiple SCEs. As demonstrated in Fig. 4e(i) and ESI S15a,† our 4 × 4 SCE array successfully achieves pressure mapping on curved skin surfaces. Experimental validation involves affixing the array to a volunteer's forearm using medical-grade adhesive, followed by localized application of a 100 g weight to the superior-left SCE. Capacitance differential analysis reveals more significant variations in the loaded SCEs compared to baseline measurements, as shown in Fig. 4e(ii). The non-uniform capacitance variations observed under identical loads arise from the conformal contact between the soft PSICI and anatomically curved human forearm surfaces. These topographically induced deviations can be systematically addressed through the implementation of comprehensive mitigation strategies combining mechanical interface engineering and adaptive signal compensation algorithms. Notably, secondary capacitive fluctuations in non-loaded SCEs (ΔC = 3.2 ± 0.8 pF) suggest micro-deformation effects caused by dermal tissue displacement under mechanical loading. In addition, as shown in Fig. 4f and S15b,† we have designed a 1 × 4 array of SCEs to demonstrate the distinct signal discrimination capabilities between discrete tactile input and continuous sliding interactions. Discrete tapping events generate isolated capacitive pulses with temporal resolution <50 ms and a significant signal-to-noise ratio, enabling precise spatial localization. During continuous hand-sliding experiments, the system exhibits sequential capacitance activation patterns with 92.3% temporal continuity preservation. Notably, the peak capacitance variation (ΔC_max = 15.7 pF) occurs during simultaneous contact with SCE2 and SCE3, corresponding to maximum epidermal deformation regions. This spatiotemporal sensing modality facilitates potential dynamic gesture tracking through integration with machine learning algorithms. The PSICI array architecture shows particular promise for developing epidermal electronic interfaces, including skin-friendly interactive keyboards or touch screens.

The PSICI demonstrates exceptional sensitivity to micro-deformations, which enables its deployment as a multimodal biometric monitoring system for human joint kinematics. Taking advantage of the PSICI's characteristics, particularly the negligible effect of the distance between the SCE and RCE on signal magnitude, we have developed a comprehensive motion detection solution for whole-body movement monitoring (Fig. 5a and b). Strategic placement of SCEs at some major articulation points (such as the elbow, wrist, finger joints, fingertips, and knee) synergizes with the RCE attached to the abdomen. This biomechanical sensor array operates through two complementary subsystems: the upper-body module, integrating wrist pulse waveform detection with elbow/wrist/finger angular position tracking, and the lower-body module, combining foot strike pattern recognition with ankle proprioceptive ability. Given that the amplitudes of pulse signals are typically much smaller compared to other joint motion signals, they can be calibrated using the elbow's motion signals to enhance the reliability of the detection of pulse signals (Fig. S16†). Similarly, step frequency signals can be refined through cross-calibration between the ankle and foot sensors. The data collected from these sensors are processed to analyze and interpret motion patterns, providing a detailed understanding of the body's kinetic activities. This processed data is then transmitted to the display system, where it can be presented through a mobile application, allowing for real-time monitoring and historical analysis of the user's physical activities (Fig. 5a).^34,35 The PSICI demonstrates a precise angular displacement-dependent capacitive response across human articulation points, as systematically characterized in Fig. 5c. Quantitative analysis reveals capacitive variation related to different elbow flexion angles. Wrist pronation-supination movements generate biphasic capacitive signatures (Fig. 5d), where dermal electrode deformation induces extension and flexion differential signals through strain-mediated permittivity modulation. Various finger motions can also be monitored to exhibit rapid capacitive transition dynamics (ESI Fig. S17a†), which are independent of the contact duration and able to return to the initial capacitance once the pressure is removed. Joint motion tracking extends to metacarpophalangeal articulations and knee kinematics (Fig. S17b,† and 5e), achieving distinguishable angular resolution through differential SCE–RCE signal processing.

