Impedance-driven capacitance amplification in dielectric gradient all-fiber non-ionic electronic skin

Domain: biology
Published: 2026-03-27
Editorial outcome: minor_revision

Abstract
Non-ionic electronic skins offer intrinsic environmental stability, avoiding the leakage, volatility, and temperature sensitivity that limit ionic sensing systems. Yet, capacitive sensors based on electronic polarization typically exhibit low sensitivity because their dielectric modulation is confined to a single mode. Here, we introduce a dielectric-gradient, fully fiber-integrated non-ionic capacitive architecture that employs an impedance-driven enhancement mechanism. Controlled fiber deformation establishes a dual-variable dielectric network in which pressure-induced reduction of interfacial resistance and impedance releases suppressed polarization, yielding amplified capacitance far beyond that of conventional non-ionic sensors. The resulting device achieves ultrahigh sensitivity of 169.8 kPa-1 over a wide range of 20 Pa–8 MPa and maintains stable operation from −80 °C to 200 °C with less than 6% deviation. When integrated into a tactile-sensing glove and combined with machine learning, it attains 99.25% accuracy in recognizing multiple operational tools under both cryogenic and high-temperature conditions. These findings establish impedance engineering as a universal strategy for constructing high-gain, thermally robust, and reliable non-ionic electronic skins, enabling precision tactile sensing in environments previously inaccessible to flexible electronics.

Introduction
The development of flexible electronic skin is shifting from laboratory demonstrations toward long-term applications in harsh environments1-4. For robotic manipulation, human–machine collaboration at extreme temperatures, and in situ monitoring of critical equipment, sensors must detect weak pressures with high resolution while maintaining stable operation under low and high temperatures and complex mechanical loads. Current progress shows that achieving both high sensitivity and environmental stability remains difficult5-7. This conflict has become a key bottleneck preventing electronic skin from being deployed in real-world scenarios. Notably, recent capacitive and iontronic sensors have already achieved wide pressure ranges and high sensitivities through graded dielectric designs, hierarchical porous architectures, and impedance-engineered interfaces, highlighting the effectiveness of structural design in boosting signal output8-11.

Meanwhile, beyond environmental stability, practical wearable and implantable applications further requires tissue-like conformal contact to maintain long-term signal fidelity on soft and dynamically deforming surfaces. Recent advances in tissue-like wearable and implantable bioelectronics have underscored that long-term signal fidelity critically depends on device structure conformality, such as mechanically compliant, and stable contact with biological tissues. Notable strategies, including conformal nanomembranes12, soft morphing interfaces13, and mechanically matched architectures14,15, which have demonstrated improved bio integration and reduced motion-induced artifacts, enabling more reliable sensing in practical wearable settings. However, integrating tissue-like conformality with wide-temperature robustness and drift-resistant readout under harsh conditions remains nontrivial, especially for sensing schemes in which signal generation relies on mobile-ion transport.

Ionic pressure sensors generate large capacitance variations under small loads through the coupling of ionic migration and interfacial capacitance governed by the electric double layer effect. This mechanism has driven the rapid development of highly sensitive tactile perception in recent years16-18. The physical basis lies in the displacement of mobile ions and the accumulation of interfacial charges, enabling pressure resolution comparable to that of human skin19-21. However, such performance often comes with practical challenges in harsh environments, because ionic systems relying on mobile ions may suffer from leakage, evaporation, and thermal degradation16,22, and their electrical response can exhibit pronounced temperature dependence. At low temperature, ion freezing reduces the response, while at high temperature, excessive activation causes signal drift and mismatch. Even with improved encapsulation or electrolyte formulations, the temperature dependence and possible phase transitions of ionic transport remain difficult to eliminate. As a result, maintaining reliable operation across a wide temperature range is still unrealistic23,24.

Non-ionic capacitive sensors rely on electronic polarization rather than ionic conduction and therefore possess superior material stability, thermal endurance, and encapsulation compatibility7,25-27. Extensive research has focused on enhancing their dielectric constants and structural compliance. Reported strategies include incorporating high-permittivity fillers, constructing porous or microstructure layers, and designing multilayer or gradient geometries to improve stress transfer28,29. These approaches have enhanced low-pressure responses and extended the detectable range to some extent, but they have not fundamentally broken through the sensitivity ceiling of non-ionic systems30,31. The limited polarizable volume and modest enhancement of the effective electric field make it difficult to balance initial capacitance and noise. As pressure increases, contact rearrangement gradually saturates, and the signal gain reaches a plateau with reduced linearity. Most current methods rely on geometric or single dielectric-channel amplification and fail to address the key impedance factors that suppress polarization in non-ionic systems32-34.

The measured capacitance of non-ionic sensors depends not only on the number of polarizable dipoles and the effective contact area but also on the suppression of polarization caused by series impedance. Incomplete interfacial contact, dielectric losses, and local high resistance in the electrode network jointly determine the polarization time scale through the equivalent series resistance and the operating frequency, which lowers the apparent capacitance within the working range17,35,36. Such suppressed polarization, often overlooked in conventional geometric optimization, prevents materials with high intrinsic polarizability from expressing their full response potential. The existence of this impedance-induced suppression indicates that the performance limitation of non-ionic sensors originates not from insufficient dielectric modulation but from the lack of a mechanism that can release the constrained polarization under operating conditions37.

To address this issue, we introduce a dielectric-gradient all-fiber capacitive architecture that enables impedance-driven capacitance enhancement. In this design, a cooperative network composed of highly conductive electrode fibers and low-conductive dielectric fibers, together with pressure-induced fiber rearrangement, reduces interfacial impedance and activates dipole polarization simultaneously. The combined effects of decreased impedance suppression and expanded polarizable volume create a synergistic amplification of the capacitive signal. Frequency-domain analysis reveals a clear transition from resistive-dominant to capacitive-dominant behavior, while finite-element simulations and microscopic observations confirm pressure-induced interfacial reconstruction and the formation of new dielectric pathways. The resulting sensor achieves a high sensitivity of 169.84 kPa⁻¹, a broad detection range from 20 Pa to 8 MPa, and stable performance between −80 °C and 200 °C with less than six percent deviation. When integrated into a machine-learning-assisted tactile glove, it attains an object-recognition accuracy of 99.25 percent even under extreme temperature conditions. This impedance-driven amplification mechanism provides a new pathway for non-ionic systems to achieve iontronic-level sensitivity while maintaining environmental robustness, and it establishes a general framework for reliable flexible tactile sensing in complex and demanding environments.

Results
2.1 Impedance-driven capacitive sensing design

Flexible capacitive pressure sensors can generally be divided into non-ionic and ionic types according to their signal transduction mechanisms (Supplementary Figure S1). Ionic sensors exploit the migration of mobile ions and the formation of electric double layers (EDLs), which yield large capacitance variations under small stimuli but suffer from leakage and strong temperature dependence. Non-ionic sensors, in contrast, rely on electronic polarization within solid dielectrics, providing better environmental stability but limited capacitance change because their output originates solely from minor variations of geometry and permittivity. Bridging the advantages of both mechanisms requires a non-ionic strategy that can internally amplify the polarization response without relying on ion transport.

The sensitivity of a capacitive sensor is given by

S = 1/C0 * dCmeas/dP (1)

where C0 is the initial capacitance and Cmeas the measured capacitance at angular frequency ω. For non-ionic systems, the equivalent impedance model can be expressed as

Z(ω,P) = Rs(P) + 1/(jωCs(ω,P)) (2)

where Rs(P) denotes the pressure-dependent series resistance and Cs(ω,P) the apparent capacitance of the dielectric structure. The measurable capacitance can then be expressed as

Cmeas = Cs(ω,P) = Cgeo(P) + Cpol(P)Φ(ω/ωtr(P)) (3)

Here, Cgeo(P) is the geometric capacitance governed by the electrode contact area and dielectric thickness, Cpol(P) is the dipolar polarization capacitance determined by the density of polarizable domains and interfacial junctions, and Φ(ω/ωtr) is a dispersive factor representing the ability of polarization to follow the alternating field. The transition frequency ωtr(P) is inversely proportional to the product Rs(P)Cs(P) and increases as pressure reduces Rs(P).

Differentiating equation with respect to pressure yields

dCmeas/dP = dCgeo/dP + dCpol/dP * Φ + Cpol * dΦ/dωtr * dωtr/dP (4)

geo. contribution pol. contribution impedance contribution

This expression indicates that the measurable capacitance change originates from three cooperative contributions. The geometric contribution arises from pressure-induced densification that increases the effective contact area and decreases the dielectric spacing, thereby raising Cgeo. The polarization contribution stems from the formation of new interfacial junctions and enhanced dipole alignment, which elevate Cpol. The impedance-release contribution originates from the reduction of Rs(P), which shifts ωtr toward higher frequencies and allows a greater fraction of dipoles to respond to the alternating field, thus amplifying the capacitance.

From Equations above, achieving high sensitivity requires a design that maximizes the pressure-induced capacitance change dCmeas/dP while minimizing the initial capacitance C0. This can be realized by simultaneously strengthening the effects of structural densification and polarization activation, and by promoting efficient impedance release. A porous architecture with compressible dielectric domains can enlarge the variation of Cgeo, a heterogeneous dielectric composition can enhance RCs through increased polarizable interfaces, and a conductive network with moderate resistance can enable a strong pressure dependence of Rs(P). Balancing these three factors yields a structure capable of converting mechanical compression into a pronounced electrical response without relying on ionic transport. The combined influence of these mechanisms establishes the general physical foundation for impedance-driven capacitance amplification and provides the theoretical guidance for designing high-sensitivity non-ionic capacitive sensors.

Guided by the theoretical analysis, we constructed a dielectric-gradient all-fiber capacitive architecture that translates pressure-induced impedance modulation into a measurable capacitance amplification (Figure 1a). At zero load, the highly porous network and limited inter-fiber contact produce a large initial series resistance Rs(0) and a small baseline capacitance C0. When external pressure is applied, the effective contact area (Aed) increases rapidly, the fraction of polarizable volume expands, and the previously constrained dipoles are released, leading to a substantial increase in capacitance (ΔC). This reversible transition from a resistive-dominant to a polarization-active regime constitutes the fundamental working principle of the impedance-driven response, as analytically described in Supplementary Note 1.

In the series-readout representation, the device response can be described by an effective series resistance Rs in series with an effective storage capacitance Cs, such that the capacitance reported by the LCR meter is an impedance-derived apparent capacitance rather than a purely geometric capacitance. Whether this storage component can be expressed within a given AC measurement window is governed by the electrical relaxation time of the series branch, τ ∼ RsCs, which defines a characteristic (transition) frequency

ωtr ≈ 1/τ ≈ 1/(RsCs) (f = ωtr/2π) (5)

A larger Rs leads to a longer τ, thereby constraining polarization-related charge storage at a fixed test frequency, whereas a reduced Rs shortens τ and shifts ωtr to higher frequency, making a larger fraction of the storage response observable. In this context, impedance release refers to the pressure-induced decrease of Rs, which increases ωtr and enables an enhanced apparent capacitive output under the same AC readout conditions; the frequency-domain evidence for this shift is discussed in following text.

The sensor structure comprises alternately stacked conductive and dielectric fiber layers with a precisely tuned carbon nanotube (CNTs) concentration gradient (Figure 1b and Supplementary Figure S2). These layers were fabricated through an airflow-spinning process under identical processing conditions, followed by thermal imidization of poly(amic acid) into polyimide (PI) with excellent temperature resistance (Supplementary Figures S3 and S4), with a structural porosity of up to 90%, estimated using a density-based method (Supplementary Table S1). Notably, the millimeter-scale device thickness (~2 mm; inset of Figure 1b) is not dictated by uncontrolled fiber stacking, but is defined during thermal imidization using a fixed 2 mm mold cavity, ensuring good thickness reproducibility across samples (Supplementary Figure S5). Meanwhile, the device is intended to operate within the thermal endurance window of the PI/CNT system, while direct flame exposure beyond this limit may induce irreversible decomposition/carbonization and unstable signals (Supplementary Figure S6). Cross-sectional SEM observations reveal well-interpenetrated interfaces that maintain mechanical continuity and facilitate stress transfer across layers (Figure 1c and Supplementary Figure S7). Individual fiber cross-sections show that the CNTs are densely distributed within the electrode layer and sparsely, yet uniformly, dispersed within the dielectric layer (Figure 1d and Supplementary Figure S8), forming a conductivity–permittivity gradient that enables continuous tuning of Rs(P) under compression. Importantly, the differences between the electrode and dielectric fibers are caused by the content of CNTs in the spinning dope, whereas the airflow-spinning conditions were kept identical for all layers, helping minimize processing-induced variability and improve diameter uniformity and reproducibility (Supplementary Figure S5). As the structure is compressed, the porous network gradually densifies and new inter-fiber junctions emerge (Figure 1e). Kelvin probe force microscopy reveals a distinct potential contrast at the inter-fiber junctions, where the local surface potential increases from approximately 173 mV on isolated fibers to about 196 mV at overlapping regions (Figure 1f and Supplementary Figure S9). This potential elevation indicates enhanced charge accumulation and local dipole activation at the newly formed contacts, directly confirming the creation of additional dielectric pathways that substantiate the predicted impedance-release mechanism. Consistently, additional multi-site KPFM mappings and conductive-AFM measurements further support junction-enabled conduction within the dielectric network under compression (Supplementary Fig. S9).

The PI matrix endows the all-fiber architecture with high stiffness, excellent thermal stability, and reversible deformation capability, thereby maintaining interfacial integrity during repeated compression. The electrode and dielectric fiber mats exhibit tensile strengths of approximately 115 MPa and 70 MPa and moduli of 6.3 GPa and 4.9 GPa, respectively (Supplementary Figure S10), ensuring efficient load transfer and structural stability throughout the full pressure range. The compressive stress–strain response (Figure 1g) displays pronounced strain hardening, arising from progressive pore collapse and inter-fiber contact densification, which enables continuous impedance modulation without structural failure. Collectively, these results confirm that the hierarchical fiber network provides a robust mechanical foundation for the impedance-driven capacitive sensing concept, transforming the theoretical design framework into a practical and reliable structural embodiment.

2.2 Sensing mechanism and performance

The impedance-driven mechanism proposed above was experimentally verified through frequency-domain analysis, compositional tuning, and structural modeling. The impedance–frequency map (Figure 2a and Supplementary Figure S11) divides the sensor’s behavior into three regions characterized by ωRsCs: the resistive-dominant regime (ωRsCs > 10), the transition regime (0.1 < ωRsCs < 10), and the capacitive-dominant regime (ωRsCs < 0.1). At 0 kPa, the device lies in the transition regime, and increasing pressure enhances electronic conduction in the CNTs network and reduces Rs, progressively relaxing the resistive constraint. Accordingly, the extracted Cs is an impedance-derived apparent capacitance, for which a lower Rs makes polarization-related storage increasingly accessible at the measurement frequency.

The frequency evolution of the impedance imaginary component provides a spectral signature of this shift (Figure 2b). Here, |X| denotes the magnitude of the imaginary impedance. At 0 kPa, |X| exhibits a weak maximum within the measurable window (102–105 Hz), whereas at higher pressures (200–8000 kPa), it shows no discernible peak across the same range. This monotonic increase indicates that the relaxation peak has shifted beyond the measurement band as pressure rises, consistent with a decrease in Rs and a corresponding increase in the characteristic frequency ωtr. Thus, pressure accelerates the polarization dynamics and converts the system from a resistive-dominated state to a polarization-active state, in agreement with the theoretical criterion derived in Supplementary Note 1.

To quantitatively differentiate conduction-dominated artifacts from polarization-related loss, we performed broadband dielectric spectroscopy of the full device under representative compressive strains (0, 50, and 80%). The measured imaginary permittivity ε''(ω) was decomposed into a conduction term (ε''cond(ω)) and a polarization-loss term (ε''pol(ω)) according to

ε''(ω) = ε''pol(ω) + ε''cond(ω), ε''cond(ω) = σ'(ω)/(ε0ω) (6)

where σ'(ω) is the real part of AC conductivity, ε0 is the vacuum dielectric constant (ε0 ≈ 8.854×10−12 F/m). Notably, across the measured frequency range and for all strains, the conduction contribution ε''cond(ω) remains about two orders of magnitude smaller than ε''pol(ω) over most of the measured frequency range, indicating that the imaginary permittivity is dominated by polarization-related dissipation rather than pure conduction leakage. Meanwhile, compression increases σ'(ω) (consistent with enhanced conductive pathways and loss), but ε''pol(ω) remains the main component and evolves systematically with strain, supporting that the capacitance amplification is not solely a leakage-driven artifact (Supplementary Figure S12).