Fig. 5 PSICI for human motion detection and joint monitoring. (a) System block diagram of the whole-body motion detection application based on the PSICI. (b) Schematic representation of SCE sensors placed at various joint locations on the human body, along with a conceptual diagram of the accompanying app. (c) Capacitive response graphs of an arm equipped with PSICI sensors in both flexed and unflexed positions. (d) Capacitive response of the wrist with PSICI sensors during supination and rotation. (e) Capacitive response measured by PSICI sensors for the flexion/extension of the knee joint. (f) Capacitive response detected by PSICI sensors for foot motion.

Real-time knee motion analysis during biomechanical tasks demonstrates clear static squatting, gait cycles, and dynamic squatting. As shown in Fig. 5f, during the slow squatting process, the capacitance changes minimally when the squatting position is maintained. In contrast, the change in capacitance during walking is slightly less pronounced due to the lower amplitude of knee movement, yet the number of joint movements can be clearly recorded. The sensitivity to subtle movements and the capability to accurately record a variety of motion patterns render the PSICI an ideal candidate for applications in health monitoring, sports science, and interactive devices. The PSICI's high-dimensional motion data generation creates new paradigms in precision physiotherapy and smart human–computer interfaces.

3 Conclusions

This study presents a PSICI that redefines epidermal sensing through bio-inspired ionic–electronic synergy for cardiovascular monitoring, tactile interfaces, and dynamic gesture recognition. Three fundamental achievements distinguish our approach, including structural innovation of replacing the conventional trilayer architecture with an ionic dual-electrode system, excellent dynamic performance through interfacial EDL modulation, and system integration with multi-SCE/RCE signal decoupling for whole-body motion capture. The PSICI's unique electrode displacement tolerance facilitates unconventional implementations beyond epidermal sensing. Future development will focus on neuromorphic computing integration (spiking LSTM networks) and self-powered operation through piezo/tribo–iontronic coupling. This technology establishes a new paradigm for human–machine symbiosis in healthcare, sports biomechanics, and neuroprosthetics.

4 Experimental section

4.1 Materials

[EMIM][TFSI] was purchased from Sigma-Aldrich and used as received. Thermoplastic copolymer (P(VDF-HFP)) was purchased from Beijing JOHN LONG Co., Ltd. EGaIn alloy (75.5 wt% gallium, 24.5 wt% indium) was purchased from Changsha Santech Materials Co. Ltd and used as the metallic liquid base. The cured 3D printing material was made of LCD photosensitive resin from FLASHFORGE Co., Ltd. The medical-grade breathable tape and Kapton tape were purchased online.

4.2 Device preparation

The molds in the experiments were all printed using a FLASHFORGE Foto 6.0 light-curing 3D printer. All 3D printed molds were sequentially subjected to ultrasonic treatment for 5 minutes in 100% ethanol, 50% ethanol, and deionized water, respectively. Subsequently, the molds were cured under ultraviolet light at a wavelength of 395 nm for 30 seconds and then dried at ambient temperature. First, 1 g of P(VDF-HFP) was dissolved in 5 g of acetone solution using a magnetic stirrer for 10 min to form a clarified solution. Then 1 g of [EMIM][TFSI] was added to the above clarified solution and stirred for 2 hours. Next, 1 g of DMF was added to the above solution and stirred for 30 minutes. Finally, 0.1 g of HMDA was dissolved in 1.2 mL of acetone and stirred for 30 minutes. Then, 120 μL of this solution was added to the prepolymer solution, stirring for 1 hour to obtain the ionic gel prepolymer. The ionic gel prepolymer was poured into the prepared template, permitted to solidify at room temperature, and then removed from the mold to obtain the ionic gel film. A PET sheet with a pre-engraved pattern was placed atop medical tape, followed by the application of liquid metal to form a conductive electrode. Upon peeling off the PET sheet, a patterned liquid metal electrode was obtained. The ionic gel film was then transferred onto the patterned liquid metal to fabricate a single electrode of the PSICI. In this configuration, the ionic gel film serves as the RCE when it is smooth and as the SCE when it features microstructures. The medical tape, with its single-sided adhesion, allows for direct application on the skin and can be substituted with alternative supporting materials as needed.