At a fixed readout frequency of 10 kHz, both the series resistance Rs (left axis) and the imaginary component |X| (right axis) decrease markedly with increasing pressure (Figure 2c). Under the series-equivalent readout and within the transition-to-capacitive regime, the concurrent reduction of Rs and |X| is consistent with an increase in the extracted series capacitance Cs at the same measurement frequency. This behavior reflects a pressure-induced shift of the readout window governed by ωCsRs through which polarization-related responses become increasingly accessible in the AC measurement. In this sense, the observed impedance-controlled capacitance amplification represents the experimental manifestation of the pressure-dependent polarization term Cpol(P)Φ(ω/ωtr(P)), as formalized in the impedance-release model in Supplementary Note 1.

Notably, the capacitance enhancement is not uniquely tied to mechanical compression. To decouple impedance modulation from deformation, two zero-pressure control experiments were conducted (Supplementary Figure S13). First, with the electrode CNTs loading fixed at 20 wt.%, increasing the dielectric CNTs content (6–11 wt.%) monotonically reduced Rs and increased the extracted Cs. Second, introducing external series resistors (100 Ω–1 MΩ) systematically suppressed the measured Cs. Together, these results demonstrate that the enhanced impedance-derived apparent capacitance is governed by the total series resistance within the AC readout window, while mechanical compression mainly serves as a convenient and reversible means to reduce Rs in a single device.

The compositional dependence of the two fiber layers defines the effective impedance window of the sensor. When the dielectric layer (9 wt.% CNTs) was paired with electrode layers containing 18–20 wt.% CNTs, the capacitance–pressure (ΔC/C0) curves exhibited a two-region linear response (Figure 2d). The first region corresponds to impedance release at low pressures, while the second originates from cooperative growth of polarization and contact area at higher pressures. Figure 2e shows the enlarged sensitivity map in the low-pressure range, highlighting the corresponding sensitivity differences. Moreover, the sensor response is highly reproducible across three independently fabricated batches (<6% variation in sensitivity; Supplementary Figure S14). Over-doping the dielectric layer (10 wt.% CNTs) led to signal distortion and loss of capacitance response above ~5 MPa (Supplementary Figure S15), whereas replacing the porous electrode with a metallic foil increased C0 to ~8 nF and induced inductive behavior at ~7 MPa. Importantly, because the Cu foil electrode presents a dense and continuous conductive surface, extensive interfacial contact with the dielectric layer is already established in the initial state, resulting in a high baseline capacitance (C0). This significantly reduces the relative capacitance change (ΔC/C0) and limits the achievable sensitivity. In contrast, the porous fiber electrode exhibits restricted initial contact and higher interfacial impedance, allowing polarization to be gradually activated under applied pressure. This enables a wider dynamic modulation range of the series impedance and leads to a more effective and stable capacitance readout (Supplementary Figure S16). These results confirm that the impedance-tunable, porous architecture is essential for maintaining capacitive operation across a wide pressure range. Pressure-dependent resistances of the electrode Re(P) and dielectric Rd(P) layers reveal the same trend, both decreasing by several orders of magnitude as the structure compresses (Figure 2f). This evolution provides direct experimental evidence for Equation (S2) in Supplementary Note 1, where pressure-induced enlargement of contact area Acontact(P) and reduction in resistive path length jointly lower Rs(P) = Re + Rd.

Finite-element analysis (FEA) provides further insight into the cooperative contributions of the electrode and dielectric layers (Figures 2g and h; Supplementary Figures S17 and S18). When deformation occurs only in the dielectric layer (model D) or only in the electrode layer (model E), the simulated capacitance–pressure response C(P) increases only slightly. In contrast, the coupled deformation model (E–D) accurately reproduces the experimentally observed two-slope behavior. This result demonstrates that geometric densification and impedance modulation operate in concert, where mechanical compression simultaneously lowers Rs and enhances Cs, ultimately yielding the pronounced capacitance amplification observed in the impedance-driven sensor.

The impedance-driven all-fiber sensor demonstrates a combination of ultrahigh sensitivity and remarkable mechanical resilience. In the sensitivity–pressure diagram (Figure 2i and Supplementary Table S2), the sensor occupies the high-sensitivity and wide-pressure-range quadrant, surpassing conventional non-ionic capacitive sensors and approaching the response levels of ionic counterparts4,7,16,19,26,29,31,40-43. It delivers a maximum sensitivity of 169.8 kPa-1 within a broad range up to 8 MPa, together with an ultralow detection limit of approximately 20 Pa (Supplementary Figure S19). In addition, representative loading–unloading curves were measured to quantify hysteresis. The porous fiber network exhibits a stable and reproducible hysteresis loop with closely overlapped responses over successive cycles Within the sensing range of 8MPa (Supplementary Figure S20). The sensor also exhibits rapid polarization–depolarization kinetics, achieving response and recovery times of 5.8 ms and 6.1 ms, respectively (Supplementary Figure S21). To complement the sub-10 ms response/recovery results, we further evaluated loading-rate dependence (10–100 mm min⁻¹) and dwell-time stability at 2000 kPa for 1 h. The sensor maintained consistent robust signal tracking with minimal drift over the holding period (Supplementary Fig. S22). Meanwhile, under repetitive loading at 2 MPa, the device maintains a stable capacitance output for more than 20 000 cycles without observable baseline drift (Figure 2j), confirming excellent structural reversibility and long-term operational reliability.

2.3 Temperature-independent sensing performance

Building upon the impedance-driven design and structural validation described above, the environmental stability of the all-fiber sensor was further evaluated under extreme temperature conditions. For high-performance tactile sensors, temperature represents a critical factor that often limits practical deployment. In ionic and hybrid iontronic systems, the electrical activity of mobile ions is inherently temperature-dependent, leading to pronounced signal fluctuations over broad temperature ranges. At subzero temperatures, ionic motion is severely restricted, resulting in functional degradation, whereas at elevated temperatures, excessive ion mobility causes anomalous signal amplification and drift (Supplementary Figure S23). Although conventional non-ionic capacitive sensors exhibit better thermal stability, their low dielectric tunability results in weak sensitivity and limited resolution, hindering high-precision sensing applications under variable thermal environments.

In contrast, the impedance-driven all-fiber sensor demonstrates remarkable signal uniformity and stability across an ultrawide temperature window from −80 °C to 200 °C, enabled by the thermally robust PI matrix, the stable CNTs network, and the low-impedance sensing mechanism. Even under extreme conditions, the device maintains high sensitivities of 165.2 kPa-1 (R2 = 0.9998) at −80 °C and 179.7 kPa⁻¹ (R2 = 0.9997) at 200 °C, with less than 6% deviation, while preserving a broad operating range up to 8 MPa (Figure 3a). Notably, the temperature-invariant normalized response (ΔC/C0) cannot be attributed to the thermally robust PI matrix alone; it also benefits from the low-impedance sensing mechanism. This outstanding temperature-independent performance arises from the coupled mechanical–electrical stability of the fibrous network, where the PI framework maintains structural integrity while the impedance-regulated readout stabilizes the electrical response. To clarify this point, we compared three sensors with the same PI-based architecture, including the impedance-driven PI/CNT device, a conventional capacitive PI device, and an iontronic PI/IL device (Supplementary Figure S24), which show markedly different capacitance responses at elevated temperature, confirming that the robustness is not solely a matrix effect. As shown in Figure 3b, the compressive stress–strain responses measured at −80 °C, 25 °C, and 200 °C exhibit nearly identical modulus values (20 kPa–32 MPa), confirming that the load transfer and deformation behavior remain consistent across the full temperature range.

The electrical characteristics of the CNTs network also contribute to this robustness. The conductivity of CNTs increases nearly proportionally with temperature, which simultaneously enhances both the baseline capacitance C0 and the saturation capacitance, thereby preserving the relative variation ΔC/C0 (Supplementary Figure S25 and Table S3). To further verify robustness against extreme temperature transitions, we performed repeated thermal cycling among −80 °C, 25 °C, and 200 °C, together with multi-level loading–unloading tests from 100 to 2000 kPa. As shown in Figure 3c and Supplementary Figure S26, ΔC/C0, Rs, and tanδ remain highly repeatable throughout successive temperature cycles. Together with the long-term loading and hysteresis characterization, these results confirm stable and reliable sensing under repeated thermal transitions. Long-term durability tests under 2 MPa loading further confirm that the capacitance waveform remains nearly unchanged after 500 cycles at −80 °C and 200 °C (Figures 3d and e), reflecting excellent structural reversibility and environmental endurance.

A quantitative comparison of key performance metrics (Figure 3f and Supplementary Table S4) highlights the distinct advantages of the impedance-driven architecture4,14-16,42,43. The all-fiber sensor achieves a balanced combination of high sensitivity, broad sensing range, and long-term reliability, while maintaining temperature-independent operation across an ultrawide thermal window. This convergence of electrical, mechanical, and environmental robustness resolves the long-standing trade-off that has limited non-ionic capacitive sensors, positioning the device performance on par with or beyond ionic systems. The results confirm that the impedance-driven amplification mechanism not only enhances sensitivity through coordinated modulation of Cs and Rs but also inherently suppresses thermally induced drift, enabling stable signal output under extreme conditions. Such coupling between mechanism-level control and materials-level stability provides a generalizable route toward reliable tactile sensing in harsh environments and paves the way for system-level integration under real-world thermal variations.

2.4 Application demonstration of wide-temperature tactile-sensing gloves

Following the validation of temperature-independent sensing characteristics, the practical applicability of the impedance-driven all-fiber sensor was further verified through a tactile-assistive glove that functions reliably across extreme thermal environments. The concept of this wide-temperature tactile-sensing application is illustrated in Figure 4a, which depicts representative scenarios including high-temperature manufacturing, firefighting, cryogenic operations, and polar or deep-sea exploration. In these real industrial and scientific settings, workers must rely on heavy protective gear for thermal insulation. Although such gloves provide effective temperature protection, their multilayered structure substantially diminishes tactile sensitivity, making precise manipulation more difficult and increasing the likelihood of delayed responses or accidental injuries44-46.

To address these challenges, a wide-temperature tactile-sensing glove was constructed by integrating fourteen all-fiber capacitive pressure sensors into a commercial heat-resistant glove (Figure 4b). The sensors were distributed over the fingertip regions, and the Polytetrafluoroethylene (PTFE) conductive wires were woven between inner layers to maintain flexibility and ensure user comfort. This design allows the glove to provide real-time signal response on grasping pressure and contact conditions while preserving natural motion under both high and low temperatures.

The glove was evaluated under two representative thermal extremes. As shown in Figure 4c, at approximately 200 °C, a wearer grasped and released a heated glass beaker inside a thermal chamber. The glove produced stable, reversible ΔC/C0 responses accompanied by synchronized pressure-distribution maps that clearly reflected the sequence of initial contact, grasping, and release. Under cryogenic conditions near −50 °C, the same glove exhibited equally stable responses while grasping a beaker containing liquid nitrogen (Figure 4d). In both tests, the signal waveforms and thermal–pressure mappings remained consistent, confirming that the tactile-assistive glove delivers reliable and stable tactile signal responses, supporting consistent tactile perception in thermally insulated environments.

Beyond temperature adaptability, the glove was further tested under complex contact modes simulating realistic grasping scenarios. During object manipulation, irregular geometries such as corners, edges, or flat surfaces produce localized stress concentrations that challenge the durability and dynamic range of flexible sensors. Based on an average adult grasping force of ~30 N applied over a 49 mm2 area, the estimated maximum stresses reach approximately 600 kPa for area contact (7 mm × 7 mm), 2.2 MPa for line contact (2 mm × 7 mm), and 7.5 MPa for point contact (2 mm × 2 mm) (Figure 4e). These values exceed the operational limits of most reported flexible sensors but remain well within the 20 Pa–8 MPa range of our impedance-driven architecture. Figure 4f displays the capacitance responses from the thumb sensor while grasping an irregular stone, where distinct and repeatable ΔC/C0 patterns were recorded during sequential transitions from point to line to area contact. This capability confirms that the all-fiber sensors accurately perceive stress distribution and contact modality even under high localized pressure.

These demonstrations confirm that the impedance-driven all-fiber sensors effectively bridge the gap between material design and functional implementation. By maintaining stable and high-fidelity signal outputs under wide-ranging thermal and mechanical conditions, the tactile glove validates the scalability and robustness of the proposed sensing strategy. This system-level demonstration illustrates how impedance modulation can be harnessed to achieve reliable and adaptive tactile perception, establishing a foundation for future intelligent protective systems and robotic interfaces capable of operating in extreme environments.

2.5 Tactile perception and recognition performance

Considering that thick protective clothing in extreme environments can restrict vision and hinder precise control, a multi-object recognition system was developed by combining high-resolution pressure mapping with a two-dimensional convolutional neural network (2D-CNN) (Figure 5a). The system consists of signal acquisition, data preprocessing, and deep-learning recognition modules, operating in conjunction with the tactile-assistive glove to provide real-time signal response on grasping forces and object identification across a wide temperature range. Here, machine learning is used to analyze the pressure-mapping signals captured by our sensor and evaluate the separability of different object patterns47,48. Supplementary Figure S27 presents a simplified workflow of the learning process. Briefly, the raw capacitance signals were converted to normalized relative responses (ΔC/C0), and statistical features (mean and variance) were extracted and reorganized according to the spatial layout of the sensing channels to form 2D input maps. The resulting feature maps were interpolated to a unified 52×52 grid and fed into a 2D-CNN consisting of two convolution–pooling blocks.

To evaluate the comprehensive sensing performance of the impedance-driven all-fiber sensors, two additional reference sensors were prepared for comparison. An iontronic sensor incorporating 50 wt.% ionic liquid in the dielectric layer represented the ionic type, and a pure PI-fiber sensor represented the traditional capacitive type. In a simulated environment containing four representative object shapes including sphere, cube, cylinder, and triangular prism, tactile-response maps were collected at −70 °C, 25 °C, and 200 °C (Figure 5b). In total, 300 pressure-distribution maps were extracted from 30 repeated grasping trials (10 trials at each temperature) during the stable holding stage. To assess trial-to-trial consistency, we take sphere grasping as a representative case and compute the Pearson correlation coefficient (PCC) between the thumb-tip capacitance signal and a grasping-load proxy estimated from the multi-channel sensor array outputs during the holding stage49. The trial-to-trial variation of the estimated grasping force was within 60 kPa for all three sensor types, and the impedance-driven sensor exhibited a PCC of ~0.8, indicating strong and reproducible force–signal coupling (Supplementary Figure S28). The datasets for the iontronic and traditional sensors are provided in Supplementary Figure S29. Unless otherwise noted, all pressure-distribution maps are displayed using a unified legend referenced to the high-temperature signal range for cross-temperature comparison.

Meanwhile, to improve evaluation robustness, we adopted K-fold cross-validation by dividing the augmented dataset into 6 subsets, using one subset as the non-overlapping test set and the remaining subsets as the training set. Data augmentation was applied to the training folds only to avoid data leakage. This procedure was repeated across 5 folds and the averaged accuracy was reported (Figure 5c-e). The iontronic sensor exhibited strong temperature dependence, performing well at 200 °C but losing functionality at −70 °C because of frozen ionic mobility. Its recognition accuracy was limited to 60.25%. The traditional capacitive sensor maintained consistent output across temperatures owing to its intrinsic thermal stability but produced weak signal intensity and low contrast between objects, resulting in an accuracy of 85.5%. In contrast, the impedance-driven all-fiber sensor maintained consistent relative sensitivity across the entire temperature range. The pressure-mapping patterns of identical objects remained almost unchanged from −70 °C to 200 °C, as indicated by the consistent color scale in Figure 5b. This stability enabled the CNN model to reach a recognition accuracy of 99.25%, representing a substantial improvement over the other two systems.