4.3 Measurement and characterization

The capacitance of the pressure transducer containing the PSICI was measured under ambient conditions using a precision LCR meter (Agilent E4980A). For pressure-related capacitance change measurements, a computer-controlled displacement table (Teraleader) was set up together with an LCR meter. The device has a vertical spatial resolution of 1 μm and a force resolution of 0.01 N. To detect various pressure sources, including wrist vein pulses, finger touches and human joint movements, the same LCR meter measurement setup was used, but without the additional amplification components. The surface morphology of the micropatterned ionic elastomer composite and 3D printed template was examined by SEM (Quanta 450).

Ethical statement

Informed consent was obtained from all subjects.

Data availability

The data are available from the corresponding author on reasonable request.

Author contributions

Jiahong Yang and Qijun Sun: conceptualization, methodology, experiments, data analysis, writing. Yao Xiong and Yang Liu: methodology, experiments. Shishuo Wu: experiments, data analysis. Rui Gu and Chao Liu: methodology, data analysis. Zhong Lin Wang and Qijun Sun: project management, manuscript revision.

Conflicts of interest

The authors declare that there are no competing interests.

Acknowledgements

This work was financially supported by the National Key Research and Development Program from the Ministry of Science and Technology (2023YFB3208102), the National Natural Science Foundation of China (52073031), and the “Hundred Talents Program” of the Chinese Academy of Sciences.

References

1. J. Heikenfeld, A. Jajack, J. Rogers, P. Gutruf, L. Tian, T. Pan, R. Li, M. Khine, J. Kim, J. Wang and J. Kim, Lab Chip, 2018, 18, 217–248.
2. X. Lin, Z. Feng, Y. Xiong, W. Sun, W. Yao, Y. Wei, Z. L. Wang and Q. Sun, Int. J. Extrem. Manuf., 2024, 6, 032011.
3. Y. Luo, M. R. Abidian, J.-H. Ahn, D. Akinwande, A. M. Andrews, M. Antonietti, Z. Bao, M. Berggren, C. A. Berkey, C. J. Bettinger, J. Chen, P. Chen, W. Cheng, X. Cheng, S.-J. Choi, A. Chortos, C. Dagdeviren, R. H. Dauskardt, C.-a. Di, M. D. Dickey, X. Duan, A. Facchetti, Z. Fan, Y. Fang, J. Feng, X. Feng, H. Gao, W. Gao, X. Gong, C. F. Guo, X. Guo, M. C. Hartel, Z. He, J. S. Ho, Y. Hu, Q. Huang, Y. Huang, F. Huo, M. M. Hussain, A. Javey, U. Jeong, C. Jiang, X. Jiang, J. Kang, D. Karnaushenko, A. Khademhosseini, D.-H. Kim, I.-D. Kim, D. Kireev, L. Kong, C. Lee, N.-E. Lee, P. S. Lee, T.-W. Lee, F. Li, J. Li, C. Liang, C. T. Lim, Y. Lin, D. J. Lipomi, J. Liu, K. Liu, N. Liu, R. Liu, Y. Liu, Y. Liu, Z. Liu, Z. Liu, X. J. Loh, N. Lu, Z. Lv, S. Magdassi, G. G. Malliaras, N. Matsuhisa, A. Nathan, S. Niu, J. Pan, C. Pang, Q. Pei, H. Peng, D. Qi, H. Ren, J. A. Rogers, A. Rowe, O. G. Schmidt, T. Sekitani, D.-G. Seo, G. Shen, X. Sheng, Q. Shi, T. Someya, Y. Song, E. Stavrinidou, M. Su, X. Sun, K. Takei, X.-M. Tao, B. C. K. Tee, A. V.-Y. Thean, T. Q. Trung, C. Wan, H. Wang, J. Wang, M. Wang, S. Wang, T. Wang, Z. L. Wang, P. S. Weiss, H. Wen, S. Xu, T. Xu, H. Yan, X. Yan, H. Yang, L. Yang, S. Yang, L. Yin, C. Yu, G. Yu, J. Yu, S.-H. Yu, X. Yu, E. Zamburg, H. Zhang, X. Zhang, X. Zhang, X. Zhang, Y. Zhang, Y. Zhang, S. Zhao, X. Zhao, Y. Zheng, Y.-Q. Zheng, Z. Zheng, T. Zhou, B. Zhu, M. Zhu, R. Zhu, Y. Zhu, Y. Zhu, G. Zou and X. Chen, ACS Nano, 2023, 17, 5211–5295.
4. C. Dong, A. Leber, T. Das Gupta, R. Chandran, M. Volpi, Y. Qu, T. Nguyen-Dang, N. Bartolomei, W. Yan and F. Sorin, Nat. Commun., 2020, 11, 3537.
5. S. Zheng, X. Wang, W. Li, Z. Liu, Q. Li and F. Yan, Nat. Electron., 2024, 7, 576–585.
6. S. Sundaram, P. Kellnhofer, Y. Li, J. Y. Zhu, A. Torralba and W. Matusik, Nature, 2019, 569, 698–702.
7. H. Jiao, X. Lin, Y. Xiong, J. Han, Y. Liu, J. Yang, S. Wu, T. Jiang, Z. L. Wang and Q. Sun, Nano Energy, 2024, 120, 109134.
8. G. Khandelwal, N. P. Maria Joseph Raj and S.-J. Kim, Nano Today, 2020, 33, 100882.
9. P. Zhu, H. Du, X. Hou, P. Lu, L. Wang, J. Huang, N. Bai, Z. Wu, N. X. Fang and C. F. Guo, Nat. Commun., 2021, 12, 4731.
10. J. Yang, J. Cao, J. Han, Y. Xiong, L. Luo, X. Dan, Y. Yang, L. Li, J. Sun and Q. Sun, Nano Energy, 2022, 101, 107582.
11. L. Sun, J. Wang, H. Matsui, S. Lee, W. Wang, S. Guo, H. Chen, K. Fang, Y. Ito, D. Inoue, D. Hashizume, K. Mori, M. Takakuwa, S. Lee, Y. Zhou, T. Yokota, K. Fukuda and T. Someya, Sci. Adv., 2024, 10, eadk9460.
12. M. J. Cima, Nat. Biotechnol., 2014, 32, 642–643.
13. B. C. K. Tee, A. Chortos, A. Berndt, A. K. Nguyen, A. Tom, A. McGuire, Z. C. Lin, K. Tien, W.-G. Bae, H. Wang, P. Mei, H.-H. Chou, B. Cui, K. Deisseroth, T. N. Ng and Z. Bao, Science, 2015, 350, 313–316.
14. J. C. Yang, J.-O. Kim, J. Oh, S. Y. Kwon, J. Y. Sim, D. W. Kim, H. B. Choi and S. Park, ACS Appl. Mater. Interfaces, 2019, 11, 19472–19480.