The high recognition accuracy originates from the stable and high-fidelity pressure signal responses provided by the impedance-driven all-fiber sensors. This stability allows the CNN model to reliably capture subtle differences in pressure distribution caused by variations in object shape, even under extreme temperature conditions. The consistent sensing behavior and strong signal contrast demonstrate that the impedance-driven architecture ensures dependable data quality for tactile perception, confirming the robustness of the sensing mechanism and its suitability for complex, temperature-variable environments.

Methods
Preparation of Spinning Precursor Solution and Fabrication of Fiber Layers: First, 0.1 g of polyethylene oxide (PEO) was dissolved in 10 g of N, N-dimethylformamide (DMF) under magnetic stirring at room temperature for 1 h to obtain a spinnability-enhancing assistant solution. The incorporation of PEO was intended to improve the fiber-forming ability and homogeneity of the dielectric fibers. Subsequently, precursor spinning solutions for the electrode and dielectric layers were prepared separately: 2 g of the assistant solution was added into 2 g of polyamide acid (PAA) solution, followed by the addition of 0.9 g of CNTs dispersion (H-CNT) and 0.405 g of CNTs dispersion (L-CNT), respectively, to obtain the electrode-layer and dielectric-layer precursor solutions.

The porous fiber sponges were fabricated via a sequential airflow-spinning process. Unless otherwise specified, all three layers were deposited under the same spinning window, and the CNTs loading was the only intentional variable between the electrode and dielectric layers. Specifically, the precursor solution was delivered from a 1 mL syringe through a 30G nozzle (The inner diameter is about 160 μm) at 2 mL h⁻¹, while compressed air at 69 kPa (air velocity ~21 m/s at the nozzle exit) was heated to 80 °C and applied coaxially; the collection distance was 30 cm and each layer was collected for 15 min. After sequential deposition of the bottom electrode, dielectric, and top electrode layers, the stack was thermally imidized and hot-pressed in a fixed 2 mm mold cavity, which defines the final device thickness and improves layer-to-layer uniformity.

The collected fiber sponges were subsequently dried in an oven at 100 ℃ for 1 h to remove residual solvents. Finally, the samples were placed in a fixed-thickness mold and subjected to a stepwise thermal imidization process in a muffle furnace at 100 ℃ for 1 h, 200 ℃ for 1 h, and 300 ℃ for 1 h. This stepwise spinning and imidization strategy enabled the fabrication of a well-integrated three-layer porous fiber architecture (electrode–dielectric–electrode), serving as the basis of the full fiber flexible pressure sensor. The size of the sensor can be customized using a cutter.

Finite Element Analysis: Finite-element simulations were performed using COMSOL Multiphysics. The highly porous (>90%) PI/CNT fibrous architecture was represented in a homogenized manner: instead of explicitly constructing macroscopic pores in the geometry, the porosity of each layer was incorporated through COMSOL’s porous-media representation, in which porosity is treated as an internal model parameter. Mechanical parameters for the electrode and dielectric layers were assigned based on the experimentally measured compressive stress–strain responses of the corresponding layers. Electrical material parameters of the three-layer structure were defined consistently with the experimentally measured layer-wise electrical characteristics and updated according to the measured pressure-dependent resistance evolution of each layer.

The deformation of the layered structure under applied pressure was solved using the Solid Mechanics module. A uniform pressure load was applied on the top surface, while the bottom surface was constrained (Uz = 0). Contact interfaces between layers were assumed to be ideal (frictionless and non-penetrative) to simplify interfacial coupling in the integrated model. The computed deformed configurations were then used as input for the electrical simulations.

The complex impedance response was computed using the AC/DC module under small-signal AC excitation. For electrical boundary conditions, the bottom surface of the model was grounded, while a voltage boundary was applied to the top surface. The overall impedance of the three-layer architecture was extracted, with both real and imaginary components calculated as a function of applied pressure. These results were used to support a trend-level analysis of the impedance-driven capacitive enhancement behavior in the proposed sensor structure.

Mechanical characterization: The mechanical performance of the sensor was systematically characterized using a mechanical testing system (XLD-1000E, Guangzhou Jingkong Testing Co., Ltd.). For the tensile strength measurements, electrode and dielectric layer samples with dimensions of 5 mm × 30 mm were subjected to uniaxial tensile testing at a speed of 50 mm·min⁻¹ until fracture. The corresponding stress–strain curves were recorded to evaluate the tensile strength and Young’s modulus. For the compression tests, samples with dimensions of 7 mm × 7 mm were placed on the compression stage in contact with a flat surface indenter. The indenter was programmed to compress the samples at a constant speed of 5 mm·min⁻¹ until the target strain was reached. This allowed the evaluation of compressive stress and modulus as a core parameter.

Sensitivity and Sensing Performance Measurements: The sensitivity and sensing characteristics of the sensor were characterized using a mechanical testing system (XLD-1000E, Guangzhou Jingkong Testing Co., Ltd.). The sensor samples with dimensions of 7 mm × 7 mm were placed on the testing platform, and a flat indenter was programmed to approach the sample at a controlled rate to apply external forces. During the loading process, the capacitance signals were continuously recorded in real time using an LCR meter (TH2840B, Tonghui Electronics Co., Ltd.) at a fixed test frequency of 10 kHz. For determining the LOD, the sensor was subjected to a gradually increasing programmed gradient force. The minimum force at which a detectable capacitance peak first appeared was defined as the LOD. For response and recovery time measurements, a weight was rapidly placed onto and released from the sensor surface, producing an instantaneous pressure. After maintaining the load for 2 s, the weight was removed. The transient capacitance response and recovery curves were recorded to evaluate the dynamic sensing performance.

Cyclic Durability Testing: To evaluate the stability of the sensor under extreme temperature and high-pressure cyclic conditions, a mechanical testing system equipped with a temperature-controlled environmental chamber and a computer-controlled platform (EUT4000, Shenzhen Sansi Testing Co., Ltd.) was employed. Sensor samples with dimensions of 7 mm × 7 mm were mounted on the testing stage. During the measurements, the target temperature was set and maintained via the environmental chamber. A compressive load was then applied at a controlled rate of 2 mm·min⁻¹ until the sensor was subjected to a pressure of 2000 kPa. Under this condition, high-cycle reciprocating displacement tests were performed to simulate long-term operational stresses. Throughout the cyclic loading process, the capacitance signals of the sensor were continuously recorded in real time using an LCR meter, enabling the evaluation of its signal stability and durability across different temperature environments and under high-pressure cyclic stresses.

Porosity of the sensor: Porosity was estimated using a density-based method. The apparent density of the porous sample (ρapp) was calculated from the measured mass m and geometric volume V. The theoretical density of the solid matrix (ρ0) was taken as 1.47 g/cm3. The porosity P was then calculated as:

P = (1 - ρapp/ρ0) × 100% (7)

Measurements were performed on at least three independent samples and averaged.

Integration and signal acquisition of sensing gloves: Firstly, use a precision cutting tool to cut the flexible pressure sensor into a standard sample of 7 mm × 7 mm. Then use conductive silver paste to bond PTFE high-temperature resistant wires to the top and bottom electrodes of each sample, ensuring reliable electrical connection and thermal stability. In order to integrate with gloves, the sensor is fixed in the designated position using PI double-sided tape, and PI tape is used as a protective surface layer to prevent mechanical wear or environmental damage. The PTFE wire is woven and placed inside the glove to minimize interference with user comfort and operational flexibility. During the testing process, a multi-channel LCR meter (TH2840NX, Tonghui Electronics Co., Ltd.) was used to collect sensing signals in real time at a working frequency of 10kHz, thereby achieving simultaneous monitoring and accurate data acquisition of multiple sensors.