15. Q. Zhou, C. Zhu, H. Xue, L. Jiang and J. Wu, ACS Appl. Mater. Interfaces, 2024, 16, 35268–35278.
16. J. Heikenfeld, A. Jajack, J. Rogers, P. Gutruf, L. Tian, T. Pan, R. Li, M. Khine, J. Kim, J. Wang and J. Kim, Lab Chip, 2018, 18, 217–248.
17. J. Yu, W. Lu, S. Pei, K. Gong, L. Wang, L. Meng, Y. Huang, J. P. Smith, K. S. Booksh, Q. Li, J. H. Byun, Y. Oh, Y. Yan and T. W. Chou, ACS Nano, 2016, 10, 5204–5211.
18. Y. Qian, Y. Zhou, M. Lu, X. Guo, D. Yang, H. Lou, X. Qiu and C. F. Guo, Small Methods, 2021, 5, e2001311.
19. Z. Zhao, S. Soni, T. Lee, C. A. Nijhuis and D. Xiang, Adv. Mater., 2023, 35, 2203391.
20. Y. Yang, J. Han, J. Huang, J. Sun, Z. L. Wang, S. Seo and Q. Sun, Adv. Funct. Mater., 2020, 30, 1909652.
21. Q. Su, Q. Zou, Y. Li, Y. Chen, S. Y. Teng, J. T. Kelleher, R. Nith, P. Cheng, N. Li, W. Liu, S. Dai, Y. Liu, A. Mazursky, J. Xu, L. Jin, P. Lopes and S. Wang, Sci. Adv., 2021, 7, eabi4563.
22. S. H. Cho, S. W. Lee, S. Yu, H. Kim, S. Chang, D. Kang, I. Hwang, H. S. Kang, B. Jeong, E. H. Kim, S. M. Cho, K. L. Kim, H. Lee, W. Shim and C. Park, ACS Appl. Mater. Interfaces, 2017, 9, 10128–10135.
23. D. Y. Choi, M. H. Kim, Y. S. Oh, S. H. Jung, J. H. Jung, H. J. Sung, H. W. Lee and H. M. Lee, ACS Appl. Mater. Interfaces, 2017, 9, 1770–1780.
24. K. Ehtiati, J. Eiler, A. Bochynska, L. L. Nissen, E. Strøbech, L. F. Nielsen and E. Thormann, ACS Appl. Bio Mater., 2023, 6, 3033–3051.
25. J. Shi, Y. Dai, Y. Cheng, S. Xie, G. Li, Y. Liu, J. Wang, R. Zhang, N. Bai, M. Cai, Y. Zhang, Y. Zhan, Z. Zhang, C. Yu and C. F. Guo, Sci. Adv., 2023, 9, eadf8831.
26. K. Yao, Q. Zhuang, Q. Zhang, J. Zhou, C. K. Yiu, J. Zhang, D. Ye, Y. Yang, K. W. Wong, L. Chow, T. Huang, Y. Qiu, S. Jia, Z. Li, G. Zhao, H. Zhang, J. Zhu, X. Huang, J. Li, Y. Gao, H. Wang, J. Li, Y. Huang, D. Li, B. Zhang, J. Wang, Z. Chen, G. Guo, Z. Zheng and X. Yu, Sci. Adv., 2024, 10, eadq9575.
27. M. K. Kwak, H.-E. Jeong and K. Y. Suh, Adv. Mater., 2011, 23, 3949–3953.
28. Y. Liu, C. Zhao, Y. Xiong, J. Yang, H. Jiao, Q. Zhang, R. Cao, Z. L. Wang and Q. Sun, Adv. Funct. Mater., 2023, 33, 2303723.
29. N. Bai, L. Wang, Q. Wang, J. Deng, Y. Wang, P. Lu, J. Huang, G. Li, Y. Zhang, J. Yang, K. Xie, X. Zhao and C. F. Guo, Nat. Commun., 2020, 11, 209.
30. S. Zhang, J. J. Jiang, Q. Jiang, W. J. Fei, C. A. F. Guo and Z. G. Wu, Adv. Mater. Technol., 2021, 6, 2001274.
31. C. Keplinger, J.-Y. Sun, C. Foo Choon, P. Rothemund, M. Whitesides George and Z. Suo, Science, 2013, 341, 984–987.
32. L. B. Baker, J. B. Model, K. A. Barnes, M. L. Anderson, S. P. Lee, K. A. Lee, S. D. Brown, A. J. Reimel, T. J. Roberts, R. P. Nuccio, J. L. Bonsignore, C. T. Ungaro, J. M. Carter, W. Li, M. S. Seib, J. T. Reeder, A. J. Aranyosi, J. A. Rogers and R. Ghaffari, Sci. Adv., 2020, 6, eabe3929.
33. Y. Xiong, J. Han, Y. Wang, Z. Lin Wang and Q. Sun, Research, 2022, 9867378.
34. S. Duan, Q. Shi, J. Hong, D. Zhu, Y. Lin, Y. Li, W. Lei, C. Lee and J. Wu, ACS Nano, 2023, 17, 1355–1371.
35. N. Li, Z. Wang, X. Yang, Z. Zhang, W. Zhang, S. Sang and H. Zhang, Adv. Funct. Mater., 2024, 34, 2314419.

---

Footnote

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d5ta01721a

---