Characterization: A comprehensive set of characterization techniques was employed to investigate the structural and functional properties of the fabricated flexible pressure sensors. Field-emission scanning electron microscopy (FE-SEM, Zeiss Sigma 300, Germany) was used to observe the microstructure of the fiber networks and interfacial morphologies, highlighting fiber junctions and porosity evolution. Kelvin probe force microscopy (KPFM, Bruker Dimension ICON, USA) was conducted to map the surface potential distribution at fiber junctions, thereby revealing localized charge accumulation and the formation of new dielectric pathways. Additionally, transmission electron microscopy (TEM, FEI Tecnai F20, USA) was used to examine the nanoscale morphology of single fibers and the dispersion of CNTs within the polymer matrix. For chemical analysis, Fourier-transform infrared spectroscopy (FTIR, Nicolet iS 10, Thermo Fisher Scientific, USA) was applied to compare the spectra of PAA and PI, confirming the imidization process and associated changes in functional groups. To evaluate thermal stability, thermogravimetric analysis (TGA, Netzsch STA 449 F3, Germany) was employed to monitor the weight loss behavior under controlled heating, providing insights into decomposition temperatures and thermal endurance.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this paper, rather than relying on conventional geometric capacitance enhancement, the authors present a new sensing framework in which pressure-driven impedance release activates previously suppressed polarization channels, converting series resistance from a limiting factor into a controllable gain parameter, and validate this concept through temperature-invariant polyimide sensor operation and wide-temperature CNN-assisted tactile recognition. While previous studies have typically enhanced sensitivity through geometric or dielectric modulation at the expense of dynamic response, environmental stability, or operating range due to intrinsic impedance constraints, this work uniquely overcomes such limitations by formalizing an impedance-driven amplification mechanism that activates previously suppressed polarization channels, and rigorously validates the resulting simultaneous enhancement of electrical and operational performance through impedance spectroscopy, systematic pressure-dependent measurements, and theoretical analysis. The integration of well-established principles from dielectric spectroscopy, impedance analysis, and relaxation dynamics into a coherent sensing framework that redefines impedance—traditionally treated as a parasitic loss to be minimized—as an active gain knob for releasing suppressed polarization channels is quite novel.\nTherefore, this paper could be further considered for publication in Nature Communications, as long as the following comments can be well addressed:\n1. It would be interesting to analyze whether the observed capacitance enhancement is uniquely tied to mechanical compression or whether comparable effects can be obtained by selectively tuning the series resistance without mechanical pressure.\n2. In Figures 3b and 3c, the outstanding temperature-independent performances are quite interesting. However, the statement that “… enabled by the thermally robust polyimide matrix …” in line 22 (page 12) is insufficient on its own. It would be informative to clarify whether the observed temperature robustness arises from the impedance-driven sensing mechanism itself.\n3. In line 19 (page 2), the authors state that “Ionic materials are prone to leakage, evaporation, and thermal degradation.” This seems overgeneralized, as ion-confined polymer matrix systems have shown low leakage and long-term stability.\n4. Given that Figure 1b (inset) suggests a millimeter-scale thickness, additional clarification of the air-jet spinning process would improve the clarity and reproducibility of the fabrication method.\n5. The Introduction currently lacks sufficient citations to recent wearable/implantable tissue-like studies that are essential for establishing tissue-device conformality. I am therefore suggesting several relevant papers and would like you to incorporate them accordingly. (Nature Nanotechnology (2025) DOI https://doi.org/10.1038/s41565-025-02031-x ; Nature Electronics 8, 474-484 (2025) ; Nature Electronics 7, 800 (2024) ; Nature Nanotechnology 17, 849 (2022))\n6. At present, several key concepts, terms, and symbols (e.g., transition frequency, impedance release, the physical meaning of |X| evolution, and the relationship between series resistance and polarization activation) are difficult to understand from the main text alone. A reader must consult Supplementary Note 1 to follow the core mechanism, which reduces the readability of the paper. The authors should consider expanding the main text to include concise but clear explanations of these concepts.\n7.Figure 2d appears to exhibit two clearly different sensing regimes at low and high pressure ranges, corresponding to different slopes. However, these results are not clearly visible in the current figure. The authors should consider annotating the extracted slopes for these two regions and adding an inset that enlarges the low-pressure area.\n8. In Figure S6, the cross-sectional SEM images suggest noticeable differences in fiber thickness between the electrode layers and the dielectric layer. It is not clear whether this difference originates from variations in electrospinning conditions (e.g., solution properties or CNT loading), or whether it is an intentional design choice. In addition, the overall thickness of each layer and of the full device is not clearly specified. According to the Methods section, each layer appears to be fabricated using a 1 mL syringe over approximately 15 minutes. Based on the device photographs, the assembled sensor appears relatively thick, which raises questions about layer-to-layer thickness uniformity. Could authors report the average fiber diameter and thickness distribution for each layer, the thickness of each individual layer, and the total thickness of the assembled device.\n9. In Figure 2d, devices using metallic foil electrodes show significantly inferior sensing performance compared to those with porous fiber electrodes. However, the manuscript does not clearly explain why metallic electrodes fail to provide effective sensing in this system. A more explicit explanation is needed, particularly addressing why high electrical conductivity alone is insufficient. Clarifying this point would strengthen the design rationale for porous electrodes.\n10. Figures S1b and S1c suggest that ionic capacitive pressure sensors typically exhibit nF level signals, whereas the proposed impedance-based capacitive sensors operate at the μF level. However, Figure S13 shows that under an 80 kPa load, the measured capacitance change is on the order of ~25 nF. This discrepancy may give the impression that the signal magnitude is overstated. The authors should clarify the operating conditions under which μF level signals are achieved.\n11. The fire-scenario demonstration effectively shows that the sensor can operate stably at elevated temperatures. However, a real fire scenario typically involves conditions beyond uniform high temperature, such as direct flame exposure, oxidation, and transient thermal gradients. The authors have any additional analysis, characterization, or testing under more extreme fire-related conditions?", "reviewer_2": "The manuscript reports an impedance-driven PI–CNT all-fiber capacitive sensor with high sensitivity, MPa-level pressure operation, and wide temperature tolerance; similar research has been reported [1–4]. These results are promising, but several scientific and methodological issues must be addressed before the work can be considered for publication in a high-impact venue.\nThe mechanistic validation of the proposed “impedance-release” concept remains largely qualitative. The contributions of geometric effects, dielectric response, and impedance evolution are not quantitatively separated, and alternative explanations—such as compression-induced CNT tunneling or leakage—are not rigorously excluded.[1–3] Frequency-dependent behavior, noise/drift characteristics, and device-to-device reproducibility are also insufficiently explored. In addition, the repeated reference to “real-time feedback” is not supported, as the system demonstrates sensing only.[9,10]\nThe finite-element analysis (FEA) relies on constitutive assumptions inconsistent with the highly porous (>90%) fiber architecture, limiting its mechanistic reliability.[2–4] Moreover, claims of universality are not substantiated because all demonstrations are restricted to a single PI–CNT platform without validation in chemically distinct systems.[2,3,5]\nFinally, the evaluation of pressure mapping and the deep-learning demonstration lacks statistical rigor, controlled comparisons, and methodological transparency.[11]\nOverall, the study presents a compelling device concept but currently reads as a high-performance demonstration of a single optimized system rather than a fully validated, generalizable design framework. Addressing the mechanistic, modeling, reproducibility, and benchmarking issues will substantially strengthen the manuscript.\n------\n[Additional Major Comments]\n1. Mechanism not quantitatively validated; alternative explanations not excluded\nSection 2.2 and Supplementary Note 1 ascribe the large observed ΔC to an “impedance‑release” mechanism, in which pressure‑induced reductions in Rs and shifts in the polarization transition frequency are proposed to dominate the response. However, the data in Fig. 2a–f and Supplementary Fig. S9 do not quantitatively disentangle the respective contributions of geometric effects, intrinsic dielectric properties, and impedance evolution.[1–4]In addition, CNT‑based composites are well known to exhibit compression‑induced tunneling and the formation of leakage pathways, both of which can manifest as an apparent increase in capacitance in porous structures.[1–3] In the present manuscript, these alternative pathways have not yet been rigorously differentiated from the proposed mechanism. A more systematic analysis is required to clarify whether the pressure dependence of Rs primarily reflects activated polarization, or whether it is instead dominated by changes in conductive pathways and leakage currents. In iontronic systems, capacitances of this magnitude are typically associated with electric‑double‑layer formation at soft interfaces,[4–8] whereas in the present non‑ionic architecture the existence of such interfacial mechanisms is not clearly established. Additional controls and quantitative checks are therefore needed to demonstrate that the observed behavior cannot be fully explained by leakage or tunneling effects.[4–8]\n2. FEA does not adequately represent the highly porous architecture\nIn Fig. 2g,h and Supplementary Fig. S11, the FEA treats the electrode and dielectric layers as incompressible Neo‑Hookean solids with GPa‑level Young’s moduli taken from tensile tests. However, Fig. 1g and the Supplementary tables/figures (porosity > 90%) indicate that the actual structure is a PI–CNT fiber sponge exhibiting foam‑like compressive densification. Incompressible Neo‑Hookean models are generally appropriate for dense elastomeric solids, whereas open‑cell foams and fiber networks are typically described by compressible hyperelastic or foam‑type constitutive laws that explicitly capture volumetric strain and pore collapse.[2–4] Under these assumptions, the D/E/ED simulations can only be interpreted as qualitative approximations; the fact that the ED case reproduces a two‑slope C–P curve is not, by itself, a physically faithful validation of the mechanistic behavior of the porous PI–CNT architecture. At a minimum, the main text should explicitly acknowledge these limitations, and ideally the simulations should be updated using a compressible/foam‑type model calibrated to the measured compressive stress–strain response.[2–4]\n3. Generality claims are not supported by evidence beyond a single PI–CNT system\nThe manuscript presents the proposed approach as broadly applicable to heterogeneous non‑ionic composites, yet all experimental demonstrations are confined to a single PI–CNT all‑fiber system.[1–4] Given the extensive literature on diverse polymer–filler combinations (e.g. PDMS/CNT or PDMS/graphene, MXene/polymer composites, PU/PI foams, carbon‑black or metal‑nanowire elastomers), it would be important to verify whether similar impedance‑driven trends can be reproduced in at least one chemically distinct platform in order to substantiate the notion of a “general design rule”. As currently presented, the evidence more strongly supports a system-specific optimization of PI–CNT fiber networks than with a broadly validated universal strategy.[2,3,5]\n4. Claim of providing “real-time feedback” is not supported.\nThe manuscript repeatedly uses the term “real-time feedback,” yet the presented system demonstrates only sensing and perception via pressure mapping and CNN-based recognition, without any integrated haptic actuator or closed-loop control scheme. As currently implemented, the work constitutes a sensing-only proof-of-concept, and the terminology should be adjusted to avoid implying a closed-loop or haptic feedback–enabled system that is not implemented here.[9, 10]\n5. Insufficient statistical reporting of pressure mapping patterns.\nQuantitative statistical metrics should be provided to supplement Fig. 5(b), in order to demonstrate that the pressure-mapping patterns of identical objects are consistent across repeated trials. In addition, a 2D spatio-frequency analysis would allow direct comparison of the spatial–frequency characteristics among different objects. For Fig. S16, the applied pressure should be controlled and kept consistent across all comparative groups; however, the current data suggest noticeable variation. Furthermore, the reference sensors used for benchmarking differ in geometry and electrode layout, which undermines the validity of the comparisons. These factors collectively weaken the conclusions regarding recognition accuracy. For instance, both the Traditional and Impedance types exhibit poorer spatial resolution at 25 °C yet appear to perform better at 200 °C—an outcome that contradicts physical intuition and calls for additional verification. Incorporating a ground-truth reference measurement across all three sensing modalities would substantially strengthen the reliability of the comparison.\n6. Deep learning demonstration lacks rigor and controlled comparison.\nThe dataset size is limited (300 samples), no cross-validation or train/test robustness analysis is performed, and key details—such as signal preprocessing, data normalization, construction of the 52×52 input arrays, or justification for using a 1D rather than 3D CNN architecture—are missing. The t-SNE plots indicate linearly separable clusters even for the Traditional type, yet the classification accuracy for that case drops to ~80% without sufficient explanation. Overall, the DL demonstration would benefit from clearer methodological transparency, controlled comparison, and more rigorous evaluation.[11]\n[Additional minor comments]\n1) Recent high-performance capacitive and iontronic sensors already achieve wide pressure ranges and high sensitivities through graded, porous, or impedance-engineered architectures.[4,6–8] While the wide temperature window and impedance-related mechanism explored here provide meaningful novelty, a more balanced and up-to-date discussion of these recent advances would help contextualize the contribution and avoid overstating the level of performance innovation.\n2)Supplementary Fig. S13 reports sub-10 ms response and recovery times; however, there is no systematic investigation of loading-rate dependence, dwell-time effects, or rate-dependent hysteresis. These characteristics are particularly important for impedance-driven mechanisms and are routinely quantified in recent iontronic sensor literature.[4,6–8]\n3) Although Fig. 3 and Supplementary Fig. S15 demonstrate functional operation from −80 to 200 °C, the study does not include repeated thermal cycling or long-term testing across extreme transitions. Temperature-dependent analyses of Rs(T,ω), tan δ(T,ω), Arrhenius-type activation behavior, and high-temperature durability would significantly strengthen claims of thermal robustness, as these analyses are standard in high-temperature iontronic sensor studies.[6–8]\n4) SEM and KPFM results offer useful qualitative insights, but additional microstructure-resolved evidence—such as in-situ micro-CT, conductive-AFM, or local impedance mapping—would more convincingly validate the proposed percolation and impedance-release mechanisms.[4,6–8] Moreover, only a single KPFM region is shown; statistical distributions, multiple measurement sites, or correlations between surface potential and polarization would improve the robustness of the interpretation.\n5) Porous dielectric structures generally exhibit compression–release hysteresis, but aside from long-cycle stability, no loading–unloading curves are presented. Quantifying hysteresis is essential for evaluating practical usability and mechanism consistency.\n6)The reported porosity (~90%) lacks methodological detail. It is unclear whether it was determined via gravimetry, image-based analysis, mercury porosimetry, nitrogen adsorption, or another technique. Explicitly stating the measurement method is necessary for reproducibility.\n7) Performance data appear to be based on single devices without error bars or batch-to-batch statistics. Given the stochastic nature of fiber spinning, CNT distribution, and gradient formation, reproducibility is essential to assess manufacturing robustness. Additionally, several parameters critical to reproducibility—such as airflow nozzle size, air temperature, humidity, and fiber diameter distribution—are not reported.\nReferences\n[1] Kang, Z. et al. “Highly Sensitive Flexible Pressure Sensors Enabled by Mixing of Silicone Elastomer with Ionic Liquid-Grafted Silicone Oil.” Frontiers in Robotics and AI 2021\n[2] Farman, M. et al. “All-polydimethylsiloxane-based highly flexible and stable capacitive pressure sensors with engineered interfaces for conformable electronic skin.” ACS. Applied Material and Interfaces. 2023\n[3] Qin, T., et al. “A New Strategy for the Fabrication of a Flexible and Highly Sensitive Capacitive Pressure Sensor.” Microsystems & Nanoengineering 2021\n[4] Bai, N. et al. “Graded Intrafillable Architecture-Based Iontronic Pressure Sensor with Ultra-Broad-Range High Sensitivity.” Nature Communications 2020\n[5] Wang, H. et al. “Flexible Capacitive Pressure Sensors for Wearable Electronics.” Journal of Materials Chemistry C 2022,\n[6] Tian, H. et al. “Hierarchical Synergetic Strategy for Iontronic Pressure Sensors with High Sensitivity and Wide Dynamic Range.” ACS Sensors 2025\n[7] Huang, Y., et al. “Programmable High-Sensitivity Iontronic Pressure Sensors Support Wide-Range Tactile Sensing.” npj Flexible Electronics 2025\n[8] He, Z., et al. “Hofmeister Effect Regulated Gel Iontronic Sensors for Wide-Range Pressure Detection.” The Innovation: Materials 2024\n[9] Y. Luo et al., “Adaptive tactile interaction transfer via digitally embroidered smart gloves”, Nature Communications, 2024\n[10] K. Ha et al., “Full freedom-of-motion actuators as advanced haptic interfaces”, Science, 2025\n[11] S. Ji et al., “3D Convolutional Neural Networks for Human Action Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023"}, "author_response": "Response to the Reviewers\nReviewer #1:\nComment: In this paper, rather than relying on conventional geometric capacitance enhancement,\nthe authors present a new sensing framework in which pressure-driven impedance release activates\npreviously suppressed polarization channels, converting series resistance from a limiting factor into\na controllable gain parameter, and validate this concept through temperature-invariant polyimide\nsensor operation and wide-temperature CNN-assisted tactile recognition. While previous studies\nhave typically enhanced sensitivity through geometric or dielectric modulation at the expense of\ndynamic response, environmental stability, or operating range due to intrinsic impedance constraints,\nthis work uniquely overcomes such limitations by formalizing an impedance-driven amplification\nmechanism that activates previously suppressed polarization channels, and rigorously validates the\nresulting simultaneous enhancement of electrical and operational performance through impedance\nspectroscopy, systematic pressure-dependent measurements, and theoretical analysis. The\nintegration of well-established principles from dielectric spectroscopy, impedance analysis, and\nrelaxation dynamics into a coherent sensing framework that redefines impedance—traditionally\ntreated as a parasitic loss to be minimized—as an active gain knob for releasing suppressed\npolarization channels is quite novel. Therefore, this paper could be further considered for publication\nin Nature Communications, as long as the following comments can be well addressed:\nResponse: We sincerely thank the reviewer for the positive and insightful evaluation of our work\nand appreciate the precise recognition of the study’s central concept, which redefines series\nimpedance from a parasitic limitation into an active gain parameter for releasing suppressed\npolarization. This perspective is well aligned with our goal of establishing a general design principle\nfor high-performance non-ionic sensing.\nActing on the reviewer’s constructive comments, we have revised the manuscript to clarify the\nphysical basis of the impedance-driven mechanism and explicitly distinguish it from purely resistive\neffects. Furthermore, we have strengthened the mechanistic explanation for temperature-invariant\nperformance and improved the clarity of key concepts in the main text. With additional experimental\n1\nevidence and supporting analysis, these revisions further consolidate the validity and robustness of\nthe proposed sensing framework. Below, we address the reviewer’s comments point by point.\n1. It would be interesting to analyze whether the observed capacitance enhancement is uniquely\ntied to mechanical compression or whether comparable effects can be obtained by selectively\ntuning the series resistance without mechanical pressure.\nResponse: We thank the reviewer for raising this important question regarding whether the\ncapacitance enhancement is intrinsically dependent on mechanical compression or can be achieved\nthrough non-mechanical control of the series resistance. To directly address this point, we performed\ntwo sets of control experiments under zero mechanical pressure, aimed at independently tuning the\nseries resistance (Rs) within the same series readout framework.\nFirst, as shown in Figure R1a, we fixed the CNTs content in the electrode fiber layers (20\nwt.%) and systematically varied the CNTs content in the dielectric fiber layer (6–11 wt.%). This\ncompositional adjustment modifies the internal conductive pathways and thus the effective Rs,\nwithout altering the macroscopic device geometry or measurement configuration. The results show\nthat increasing the CNTs content in the dielectric layer leads to a monotonic decrease in Rs,\naccompanied by a corresponding increase in the extracted series capacitance (Cs), even in the\nabsence of mechanical compression. This demonstrates that reducing Rs alone is sufficient to\nenhance the apparent capacitance under a fixed measurement frequency.\nTo further decouple Rs modulation from material composition, we introduced external resistors\n(100 Ω to 1 MΩ) in series with the device to systematically vary the total series resistance at the\ncircuit level. As shown in Figure R1b, increasing the external resistance consistently suppresses the\nmeasured Cs across all tested pressure levels, yielding a monotonic Cs–Rs relationship that mirrors\nthe trend observed in the compositional tuning experiments. This circuit-level control provides an\nindependent validation that the capacitance enhancement originates from the series readout physics\nrather than from mechanical deformation per se.\nTogether, these two orthogonal approaches including internal Rs modulation via material\ncomposition and external Rs modulation via circuit elements clearly demonstrate that the\n2\ncapacitance enhancement observed in this work is not uniquely tied to mechanical compression.\nInstead, it arises from the regulation of the total series resistance, which governs the extent to which\npolarization responses can participate within the AC measurement window and be reflected in the\nextracted Cs. In this context, mechanical compression serves as an effective and reversible means to\ndynamically reduce Rs within a single device, thereby amplifying the capacitance readout and\nextending the accessible sensing range, rather than acting as a prerequisite for the underlying\nmechanism. Correspondingly, the manuscript and Supplementary Information have been revised to\nreflect this distinction.\nFigure R1 | Series resistance modulation and extracted capacitance without mechanical compression.\n(a) Extracted series resistance (Rs) and series capacitance (Cs) as a function of CNT content in the dielectric\nfiber layer, with the electrode fiber layer fixed at 20 wt.% CNT. Measurements were performed under zero\napplied pressure. (b) Extracted series capacitance (Cs) measured with externally added series resistors (100\nΩ–1 MΩ) under different applied pressures.\n[Manuscript]\nPage 11, Lines 23-27 and Page 12, Lines 1-4.\nNotably, the capacitance enhancement is not uniquely tied to mechanical compression. To\ndecouple impedance modulation from deformation, two zero-pressure control experiments were\nconducted (Supplementary Figure S13). First, with the electrode CNT loading fixed at 20 wt.%,\n3\nincreasing the dielectric CNTs content (6–11 wt.%) monotonically reduced 𝑅 and increased the\ns\nextracted 𝐶 . Second, introducing external series resistors (100 Ω–1 MΩ) systematically suppressed\ns\nthe measured𝐶 . Together, these results demonstrate that the enhanced impedance-derived apparent\ns\ncapacitance is governed by the total series resistance within the AC readout window, while\nmechanical compression mainly serves as a convenient and reversible means to reduce 𝑅 in a single\ns\ndevice.\n[Supplementary Information]\nFigure S13 | Series resistance modulation and extracted capacitance without mechanical compression.\n(a) Extracted series resistance (Rs) and series capacitance (Cs) as a function of CNT content in the dielectric\nfiber layer, with the electrode fiber layer fixed at 20 wt.% CNT. Measurements were performed under zero\napplied pressure. (b) Extracted series capacitance (Cs) measured with externally added series resistors (100\nΩ–1 MΩ) under different applied pressures.\n2. In Figures 3b and 3c, the outstanding temperature-independent performances are quite\ninteresting. However, the statement that “… enabled by the thermally robust polyimide\nmatrix …” in line 22 (page 12) is insufficient on its own. It would be informative to clarify\nwhether the observed temperature robustness arises from the impedance-driven sensing\nmechanism itself.\nResponse: We agree with the reviewer that attributing the temperature-invariant performance solely\n4\nto the thermal robustness of the polyimide (PI) matrix is insufficient. In the revised manuscript, we\ntherefore clarify that the observed wide-temperature stability is closely associated with the non-ionic,\nimpedance-driven sensing mechanism, which is fundamentally different from mechanisms\ndominated by ionic transport.\nTo examine this distinction, we performed a controlled comparison of three capacitive pressure\nsensors constructed using the same PI fiber matrix: the impedance-driven sensor (PI/CNTs),\na traditional capacitive sensor (PI), and an iontronic sensor (PI/IL). As shown in Figure R2, although\nall three devices employ the thermally stable PI matrix and remain operational at elevated\ntemperature, their sensing behaviors differ markedly at 200 °C. The iontronic sensor exhibits early\nsignal saturation and pronounced temperature sensitivity, while the traditional PI sensor shows only\na limited pressure response. In contrast, the impedance-driven sensor maintains a robust and\ncontinuous pressure-dependent signal under the same conditions.\nMechanistically, this difference arises because the impedance-driven readout relies on\nelectronic and interfacial polarization rather than ionic migration. Temperature variations do\ninfluence the conductivity of the CNTs network, affecting both the baseline capacitance (C₀) and\nthe response magnitude (ΔC). However, in the impedance-driven system, these parameters scale\nproportionally with temperature, such that the normalized response (ΔC/C₀) remains stable over a\nwide temperature range. This behavior indicates that the observed temperature robustness originates\nfrom the coupling of a thermally robust material platform (PI) with a thermally stable, impedance-\nregulated electronic polarization mechanism, rather than from material stability alone.\nCorresponding clarifications and supporting data have been incorporated into the revised\nmanuscript.\nfor three capacitive pressure sensors constructed on the same polyimide (PI) fiber matrix, including the\nimpedance-driven sensor (PI/CNT), the iontronic sensor (PI/IL), and the traditional capacitive sensor (PI).\n[Manuscript]\nPage 15, Lines 21-27 and Page 16, Lines 1-3.\nNotably, the temperature-invariant normalized response (Δ𝐶/𝐶 ) cannot be attributed to the\n0\nthermally robust PI matrix alone; it also benefits from the low-impedance sensing mechanism. This\noutstanding temperature-independent performance arises from the coupled mechanical–electrical\nstability of the fibrous network, where the PI framework maintains structural integrity while the\nimpedance-regulated readout stabilizes the electrical response. To clarify this point, we compared\nthree sensors with the same PI-based architecture, including the impedance-driven PI/CNT device,\na conventional capacitive PI device, and an iontronic PI/IL device (Supplementary Fig. S24), which\nshow markedly different capacitance responses at elevated temperature, confirming that the\nrobustness is not solely a matrix effect.\n[Supplementary Information]\nfor three capacitive pressure sensors constructed on the same polyimide (PI) fiber matrix, including the\nimpedance-driven sensor (PI/CNT), the iontronic sensor (PI/IL), and the traditional capacitive sensor (PI).\n3. In line 19 (page 2), the authors state that “Ionic materials are prone to leakage, evaporation,\nand thermal degradation.” This seems overgeneralized, as ion-confined polymer matrix\nsystems have shown low leakage and long-term stability.\nResponse: We thank the reviewer for pointing out that the original statement was overly broad. In\nthe revised manuscript, we have replaced “ionic materials” with “ionic systems relying on mobile\nions” to more precisely define the scope of the discussion, as the original concern primarily applies\nto systems dominated by free ion migration rather than ion-confined polymer matrices. The\ncorresponding text has been revised accordingly.\n[Manuscript]\nPage 3, Lines 5-8.\nHowever, such performance often comes with practical challenges in harsh environments,\nbecause ionic systems relying on mobile ions may suffer from leakage, evaporation, and thermal\ndegradation16,22, and their electrical response can exhibit pronounced temperature dependence.\n7\n4. Given that Figure 1b (inset) suggests a millimeter-scale thickness, additional clarification of\nthe air-jet spinning process would improve the clarity and reproducibility of the fabrication\nmethod.\nResponse: We thank the reviewer for the suggestion regarding the clarification of the airflow-\nspinning process. In the revised manuscript, we have supplemented the full set of fabrication\nparameters in the Methods and Supplementary Information, including the solution feeding rate, gas\npressure and temperature, nozzle diameter, collection distance, and deposition time for each layer.\nWe further clarify that the millimeter-scale device thickness (~2 mm) is not determined by\nuncontrolled fiber stacking, but is defined during the high-temperature imidization process using a\nfixed 2 mm mold cavity, ensuring good thickness reproducibility across samples. All fiber layers\nwere fabricated under identical spinning conditions to minimize layer-to-layer variability. The\ncorresponding details have been added to the Methods and Supplementary Information for\nreproducibility.\n[Manuscript]\nPage 7, Lines 21-22.\nThese layers were fabricated through an airflow-spinning process under identical processing\nconditions, followed by thermal imidization of poly(amic acid) into polyimide (PI) with excellent\ntemperature resistance.\nPage 7, Lines 25-27, Page 8, Line 1.\nNotably, the millimeter-scale device thickness (~2 mm; inset of Figure 1b) is not dictated by\nuncontrolled fiber stacking, but is defined during thermal imidization using a fixed 2 mm mold\ncavity, ensuring good thickness reproducibility across samples (Supplementary Figure S5).\nPage 25, Lines 25-27 and Page 26, Lines 1-7.\nThe porous fiber sponges were fabricated via a sequential airflow-spinning process. Unless\n8\notherwise specified, all three layers were deposited under the same spinning window, and the CNTs\nloading was the only intentional variable between the electrode and dielectric layers. Specifically,\nthe precursor solution was delivered from a 1 mL syringe through a 30G nozzle (The inner diameter\nis about 160 μm) at 2 mL/h, while compressed air at 69 kPa (air velocity ~21 m/s at the nozzle exit)\nwas heated to 80 °C and applied coaxially; the collection distance was 30 cm and each layer was\ncollected for 15 min. After sequential deposition of the bottom electrode, dielectric, and top\nelectrode layers, the stack was thermally imidized and hot-pressed in a fixed 2 mm mold cavity,\nwhich defines the final device thickness and improves layer-to-layer uniformity.\n5. The Introduction currently lacks sufficient citations to recent wearable/implantable tissue-like\nstudies that are essential for establishing tissue-device conformality. I am therefore suggesting\nseveral relevant papers and would like you to incorporate them accordingly. (Nature\nNanotechnology (2025) DOI: https://doi.org/10.1038/s41565-025-02031-x; Nature Electronics\n8, 474-484 (2025) ; Nature Electronics 7, 800 (2024) ; Nature Nanotechnology 17, 849 (2022))\nResponse: We thank the reviewer for providing these important references. The suggested recent\nstudies have been incorporated into the Introduction to strengthen the discussion on tissue-like\nconformality of wearable and implantable devices and to better position the present work within this\ncontext.\n[Manuscript]\nPage 2, Lines 16-26.\nMeanwhile, beyond environmental stability, practical wearable and implantable applications\nfurther requires tissue-like conformal contact to maintain long-term signal fidelity on soft and\ndynamically deforming surfaces. Recent advances in tissue-like wearable and implantable\nbioelectronics have underscored that long-term signal fidelity critically depends on device structure\nconformality, such as mechanically compliant, and stable contact with biological tissues. Notable\n9\nstrategies, including conformal nanomembranes12, soft morphing interfaces13, and mechanically\nmatched architectures14,15, which have demonstrated improved bio integration and reduced motion-\ninduced artifacts, enabling more reliable sensing in practical wearable settings. However, integrating\ntissue-like conformality with wide-temperature robustness and drift-resistant readout under harsh\nconditions remains nontrivial, especially for sensing schemes in which signal generation relies on\nmobile-ion transport.\n6. At present, several key concepts, terms, and symbols (e.g., transition frequency, impedance\nrelease, the physical meaning of |X| evolution, and the relationship between series resistance\nand polarization activation) are difficult to understand from the main text alone. A reader must\nconsult Supplementary Note 1 to follow the core mechanism, which reduces the readability of\nthe paper. The authors should consider expanding the main text to include concise but clear\nexplanations of these concepts.\nResponse: We agree with the reviewer and have revised the main text to improve the readability of\nthe core mechanism. Specifically, concise explanations of key concepts have been added to Sections\n2.1 and 2.2, including the physical meaning of the transition frequency (ω ), the pressure-dependent\nc\nevolution of |X|, and the relationship between series resistance (Rs) and polarization activation.\nWith these additions, the essential mechanism can now be understood directly from the main\ntext, while Supplementary Note 1 is retained only for the complete theoretical derivation.\n[Manuscript]\nPage 7, Lines 6-18.\nIn the series-readout representation, the device response can be described by an effective series\nresistance 𝑅 in series with an effective storage capacitance 𝐶 , such that the capacitance reported by\n𝑠 𝑠\nthe LCR meter is an impedance-derived apparent capacitance rather than a purely geometric\ncapacitance. Whether this storage component can be expressed within a given AC measurement\n10\nwindow is governed by the electrical relaxation time of the series branch, 𝜏 ∼ 𝑅 𝐶 , which defines\n𝑠 𝑠\na characteristic (transition) frequency\n1 1\n𝜔 ≈ ≈ (𝑓 = 𝜔 /2𝜋)\n𝑟𝑡 𝜏 𝑅 𝐶 𝑐 𝑟𝑡\n𝑠 𝑠\nA larger 𝑅 leads to a longer 𝜏, thereby constraining polarization-related charge storage at a\n𝑠\nfixed test frequency, whereas a reduced 𝑅 shortens 𝜏and shifts 𝜔 to higher frequency, making a\n𝑠 𝑟𝑡\nlarger fraction of the storage response observable. In this context, impedance release refers to the\npressure-induced decrease of 𝑅 , which increases 𝜔 and enables an enhanced apparent capacitive\n𝑠 𝑟𝑡\noutput under the same AC readout conditions; the frequency-domain evidence for this shift is\ndiscussed in following text.\nPage 10, Lines 16-18.\nThe frequency evolution of the impedance imaginary component provides a spectral signature\nof this shift (Figure 2b). Here, |𝑋| denotes the magnitude of the imaginary impedance.\n7. Figure 2d appears to exhibit two clearly different sensing regimes at low- and high-pressure\nranges, corresponding to different slopes. However, these results are not clearly visible in the\ncurrent figure. The authors should consider annotating the extracted slopes for these two\nregions and adding an inset that enlarges the low-pressure area.\nResponse: We thank the reviewer for this helpful suggestion. In the revised manuscript, Figure 2d\nhas been updated (now shown as Figure R3) to explicitly annotate the extracted linear slopes in the\nlow- and high-pressure regimes (1200 kPa - 8000 kPa). In addition, an enlarged inset highlighting\nthe low-pressure region (20 Pa - 1200 kPa) has been added to improve the visibility of the distinct\nsensing behaviors in the two pressure ranges.\ndifferent CNTs contents in the electrode and dielectric layers, with the extracted linear sensitivities annotated\nfor different pressure ranges. (b) Enlarged view of the low-pressure region highlighting the corresponding\nsensitivities.\n[Manuscript]\nPage 12, Lines 10-11.\nFigure 2e shows the enlarged sensitivity map in the low-pressure range, highlighting the\ncorresponding sensitivity differences.\n8. In Figure S6, the cross-sectional SEM images suggest noticeable differences in fiber thickness\nbetween the electrode layers and the dielectric layer. It is not clear whether this difference\noriginates from variations in electrospinning conditions (e.g., solution properties or CNT\nloading), or whether it is an intentional design choice. In addition, the overall thickness of each\nlayer and of the full device is not clearly specified. According to the Methods section, each\nlayer appears to be fabricated using a 1 mL syringe over approximately 15 minutes. Based on\nthe device photographs, the assembled sensor appears relatively thick, which raises questions\nabout layer-to-layer thickness uniformity. Could authors report the average fiber diameter and\nthickness distribution for each layer, the thickness of each individual layer, and the total\nthickness of the assembled device.\nFigure S6 and for raising questions regarding fiber diameter variation and layer thickness uniformity.\nIn the revised manuscript, we have clarified that the observed difference in fiber thickness between\nthe electrode and dielectric layers originates primarily from differences in solution composition,\nparticularly the CNTs loading, which leads to changes in solution viscosity during airflow-spinning,\nrather than from variations in processing conditions.\nDuring fabrication, all fiber layers were produced using the same airflow-spinning parameters,\nincluding feeding rate, gas pressure, nozzle diameter, collection distance, and deposition time (15\nmin per layer), with the CNTs content being the only intentional difference between layers. To\nquantitatively address the concern regarding fiber uniformity, we have added statistical analyses of\nfiber diameter distributions for each layer (Figure R4). The average fiber diameter is approximately\n2.25 ± 0.32 μm for the electrode layer and 2.12 ± 0.25 μm for the dielectric layer.\nWe also report the thickness of each layer and of the assembled device. The thickness of a\nsingle fiber layer is approximately 0.67 mm, and the total thickness of the three-layer device is ~2\nmm. Importantly, the final device thickness is not determined by uncontrolled fiber stacking, but is\ndefined during the high-temperature imidization process using a fixed 2 mm mold cavity, which\nensures good thickness uniformity across layers and between devices. Corresponding details have\nbeen added to the Methods and Supplementary Information.\nof the assembled all-fiber capacitive pressure sensor. (b) SEM image of the electrode fiber layer and the\ncorresponding fiber diameter distribution. (c) SEM image of the dielectric fiber layer and the corresponding\nfiber diameter distribution.\n[Manuscript]\nPage 7, Lines 21-22.\nThese layers were fabricated through an airflow-spinning process under identical processing\nconditions……\nPage 8, Lines 9-13.\nImportantly, the differences between the electrode and dielectric fibers are caused by the\ncontent of CNTs in the spinning dope, whereas the airflow-spinning conditions were kept identical\nfor all layers, helping minimize processing-induced variability and improve diameter uniformity and\nreproducibility (Supplementary Figure S5).\n[Supplementary Information]\nFigure S5 | Structural characterization of the all-fiber capacitive sensor. (a) Cross-sectional SEM image\nof the assembled all-fiber capacitive pressure sensor. (b) SEM image of the electrode fiber layer and the\n14\ncorresponding fiber diameter distribution. (c) SEM image of the dielectric fiber layer and the\ncorresponding fiber diameter distribution.\n9. In Figure 2d, devices using metallic foil electrodes show significantly inferior sensing\nperformance compared to those with porous fiber electrodes. However, the manuscript does\nnot clearly explain why metallic electrodes fail to provide effective sensing in this system. A\nmore explicit explanation is needed, particularly addressing why high electrical conductivity\nalone is insufficient. Clarifying this point would strengthen the design rationale for porous\nelectrodes.\nResponse: We thank the reviewer for this insightful question regarding the role of electrode\nconductivity. As shown in Figure R5, the use of a metallic Cu foil electrode indeed leads to a higher\nabsolute capacitance due to its high electrical conductivity. However, in the impedance-driven\nsensing framework proposed in this work, sensing performance is not determined by conductivity\nalone, but rather by whether the electrode–dielectric interface provides sufficient and controllable\nimpedance modulation.\nBecause the Cu foil electrode presents a dense and continuous conductive surface, extensive\ninterfacial contact with the dielectric layer is already established in the initial state, resulting in a\nhigh baseline capacitance (C₀). This significantly reduces the relative capacitance change (ΔC/C₀)\nand limits the achievable sensitivity. In contrast, the porous fiber electrode exhibits restricted initial\ncontact and higher interfacial impedance, allowing polarization to be gradually activated under\napplied pressure. This enables a wider dynamic modulation range of the series impedance and leads\nto a more effective and stable capacitance readout.\nTherefore, the inferior sensing performance of metallic foil electrodes in this system does not\narise from insufficient conductivity, but from the lack of tunable interfacial impedance space, which\nis essential for impedance-regulated amplification in the proposed sensing mechanism.\nCorresponding explanations and the additional comparative data have been incorporated into the\nrevised main text and Supplementary Information.\ncontent 20 wt%) and a metallic Cu foil electrode as a function of applied pressure.\n[Manuscript]\nPage 12, Lines 16-23.\nImportantly, because the Cu foil electrode presents a dense and continuous conductive surface,\nextensive interfacial contact with the dielectric layer is already established in the initial state,\nresulting in a high baseline capacitance (C₀). This significantly reduces the relative capacitance\nchange (ΔC/C₀) and limits the achievable sensitivity. In contrast, the porous fiber electrode exhibits\nrestricted initial contact and higher interfacial impedance, allowing polarization to be gradually\nactivated under applied pressure. This enables a wider dynamic modulation range of the series\nimpedance and leads to a more effective and stable capacitance readout (Supplementary Figure\nS16).\n[Supplementary Information]\ncontent 20 wt%) and a metallic Cu foil electrode as a function of applied pressure.\n10. Figures S1b and S1c suggest that ionic capacitive pressure sensors typically exhibit nF level\nsignals, whereas the proposed impedance-based capacitive sensors operate at the μF level.\nHowever, Figure S13 shows that under an 80 kPa load, the measured capacitance change is on\nthe order of ~25 nF. This discrepancy may give the impression that the signal magnitude is\noverstated. The authors should clarify the operating conditions under which μF level signals\nare achieved.\nResponse: We thank the reviewer for pointing out this potential source of confusion. The\n“pF/nF/μF-level signal” labels in Figures S1b and S1c are schematic, order-of-magnitude\nillustrations intended to compare typical signal scales associated with different sensing mechanisms,\nrather than to represent the capacitance change at a single fixed pressure.\nFor the impedance-driven sensor reported in this work, μF-level capacitance values are\nobtained under high-pressure operation near the upper end of the working range (MPa-level\npressures), where the extracted series capacitance (or absolute capacitance, depending on the\nplotting format) increases into the μF regime. In contrast, the dynamic characterization shown in\nFigure S13 corresponds to a low-pressure condition (80 kPa), under which the absolute capacitance\nchange is naturally in the nF range.\nThese results are therefore fully consistent, as they reflect different pressure regimes and\n17\nmeasurement contexts rather than a discrepancy in signal magnitude. To avoid any potential\nmisunderstanding, we have explicitly clarified the corresponding operating conditions and pressure\nranges in the main text and Supplementary Information.\n[Supplementary Information]\nNote in Figure S1.\nHere, the “μF-level” refers to the signal magnitude near the upper end of the pressure range\n(MPa-level), rather than at low-kPa loading.\n11. The fire-scenario demonstration effectively shows that the sensor can operate stably at elevated\ntemperatures. However, a real fire scenario typically involves conditions beyond uniform high\ntemperature, such as direct flame exposure, oxidation, and transient thermal gradients. The\nauthors have any additional analysis, characterization, or testing under more extreme fire-\nrelated conditions?\nResponse: We thank the reviewer for this important comment regarding the relevance of fire-related\nconditions. We would like to clarify that the fire-scenario demonstration presented in this work is\nnot intended to represent a realistic or sustainable operating condition for the sensor, but rather to\nillustrate its behavior under elevated temperature prior to material degradation. The intended\nworking regime of the device is below the thermal stability limit of the constituent materials\n(≤200 °C), where long-term and repeatable operation can be reliably maintained.\nIn response to the reviewer’s suggestion, we have additionally conducted a more extreme test\ninvolving direct flame exposure to more closely mimic a real fire scenario. As shown in Figure R6,\nafter direct burning, the sensor no longer exhibits stable or repeatable sensing behavior. Instead, the\nsignal becomes abnormally elevated and highly unstable. This degradation is attributed to\nirreversible material damage, including carbonization and disruption of the fibrous network, which\nfundamentally alters the electrical pathways and impedance characteristics. These results indicate\n18\nthat direct flame exposure exceeds the intrinsic thermal tolerance of the current PI/CNTs material\nsystem.\nImportantly, this outcome does not contradict the conclusions of this work. Rather, it highlights\nthe distinction between stable high-temperature operation within the material’s thermal endurance\nwindow and extreme fire conditions that lead to material decomposition. Reliable sensing\nperformance in such extreme environments would require additional design strategies, such as the\nincorporation of flame-resistant or ablation-resistant materials, protective coatings, or thermal\nbarrier layers, which are beyond the scope of the present study but represent meaningful directions\nfor future exploration.\nCorresponding descriptions and the additional experimental results have been included in the\nrevised manuscript to clarify the operational boundaries and to address the reviewer’s concern.\nFigure R6 | Comparison of optical images and capacitive signals of the sensor before and after burning in an\nalcohol lamp flame for 30 seconds.\n19\n[Manuscript]\nPage 8, Lines 1-4.\nMeanwhile, the device is intended to operate within the thermal endurance window of the\nPI/CNT system, while direct flame exposure beyond this limit may induce irreversible\ndecomposition/carbonization and unstable signals (Supplementary Figure S6).\n[Supplementary Information]\nFigure S6 | Comparison of optical images and capacitive signals of the sensor before and after burning in an\nalcohol lamp flame for 30 seconds.\nReviewer #2:\nComment: The manuscript reports an impedance-driven PI–CNT all-fiber capacitive sensor with\nhigh sensitivity, MPa-level pressure operation, and wide temperature tolerance; similar research has\nbeen reported [1–4]. These results are promising, but several scientific and methodological issues\nmust be addressed before the work can be considered for publication in a high-impact venue. The\n20\nmechanistic validation of the proposed “impedance-release” concept remains largely qualitative.\nThe contributions of geometric effects, dielectric response, and impedance evolution are not\nquantitatively separated, and alternative explanations—such as compression-induced CNT\ntunneling or leakage—are not rigorously excluded.[1–3] Frequency-dependent behavior, noise/drift\ncharacteristics, and device-to-device reproducibility are also insufficiently explored. In addition,\nthe repeated reference to “real-time feedback” is not supported, as the system demonstrates\nsensing only.[9,10] The finite-element analysis (FEA) relies on constitutive assumptions inconsistent\nwith the highly porous (>90%) fiber architecture, limiting its mechanistic reliability.[2–4] Moreover,\nclaims of universality are not substantiated because all demonstrations are restricted to a single PI–\nCNT platform without validation in chemically distinct systems.[2,3,5] Finally, the evaluation of\npressure mapping and the deep-learning demonstration lacks statistical rigor, controlled\ncomparisons, and methodological transparency.[11] Overall, the study presents a compelling device\nconcept but currently reads as a high-performance demonstration of a single optimized system rather\nthan a fully validated, generalizable design framework. Addressing the mechanistic, modeling,\nreproducibility, and benchmarking issues will substantially strengthen the manuscript.\nReferences\n[1] Kang, Z. et al. “Highly Sensitive Flexible Pressure Sensors Enabled by Mixing of Silicone Elastomer\nwith Ionic Liquid-Grafted Silicone Oil.” Frontiers in Robotics and AI 2021\n[2] Farman, M. et al. “All-polydimethylsiloxane-based highly flexible and stable capacitive pressure sensors\nwith engineered interfaces for conformable electronic skin.” ACS. Applied Material and Interfaces.\n2023\n[3] Qin, T., et al. “A New Strategy for the Fabrication of a Flexible and Highly Sensitive Capacitive Pressure\nSensor.” Microsystems & Nanoengineering 2021\n[4] Bai, N. et al. “Graded Intrafillable Architecture-Based Iontronic Pressure Sensor with Ultra-Broad-\nRange High Sensitivity.” Nature Communications 2020\n[5] Wang, H. et al. “Flexible Capacitive Pressure Sensors for Wearable Electronics.” Journal of Materials\nChemistry C 2022\n[6] Tian, H. et al. “Hierarchical Synergetic Strategy for Iontronic Pressure Sensors with High Sensitivity\nand Wide Dynamic Range.” ACS Sensors 2025\n[7] Huang, Y., et al. “Programmable High-Sensitivity Iontronic Pressure Sensors Support Wide-Range\n[8] He, Z., et al. “Hofmeister Effect Regulated Gel Iontronic Sensors for Wide-Range Pressure Detection.”\nThe Innovation: Materials 2024\n[9] Y. Luo et al., “Adaptive tactile interaction transfer via digitally embroidered smart gloves”, Nature\nCommunications, 2024\n[10] K. Ha et al., “Full freedom-of-motion actuators as advanced haptic interfaces”, Science, 2025\n[11] S. Ji et al., “3D Convolutional Neural Networks for Human Action Recognition”, IEEE Transactions\non Pattern Analysis and Machine Intelligence, 2023\nResponse: We thank the reviewer for the thorough and constructive assessment of our work. For a\nmechanism-oriented study, it is essential to clearly distinguish performance demonstration from\nphysical interpretation, to avoid overgeneralization, and to establish well-defined applicability\nboundaries.\nIn the revised manuscript, we have systematically addressed the reviewer’s major concerns by\nstrengthening the mechanistic validation of the impedance-driven concept, clarifying the respective\nroles of geometry, dielectric response, and impedance evolution through additional control\nexperiments and quantitative analysis, and explicitly refining the scope of our claims. We have also\nrevised the modeling section to clarify the assumptions and limitations of the finite-element analysis,\nimproved the transparency of experimental methodologies and data processing, corrected imprecise\nterminology (e.g., “real-time feedback”), and supplemented statistical and comparative analyses\nwhere appropriate.\nWe believe that these revisions reposition the work from a performance-focused demonstration\nof a single device toward a mechanism-driven study with clearly articulated physical boundaries and\ndesign principles. Detailed point-by-point responses to each specific comment are provided below.\n1. Mechanism not quantitatively validated; alternative explanations not excluded. Section 2.2 and\nSupplementary Note 1 ascribe the large observed ΔC to an “impedance-release” mechanism, in\nwhich pressure-induced reductions in Rs and shifts in the polarization transition frequency are\nproposed to dominate the response. However, the data in Fig. 2a–f and Supplementary Fig. S9\n22\ndo not quantitatively disentangle the respective contributions of geometric effects, intrinsic\ndielectric properties, and impedance evolution.[1–4] In addition, CNT-based composites are\nwell known to exhibit compression-induced tunneling and the formation of leakage pathways,\nboth of which can manifest as an apparent increase in capacitance in porous structures.[1–3]\nIn the present manuscript, these alternative pathways have not yet been rigorously differentiated\nfrom the proposed mechanism. A more systematic analysis is required to clarify whether the\npressure dependence of Rs primarily reflects activated polarization, or whether it is instead\ndominated by changes in conductive pathways and leakage currents. In iontronic systems,\ncapacitances of this magnitude are typically associated with electric-double-layer formation at\nsoft interfaces,[4–8] whereas in the present non-ionic architecture the existence of such\ninterfacial mechanisms is not clearly established. Additional controls and quantitative checks\nare therefore needed to demonstrate that the observed behavior cannot be fully explained by\nleakage or tunneling effects.[4–8]\nResponse: We thank the reviewer for this critical comment. We fully agree that, in porous CNT-\nbased composites, compression can strengthen conductive pathways (tunneling/percolation) and\nincrease dissipation, which may contaminate capacitance extraction if conduction and polarization\nare not separated. We therefore revised the analysis to (i) bound the geometric contribution,\n(ii) quantitatively separate polarization- and conduction-related losses, and (iii) explicitly link the\npressure dependence of Rs to the readout-window criterion that governs whether polarization\nresponses are reflected in the extracted series capacitance.\n(i) Geometric upper bound. Under compression, the geometric capacitance change is bounded\nby the thickness reduction (and modest contact-area change). Even for large compressive strain, the\nexpected geometric scaling 𝛥𝐶/𝐶~𝛥𝑑/𝑑 is orders of magnitude smaller than the experimentally\nobserved 𝛥𝐶/𝐶 (MPa-range response), indicating that geometry alone cannot account for the large\n0\napparent capacitance amplification. For clarity, this geometric contribution is intentionally not\nsubtracted from the measured signal, as retaining the full capacitance response represents a\nconservative assessment and avoids over-attribution of the observed amplification.\n23\n(ii) Leakage/tunneling vs polarization in the frequency domain. Within the LCR readout\nwindow, we indeed observe an increase in loss tangent and a reduced phase angle upon compression\n(Figure R7), confirming that resistive loss becomes non-negligible. However, this does not imply\nthat the apparent capacitance increase is dominated by leakage. We therefore performed broadband\ndielectric spectroscopy and decomposed the imaginary permittivity as 𝜀′′(𝜔) = 𝜀′′ (𝜔)+\npol\n𝜀′′ (𝜔), where 𝜀′′ (𝜔) = 𝜎′(𝜔)/(𝜀 𝜔). As shown in Figure R8, 𝜀′′ remains consistently\ncond cond 0 cond\nabout two orders of magnitude lower than 𝜀′′ across 300 Hz–10 MHz, while 𝜀′′ exhibits the\npol pol\ncharacteristic relaxation dispersion and increases systematically under compression. These\nobservations provide quantitative evidence that the dominant spectral evolution is governed by\npolarization/relaxation processes, with conduction-related loss playing a secondary but non-\nnegligible role under load.\n(iii) What 𝑹 (𝑷) means in our mechanism. We refined the mechanistic wording to reflect\n𝒔\nthe coupled roles of conduction and polarization: compression-induced evolution of conductive\npathways reduces the effective 𝑅 , and the key consequence is that the dimensionless readout\n𝑠\ncriterion 𝜔𝐶𝑅 shifts from the R-dominant regime toward the transition/C-dominant regime (Figure\n𝑠\nR7). This shift moves a larger fraction of the intrinsic polarization/relaxation response into the AC\nmeasurement window, thereby being captured as an increased extracted series capacitance.\nImportantly, the large apparent capacitance observed here is not attributed to electric double layer\nformation or trivial leakage-induced “false capacitance,” but rather to impedance-regulated access\nto intrinsic polarization/relaxation responses in a non-ionic architecture, with increased dielectric\nloss serving as an accompanying boundary condition.\nCorresponding revisions and the new quantitative analyses have been incorporated into the\nrevised main text and Supplementary Information.\nequivalent readout). (b) Phase angle as a function of frequency measured using an LCR meter under different\ncompressive states.\nrelated contribution to the imaginary permittivity calculated from the measured conductivity. (d)\nPolarization-related contribution obtained by subtracting the conduction term from the total imaginary\npermittivity, measured under 0%, 50% and 80% compressive strain.\n[Manuscript]\nPage 10, Lines 11-15.\nAt 0 kPa, the device lies in the transition regime, and increasing pressure enhances electronic\nconduction in the CNTs network and reduces 𝑅 , progressively relaxing the resistive constraint.\n𝑠\nAccordingly, the extracted 𝐶 is an impedance-derived apparent capacitance, for which a lower\n𝑠\n𝑅 makes polarization-related storage increasingly accessible at the measurement frequency.\n𝑠\nPage 10, Lines 25-27 and Page 11, Lines 1-12.\nTo quantitatively differentiate conduction-dominated artifacts from polarization-related loss,\nwe performed broadband dielectric spectroscopy of the full device under representative compressive\nstrains (0, 50, and 80%). The measured imaginary permittivity 𝜀′′(𝜔)was decomposed into a\nconduction term (𝜀′′ (𝜔)) and a polarization-loss term (𝜀′′ (𝜔)) according to\ncond pol\n𝜎′(𝜔)\n𝜀′′(𝜔) = 𝜀′′ (𝜔)+𝜀′′ (𝜔), 𝜀′′ (𝜔) =\npol cond cond 𝜀 𝜔\n0\nwhere 𝜎′(𝜔)is the real part of AC conductivity, 𝜀 is the vacuum dielectric constant (𝜀 ≈\n0 0\n8.854×10−12 F/m). Notably, across the measured frequency range and for all strains, the\nconduction contribution 𝜀′′ (𝜔) remains about two orders of magnitude smaller than 𝜀′′ (𝜔)\ncond pol\nover most of the measured frequency range, indicating that the imaginary permittivity is dominated\nby polarization-related dissipation rather than pure conduction leakage. Meanwhile, compression\nincreases 𝜎′(𝜔)(consistent with enhanced conductive pathways and loss), but 𝜀′′ (𝜔) remains the\npol\nmain component and evolves systematically with strain, supporting that the capacitance\namplification is not solely a leakage-driven artifact (Supplementary Figure S12).\nUnder the series-equivalent readout and within the transition-to-capacitive regime, the\nconcurrent reduction of 𝑅 and |𝑋| is consistent with an increase in the extracted series capacitance\n𝑠\n𝐶 at the same measurement frequency. This behavior reflects a pressure-induced shift of the readout\ns\nwindow governed by 𝜔𝐶 𝑅 through which polarization-related responses become increasingly\ns 𝑠\naccessible in the AC measurement. In this sense, the observed impedance-controlled capacitance\namplification represents the experimental manifestation of the pressure-dependent polarization term\n𝐶 (𝑃)Φ(𝜔/𝜔 (𝑃)), as formalized in the impedance-release model in Supplementary Note 1.\n𝑝𝑜𝑙 𝑡𝑟\n[Supplementary Information]\nFigure S11 | Frequency-dependent impedance characteristics of the impedance-driven all-fiber sensor.\n(a) phase angle as a function of frequency measured using an LCR meter under different compressive states.\n(b) Measured capacitance (𝐶 ) as a function of frequency under different applied pressures (0, 200, 1000, and\ns\n27\n8000 kPa). The capacitance decreases with increasing frequency, and its magnitude grows significantly with\npressure, confirming the pressure-induced enhancement of dipole polarization. (c) Corresponding series\nresistance (𝑅 ) versus frequency at the same pressure levels. The Rs value decreases by several orders of\ns\nmagnitude as the pressure increases and shows a rightward shift of the characteristic frequency boundary,\ndemonstrating accelerated polarization dynamics and the transition from resistive- to capacitive-dominant\nbehavior.\nFigure S12 | (a) Frequency-dependent loss tangent, (b) real part of the dielectric permittivity, (c) conduction-\nrelated contribution to the imaginary permittivity calculated from the measured conductivity, and (d)\npolarization-related contribution obtained by subtracting the conduction term from the total imaginary\npermittivity, measured under 0%, 50% and 80% compressive strain.\n2. FEA does not adequately represent the highly porous architecture. In Fig. 2g, h and\nNeo-Hookean solids with GPa-level Young’s moduli taken from tensile tests. However, Fig. 1g\nand the Supplementary tables/figures (porosity > 90%) indicate that the actual structure is a PI–\nCNT fiber sponge exhibiting foam-like compressive densification. Incompressible\nNeo-Hookean models are generally appropriate for dense elastomeric solids, whereas open-cell\nfoams and fiber networks are typically described by compressible hyperelastic or foam-type\nconstitutive laws that explicitly capture volumetric strain and pore collapse.[2–4] Under these\nassumptions, the D/E/ED simulations can only be interpreted as qualitative approximations; the\nfact that the ED case reproduces a two-slope C–P curve is not, by itself, a physically faithful\nvalidation of the mechanistic behavior of the porous PI–CNT architecture. At a minimum, the\nmain text should explicitly acknowledge these limitations, and ideally the simulations should be\nupdated using a compressible/foam-type model calibrated to the measured compressive stress–\nstrain response.[2–4]\nResponse: We thank the reviewer for the rigorous and technically well-founded critique regarding\nthe applicability of our finite-element analysis (FEA) to a highly porous (>90%) PI–CNTs fibrous\narchitecture. We therefore clarify and revise the manuscript to explicitly delimit the role and validity\nof the FEA used in this work.\nThe simulations were not intended to reproduce the detailed mechanical behavior of the porous\nPI–CNTs sponge, nor to provide a quantitative validation of the impedance-release mechanism.\nInstead, the FEA serves as a trend-level, homogenized comparison between three idealized\ndeformation modes under identical boundary conditions—coupled deformation of electrode and\ndielectric layers (E–D), deformation of the electrode layers only (E), and deformation of the\ndielectric layer only (D). To avoid any implication of microstructural fidelity, we have revised the\ntext to emphasize that the porous architecture is treated in a homogenized manner and that the\nsimulations do not resolve explicit pores, fiber bending, or foam-like densification.\nWithin this deliberately limited framework, the purpose of the FEA is strictly comparative\nrather than predictive: it illustrates that deformation of the electrode layers alone primarily alters\n29\nconductive pathways, deformation of the dielectric layer alone produces only a weak electrical\nresponse, and that a pronounced amplification tendency is observed in this idealized comparison\nonly when deformation of electrode and dielectric layers is coupled. This qualitative comparison is\nused solely to support the cooperative interpretation of electrode–dielectric deformation, and not as\na physically faithful model of the porous micromechanics or a quantitative validation of the\nexperimental pressure–capacitance response.\nTo make these assumptions and limitations unambiguous, we have revised the main text and\nSupplementary Information and added a schematic illustration (Figure R9) that summarizes the\nconceptual deformation modes considered in the simulations, without implying a realistic\nrepresentation of the fibrous sponge structure. We also explicitly state in the revised manuscript that\na compressible foam-type constitutive model calibrated to the measured compressive stress–strain\nresponse would be required for quantitative mechanical modeling of the porous architecture, which\nlies beyond the scope of the present study.\nFigure R9 | Schematic representation of deformation modes considered in the finite-element analysis.\nConceptual illustration of the three idealized cases used for comparative analysis: coupled deformation of\nelectrode and dielectric layers (E–D), deformation of the electrode layers only (E), and deformation of the\ndielectric layer only (D). Arrows indicate applied compressive loading and symbols denote constrained\nboundaries. The figure is intended to clarify deformation-mode comparisons rather than to represent the\ndetailed micromechanical behavior of the porous fiber network.\n30\n[Manuscript]\nPage 26, Lines 15-27 and Page 27, Lines 1-8.\nFinite Element Analysis: Finite-element simulations were performed using COMSOL\nMultiphysics. The highly porous (>90%) PI/CNT fibrous architecture was represented in a\nhomogenized manner: instead of explicitly constructing macroscopic pores in the geometry, the\nporosity of each layer was incorporated through COMSOL’s porous-media representation, in which\nporosity is treated as an internal model parameter. Mechanical parameters for the electrode and\ndielectric layers were assigned based on the experimentally measured compressive stress–strain\nresponses of the corresponding layers. Electrical material parameters of the three-layer structure\nwere defined consistently with the experimentally measured layer-wise electrical characteristics and\nupdated according to the measured pressure-dependent resistance evolution of each layer.\nThe deformation of the layered structure under applied pressure was solved using the Solid\nMechanics module. A uniform pressure load was applied on the top surface, while the bottom\nsurface was constrained (𝑈 = 0).\n𝑧\nContact interfaces between layers were assumed to be ideal\n(frictionless and non-penetrative) to simplify interfacial coupling in the integrated model. The\ncomputed deformed configurations were then used as input for the electrical simulations.\nThe complex impedance response was computed using the AC/DC module under small-signal\nAC excitation. For electrical boundary conditions, the bottom surface of the model was grounded,\nwhile a voltage boundary was applied to the top surface. The overall impedance of the three-layer\narchitecture was extracted, with both real and imaginary components calculated as a function of\napplied pressure. These results were used to support a trend-level analysis of the impedance-driven\ncapacitive enhancement behavior in the proposed sensor structure.\n[Supplementary Information]\nanalysis. Conceptual illustration of the three idealized cases used for comparative analysis: coupled\ndeformation of electrode and dielectric layers (E–D), deformation of the electrode layers only (E),\nand deformation of the dielectric layer only (D). Arrows indicate applied compressive loading and\nsymbols denote constrained boundaries. The figure is intended to clarify deformation-mode\ncomparisons rather than to represent the detailed micromechanical behavior of the porous fiber\nnetwork.\n3. Generality claims are not supported by evidence beyond a single PI–CNT system. The\nmanuscript presents the proposed approach as broadly applicable to heterogeneous non-ionic\ncomposites, yet all experimental demonstrations are confined to a single PI–CNT all-fiber\nsystem. [1–4] Given the extensive literature on diverse polymer–filler combinations (e.g.\nPDMS/CNT or PDMS/graphene, MXene/polymer composites, PU/PI foams, carbon-black or\nmetal-nanowire elastomers), it would be important to verify whether similar impedance-driven\ntrends can be reproduced in at least one chemically distinct platform in order to substantiate\nthe notion of a “general design rule”. As currently presented, the evidence more strongly supports\na system-specific optimization of PI–CNT fiber networks than with a broadly validated universal\nstrategy.[2,3,5]\nResponse: We thank the reviewer for this careful and well-founded concern regarding the generality\nof the proposed approach. Accordingly, we emphasize that the present work does not aim to replace\n32\nor generalize the diverse material strategies reported in refs. [1–5], nor to suggest that identical\nperformance can be expected across all non-ionic systems. Instead, our objective is to identify a\ntransferable readout principle at the level of the equivalent circuit, namely that modulation of the\neffective series resistance governs the extent to which polarization-related responses can be captured\nwithin a given AC measurement window. This principle is independent of the specific chemical\nidentity of the polymer matrix or conductive filler, provided that the system remains non-ionic and\nis read out under comparable electrical conditions.\nTo examine whether the observed impedance-regulated trend is specific to the PI/CNT all-fiber\narchitecture or can be transferred to other platforms, we extended our experimental validation\nbeyond the original system. First, to assess structural transferability, we constructed a PI/CNT\naerogel-based sensor with a fundamentally different porous morphology from the fibrous network\n(Figure R10a and b). Second, to assess material transferability, we implemented the same readout\nconcept in a chemically and morphologically distinct PDMS/rGO hemispherical microstructure\n(Figure R10c and d). In both cases, systematic variation of the dielectric-layer filler content was\nused as a practical handle to modulate the effective series resistance, following the same design logic\nestablished in the primary system.\nAcross these structurally and chemically distinct platforms, we observe consistent qualitative\ntrends in the extracted capacitance response under pressure as the effective series resistance is tuned,\nindicating that the impedance-regulated amplification of the readout signal is not unique to the\nPI/CNT fiber architecture. At the same time, we do not claim that the absolute performance metrics,\noptimal compositions, or detailed microscopic mechanisms are identical across systems.\nWe have revised the manuscript to explicitly limit our claims to a general design principle or\ntransferable readout mechanism, rather than a universally applicable strategy across all materials,\nstructures, and operating conditions.\nCorresponding revisions and additional results have been incorporated into the main text and\nSupplementary Information.\nand microstructures. (a, b) Pressure–capacitance responses of PI/CNT aerogel-based sensors with different\nCNT contents in the dielectric layer, demonstrating the effect of composition-induced impedance modulation\nin a porous architecture distinct from the fibrous network. Insets show optical images of the corresponding\ndevices. (c, d) Pressure–capacitance responses of PDMS/rGO hemispherical microstructured sensors with\ndifferent rGO contents in the dielectric layer, illustrating the same readout trend in a chemically and\nmorphologically distinct non-ionic system. Insets show optical images of the corresponding devices.\n[Manuscript]\nPage 24, Lines 16-24.\nTo further assess applicability beyond the PI/CNT all-fiber system, we extended the\ndemonstration to a structurally distinct PI/CNT aerogel sensor and a chemically distinct\nPolydimethylsiloxane (PDMS)/Reduced graphene oxide (rGO) hemispherical microstructure sensor\n34\n(Supplementary Figure S30). These additional demonstrations illustrate the adaptability of the\nimpedance-regulated readout concept to other non-ionic polymer composite systems with different\nmorphologies and material chemistries50,51. While the absolute performance metrics and\nmicroscopic mechanisms are system-dependent, these results suggest that impedance-regulated\nreadout can serve as a transferable design consideration for sensing interfaces operating under\ndemanding environmental conditions.\n[Supplementary Information]\nFigure S30 | Validation of the impedance-regulated readout concept across different material systems\nand microstructures. (a, b) Pressure–capacitance responses of PI/CNT aerogel-based sensors with different\nCNT contents in the dielectric layer, demonstrating the effect of composition-induced impedance modulation\nin a porous architecture distinct from the fibrous network. Insets show optical images of the corresponding\ndevices. (c, d) Pressure–capacitance responses of PDMS/rGO hemispherical microstructured sensors with\ndifferent rGO contents in the dielectric layer, illustrating the same readout trend in a chemically and\n35\nmorphologically distinct non-ionic system. Insets show optical images of the corresponding devices.\n4. Claim of providing “real-time feedback” is not supported. The manuscript repeatedly uses the\nterm “real-time feedback,” yet the presented system demonstrates only sensing and perception\nvia pressure mapping and CNN-based recognition, without any integrated haptic actuator or\nclosed-loop control scheme. As currently implemented, the work constitutes a sensing-only\nproof-of-concept, and the terminology should be adjusted to avoid implying a closed-loop or\nhaptic feedback–enabled system that is not implemented here.[9, 10]\nResponse: We thank the reviewer for pointing out the imprecise use of the term “real-time feedback.”\nIn the revised manuscript, we have therefore corrected the terminology throughout the text by\nreplacing “real-time feedback” with the more accurate expression “real-time signal response.” This\nrevision clarifies that the present work focuses on the real-time acquisition and stability of\ncapacitance signals during pressure sensing and perception tasks, without implying the\nimplementation of a closed-loop control scheme, actuator integration, or haptic feedback system.\nCorresponding modifications have been made in the main text to avoid ambiguity\n[Manuscript]\nPage 18, Lines 13-14.\nThis design allows the glove to provide real-time signal response on grasping pressure and\ncontact conditions while preserving natural motion under both high and low temperatures.\nPage 21, Lines 7-8.\nThe system consists of signal acquisition, data preprocessing, and deep-learning recognition\nmodules, operating in conjunction with the tactile-assistive glove to provide real-time signal\nresponse on grasping forces and object identification across a wide temperature range.\n36\n5. Insufficient statistical reporting of pressure mapping patterns. Quantitative statistical metrics\nshould be provided to supplement Fig. 5(b), in order to demonstrate that the pressure-mapping\npatterns of identical objects are consistent across repeated trials. In addition, a 2D spatio-\nfrequency analysis would allow direct comparison of the spatial–frequency characteristics\namong different objects. For Fig. S16, the applied pressure should be controlled and kept\nconsistent across all comparative groups; however, the current data suggest noticeable variation.\nFurthermore, the reference sensors used for benchmarking differ in geometry and electrode\nlayout, which undermines the validity of the comparisons. These factors collectively weaken the\nconclusions regarding recognition accuracy. For instance, both the Traditional and Impedance\ntypes exhibit poorer spatial resolution at 25 °C yet appear to perform better at 200 °C—an\noutcome that contradicts physical intuition and calls for additional verification. Incorporating a\nground-truth reference measurement across all three sensing modalities would substantially\nstrengthen the reliability of the comparison.\nResponse: We thank the reviewer for the careful comments regarding the statistical reporting,\nloading consistency, and interpretation of the pressure-mapping and learning-based demonstrations.\nIn the revised manuscript, we have supplemented the pressure-mapping demonstrations with\nadditional statistics to better characterize trial-to-trial signal consistency.\nThe results shown in Fig. 5b were obtained from a total of 30 repeated grasping trials, including\n10 trials at each temperature (−70 °C, 25 °C, and 200 °C), following the same grasping protocol. As\na quantitative indicator of repeatability under these conditions, we analyzed a representative thumb-\ntip sensor channel, which provides the dominant contact during spherical object grasping.\nSpecifically, we computed the Pearson correlation coefficient (PCC) between the time-resolved\ncapacitance signal of this channel and the grasping force estimated from the multi-channel sensor\noutputs. As shown in Figure R11a-c, the impedance-type sensor exhibits a relatively stable PCC\ncentered around ~0.8 during the holding stage across temperatures, whereas the traditional and\niontronic sensors show lower and more scattered correlations. These results indicate more consistent\ntemporal coupling between the impedance-type sensor signal and the applied grasping load during\nrepeated trials, while we note that this analysis is limited to a representative channel and does not\n37\nquantify full spatial pattern repeatability.\nTo assess whether variations in applied loading could account for differences in the pressure-\nmapping results, we further analyzed the dispersion of the estimated grasping force across the 30\ntrials. As shown in Figure R11d, the grasping pressure remains within a comparable range (on the\norder of 200–300 kPa) across trials and temperatures, without systematic drift between conditions.\nThis analysis supports that the mapping demonstrations were conducted under broadly comparable\nloading conditions. We note that the applied pressure values are inferred from the sensor signals and\ntherefore do not constitute an independent external ground-truth measurement.\nFigure R11 | For spherical object grasping, the Pearson correlation coefficient between the grasping force\nvalue measured by the thumb fingertip sensor and the capacitance signal is shown for the impedance type (a),\ntraditional type (b), and iontronic type (c). (d) Statistical analysis of 30 grasping force signals from three\ntypes of sensors.\nWe also clarify the visualization protocol used for the pressure maps. In Figure 5 and Figure\nS16, a unified color scale referenced to the high-temperature signal range was used to enable cross-\ntemperature comparison of signal amplitude. As illustrated in Figure R12, this global scaling affects\nvisual contrast at lower temperatures and may give the impression of reduced spatial resolution if\nnot explicitly stated. We have therefore clarified in the revised manuscript the distinction between\nglobal color scaling for amplitude comparison and per-temperature visualization for examining\n38\nspatial distribution patterns.\nImportantly, the pressure-mapping and CNN-based demonstrations are intended as qualitative,\nproof-of-concept examples illustrating how temperature-stable and low-drift sensor signals support\ndownstream perception tasks, rather than as a rigorous quantitative benchmark of spatial resolution,\nspatial–frequency characteristics, or object-recognition accuracy across different sensing modalities.\nWhile the added statistical analysis supports temporal signal consistency across repeated trials,\nthe present data do not establish quantitative spatial pattern fidelity or comparative recognition\nperformance. A fully controlled benchmarking study requiring identical sensor geometries,\nindependently measured ground-truth pressure distributions, and formal two-dimensional spatial or\nspatio-frequency analysis would be necessary to support such conclusions and is beyond the scope\nof the present work.\nThe manuscript has been revised accordingly to clarify the scope and to avoid\noverinterpretation of the mapping and learning-based results.\nunder various temperatures. Color-mapped pressure distributions (𝛥𝐶/𝐶 ) collected from impedance-type,\n0\ntraditional non-ionic, and iontronic-type flexible pressure sensors during grasping of four objects including\nsphere, cube, cylinder, and triangular prism at −70 °C, 25 °C, and 200 °C.\n[Manuscript]\nPage 21, Lines 21-27 and Page 22, Lines 1-2.\nIn total, 300 pressure-distribution maps were extracted from 30 repeated grasping trials (10\ntrials at each temperature) during the stable holding stage. To assess trial-to-trial consistency, we\ntake sphere grasping as a representative case and compute the Pearson correlation coefficient (PCC)\nbetween the thumb-tip capacitance signal and a grasping-load proxy estimated from the multi-\nchannel sensor array outputs during the holding stage49. The trial-to-trial variation of the estimated\ngrasping force was within 60 kPa for all three sensor types, and the impedance-driven sensor\nexhibited a PCC of ~0.8, indicating strong and reproducible force–signal coupling (Supplementary\nFigure S28).\nPage 22, Lines 3-5.\nUnless otherwise noted, all pressure-distribution maps are displayed using a unified legend\nreferenced to the high-temperature signal range for cross-temperature comparison.\n[Supplementary Information]\ngrasping, the Pearson correlation coefficient between the grasping force value measured by the thumb\nfingertip sensor and the capacitance signal is shown for the impedance type (b), traditional type (c), and\niontronic type (d).\nNote: The Pearson correlation coefficient (PCC) is used to quantify the linear relationship between\ntwo variables and is calculated as follows:\n∑𝑁 (𝑆(𝑡)−𝑆̅(𝑡))(𝐹 −𝐹̅)\n𝑃𝐶𝐶(𝑡)= 𝑖=1 𝑖 𝑖\n√∑𝑁 (𝑆(𝑡)−𝑆̅(𝑡))2√∑𝑁 (𝐹 −𝐹̅)2\n𝑖=1 𝑖 𝑖=1 𝑖\nwhere 𝑃𝐶𝐶(𝑡) denotes the Pearson correlation coefficient, 𝑁 is the number of samples, 𝑆 represents\nthe capacitance signal, and 𝐹 represents the applied force.\nunder various temperatures. Color-mapped pressure distributions (𝛥𝐶/𝐶 ) collected from impedance-type,\n0\ntraditional non-ionic, and iontronic-type flexible pressure sensors during grasping of four objects including\nsphere, cube, cylinder, and triangular prism at −70 °C, 25 °C, and 200 °C.\nThe impedance-type sensor maintains strong and continuous signal feedback across all\ntemperature conditions, whereas the traditional and iontronic sensors show weakened or distorted\nresponses at low and high temperatures, respectively, demonstrating the superior temperature-\nindependent sensing performance of the impedance-driven architecture.\n6. Deep learning demonstration lacks rigor and controlled comparison. The dataset size is limited\n(300 samples), no cross-validation or train/test robustness analysis is performed, and key\ndetails—such as signal preprocessing, data normalization, construction of the 52×52 input arrays,\nor justification for using a 1D rather than 3D CNN architecture—are missing. The t-SNE plots\nindicate linearly separable clusters even for the Traditional type, yet the classification accuracy\n42\nfor that case drops to ~80% without sufficient explanation. Overall, the DL demonstration would\nbenefit from clearer methodological transparency, controlled comparison, and more rigorous\nevaluation.[11]\nResponse: We thank the reviewer for the comments on the rigor and transparency of the deep-\nlearning–assisted demonstration. In the revised manuscript, we have clarified the data-processing\npipeline and the learning workflow. As shown in Figure R13, the raw multi-channel signals\ncollected using the LCR meter are first converted into normalized relative responses (ΔC/C₀) to\nreduce baseline offsets and temperature-related drift. The signals are then resized to a unified\ndimension, and data augmentation is applied to ensure consistent input scale across different sensing\nmodalities. Statistical descriptors, including mean, variance, and standard deviation, are extracted to\ncharacterize the steady-state response level and signal fluctuations. These features are arranged\naccording to the sensor array geometry and interpolated to a unified 52 × 52 grid, which is used as\nthe network input. The same preprocessing steps are applied to the iontronic, traditional capacitive,\nand impedance-driven sensors.\nFigure R13 | Schematic of the data preprocessing, network input construction, and evaluation procedure used\nin the deep-learning analysis.\nFor evaluation, we updated the analysis to use K-fold cross-validation instead of a single\ntrain/test split. The confusion matrices in the revised figures are obtained by aggregating predictions\nacross all folds (Figure R14), and the reported accuracies therefore represent averaged performance\nunder repeated training and testing. Under this evaluation protocol, differences in classification\n43\noutcomes among the three sensing mechanisms remain observable, while we do not treat these\nresults as a quantitative performance benchmark.\nWe also clarify the role of the t-SNE visualizations. As shown in Figure R14, t-SNE is used\nonly to visualize local clustering tendencies after dimensionality reduction and does not directly\nreflect class separability in the original feature space. For the traditional capacitive sensor, partial\nseparation appears in the t-SNE plots, but the corresponding confusion matrix indicates substantial\nmisclassification under cross-validation. This discrepancy reflects intra-class variability in the\noriginal feature space rather than robust separability.\nThe use of a lightweight convolutional neural network is motivated by the dataset size. With\nthe present dataset (300 samples), more complex network architectures were found to introduce\noverfitting without providing stable improvements under cross-validation. A simple CNN was\ntherefore adopted to limit the influence of model complexity and to keep the comparison focused on\ndifferences in signal quality and stability among the sensing mechanisms. No architecture-level\ncomparison is intended.\nFinally, the deep-learning analysis is intended as a proof-of-concept demonstration to illustrate\nhow signal stability affects downstream perception tasks, rather than as a comprehensive machine-\nlearning benchmark. The manuscript has been revised accordingly to clarify this scope and to\nprovide sufficient methodological detail for transparent interpretation.\nand impedance-driven sensors obtained under identical training and evaluation conditions.\n[Manuscript]\nPage 21, Lines 9-15.\nHere, machine learning is used to analyze the pressure-mapping signals captured by our sensor\nand evaluate the separability of different object patterns47,48. Supplementary Figure S27 presents\na simplified workflow of the learning process. Briefly, the raw capacitance signals were converted\nto normalized relative responses (ΔC/C₀), and statistical features (mean and variance) were extracted\nand reorganized according to the spatial layout of the sensing channels to form 2D input maps. The\nresulting feature maps were interpolated to a unified 52×52 grid and fed into a 2D-CNN consisting\nof two convolution–pooling blocks.\nPage 22, Lines 6-10.\nMeanwhile, to improve evaluation robustness, we adopted K-fold cross-validation by dividing\nthe augmented dataset into 6 subsets, using one subset as the non-overlapping test set and the\nremaining subsets as the training set. Data augmentation was applied to the training folds only to\navoid data leakage. This procedure was repeated across 5 folds and the averaged accuracy was\nreported (Figure 5c-e).\n[Supplementary Information]\nin the deep-learning analysis.\nAdditional minor comments\n1) Recent high-performance capacitive and iontronic sensors already achieve wide pressure ranges\nand high sensitivities through graded, porous, or impedance-engineered architectures.[4,6–8]\nWhile the wide temperature window and impedance-related mechanism explored here provide\nmeaningful novelty, a more balanced and up-to-date discussion of these recent advances would\nhelp contextualize the contribution and avoid overstating the level of performance innovation.\nResponse: We thank the reviewer for this suggestion. In the revised manuscript, we have expanded\nthe Introduction and Discussion to include recent representative studies on graded architectures,\nporous designs, and impedance-engineered capacitive and iontronic sensors, and clarified their\nrespective roles in achieving wide pressure ranges and high sensitivities. At the same time, we have\nalso revised the wording to avoid overstating performance-related advances and to clarify that the\nmain contribution of this study lies in the impedance-driven polarization release mechanism in a\nnon-ionic system and its temperature-invariant operation over an ultra-wide temperature range.\n[Manuscript]\nNotably, recent capacitive and iontronic sensors have already achieved wide pressure ranges\nand high sensitivities through graded dielectric designs, hierarchical porous architectures, and\nimpedance-engineered interfaces, highlighting the effectiveness of structural design in boosting\nsignal output8-11.\n2) Supplementary Fig. S13 reports sub-10 ms response and recovery times; however, there is no\nsystematic investigation of loading-rate dependence, dwell-time effects, or rate-dependent\nhysteresis. These characteristics are particularly important for impedance-driven mechanisms\nand are routinely quantified in recent iontronic sensor literature. [4,6–8]\nResponse: We thank the reviewer for the comment regarding rate-dependent behavior and long-\nterm stability. In the revised manuscript, we have therefore added two complementary experiments\nto address loading-rate dependence and dwell-time stability.\nThe capacitance response was first measured under cyclic compression at loading rates ranging\nfrom 10 to 100 mm min⁻¹, while maintaining the same peak pressure level of 500 kPa. As shown in\nFigure R15a, within this tested rate range, the capacitance signal evolves synchronously with the\napplied pressure profile. No obvious signal distortion or instability is observed as the loading rate\nincreases, indicating that the impedance-based readout can follow dynamic loading events over the\nexamined range of compression speeds.\nTo probe signal behavior under sustained loading, the sensor was subsequently held at a\nconstant pressure of 2000 kPa for 1 h. As shown in Figure R15b, the normalized capacitance signal\nremains stable throughout the holding period, without abrupt degradation or loss of signal continuity.\nThis result indicates that, on the tested time scale, the sensor output remains steady under prolonged\nstatic pressure.\nThese additional measurements focus on operational rate tolerance and short-term stability\nunder representative loading conditions. The manuscript has been revised accordingly to reflect the\n47\nscope of these measurements.\nFigure R15 | (a) Capacitance response under compression at different loading rates ranging from 10 to 100\nmm min⁻¹. (b) Capacitance response under a constant pressure of 2000 kPa held for 1 h.\n[Manuscript]\nPage 13, Lines 22-25.\nTo complement the sub-10 ms response/recovery results, we further evaluated loading-rate\ndependence (10–100 mm min⁻¹) and dwell-time stability at 2000 kPa for 1 h. The sensor maintained\nconsistent robust signal tracking with minimal drift over the holding period (Supplementary Fig.\nS22).\n[Supplementary Information]\nFigure S22 | (a) Capacitance response under compression at different loading rates ranging from 10 to 100\n48\nmm min⁻¹. (b) Capacitance response under a constant pressure of 2000 kPa held for 1 h.\n3) Although Fig. 3 and Supplementary Fig. S15 demonstrate functional operation from −80 to\n200 °C, the study does not include repeated thermal cycling or long-term testing across extreme\ntransitions. Temperature-dependent analyses of Rs(T,ω), tan δ(T,ω), Arrhenius-type activation\nbehavior, and high-temperature durability would significantly strengthen claims of thermal\nrobustness, as these analyses are standard in high-temperature iontronic sensor studies.[6–8]\nResponse: We thank the reviewer for the comment regarding long-term stability and thermal\nrobustness under extreme temperature conditions. To address this point, we have added\ncomplementary experiments at −80 °C, 25 °C, and 200 °C to evaluate both long-term holding\nstability and repeatability under thermal cycling.\nTo evaluate signal stability at extreme temperatures, the sensor was held at a constant pressure\nof 2000 kPa for 1 hour at −80 °C, 25 °C, and 200 °C. As shown in Figure R16a, the normalized\ncapacitance signal remains stable throughout the holding period at all three temperatures, without\nabrupt degradation or loss of signal continuity. This result indicates that the impedance-based\nreadout maintains steady operation under sustained high pressure across the tested temperature range.\nTo examine repeatability during temperature transitions, repeated loading–unloading tests at\nmultiple pressure levels were performed while cycling the temperature between −80 °C, 25 °C, and\n200 °C. As shown in Figure R16b, the capacitance responses at each pressure level are reproducible\nacross successive temperature cycles, indicating consistent pressure-dependent behavior during the\ntested thermal transitions.\nWe further examined the temperature dependence of the capacitance response in the Arrhenius\nrepresentation. As shown in Figure R16c, no clear Arrhenius-type linear relationship is observed\nover the investigated temperature range. This behavior is consistent with a non-ionic, electronically\ndominated sensing mechanism, in which polarization-related responses and impedance-regulated\nreadout do not follow thermally activated ionic transport.\nrecorded simultaneously. As shown in Figure R16d and e, both parameters evolve systematically\nwith applied pressure and temperature and remain comparable across repeated temperature cycles,\nindicating that the impedance readout conditions are preserved during the tested thermal transitions.\nThese additional results have been included in the revised manuscript to better characterize the\nsensor performance under long-term loading and repeated temperature cycling.\nFigure R16 | (a) Capacitance response under a constant pressure of 2000 kPa held for 1 hour at −80 °C,\n25 °C, and 200 °C. (b) Repeated loading–unloading responses at multiple pressure levels during successive\ntemperature cycles. (c) The Arrhenius representation of the capacitance response as a function of temperature.\n(d) Corresponding evolution of the loss tangent (tan δ). (e) Corresponding evolution of the equivalent series\nresistance (Rs).\n[Manuscript]\nPage 16, Lines 10-16.\nTo further verify robustness against extreme temperature transitions, we performed repeated\nthermal cycling among −80 °C, 25 °C, and 200 °C, together with multi-level loading–unloading\n50\ntests from 100 to 2000 kPa. As shown in Figure 3c and Supplementary Figure S26, Δ𝐶/𝐶 , 𝑅 ,\n0 𝑠\nand tanδ remain highly repeatable throughout successive temperature cycles. Together with the long-\nterm loading and hysteresis characterization, these results confirm stable and reliable sensing under\nrepeated thermal transitions.\n[Supplementary Information]\nFigure S26 | (a) Capacitance response under a constant pressure of 2000 kPa held for 1 h at −80 °C, 25 °C,\nand 200 °C. (b) Corresponding evolution of the loss tangent (tan δ). (c) Corresponding evolution of the\nequivalent series resistance (Rs).\n4) SEM and KPFM results offer useful qualitative insights, but additional microstructure-resolved\nevidence—such as in-situ micro-CT, conductive-AFM, or local impedance mapping—would\nmore convincingly validate the proposed percolation and impedance-release mechanisms.[4,6–\n8] Moreover, only a single KPFM region is shown; statistical distributions, multiple\nmeasurement sites, or correlations between surface potential and polarization would improve the\nrobustness of the interpretation.\nResponse: We thank the reviewer for the constructive comments regarding microstructure-resolved\nvalidation of the proposed percolation and impedance-related mechanisms. In the revised\nmanuscript, we have therefore clarified the role of the KPFM measurements and added additional\nrepresentative data to support a network-level interpretation.\nAs shown in Figure R17a and b, KPFM mapping performed on individual fibers shows a\nrelatively uniform surface potential along the measured fiber segments, without pronounced\nlocalized extrema. This observation suggests that the electrical response is distributed along the\nfibrous network rather than being dominated by isolated surface features within a single fiber.\nTo improve spatial representativeness, KPFM measurements were further conducted at fiber\njunctions across multiple representative regions (Figure R17c and d). These junction areas\nconsistently exhibit potential contrast between contacting fibers, indicating reproducible electrical\nheterogeneity associated with fiber–fiber contacts. Such junction-associated contrast is consistent\nwith contact-dependent variations in local electrical behavior within the fibrous network.\nIn addition, conductive-AFM measurements were performed on the dielectric fibrous layer\n(Figure R17e–h). The C-AFM current maps reveal detectable current signals at overlapping fiber\njunctions, indicating that fiber–fiber contacts can locally support charge transport. These\nobservations are consistent with the notion that compression-induced changes in fiber contact and\nconnectivity may affect the effective electrical pathways within the fibrous network.\nTaken together with the macroscopic impedance measurements, the KPFM and C-AFM results\nprovide microstructure-resolved observations that are consistent with the network-level\ninterpretation presented in the main text, without implying a quantitative or fully resolved validation\nof the percolation or impedance-release mechanisms. The manuscript has been revised accordingly\nto reflect the role of these nanoscale observations within the overall experimental framework.\ntopography of the single fiber. (c) Surface potential map at a fiber junction. (d) Corresponding surface\ntopography at the fiber junction. (e) Surface current map at a fiber junction by C-AFM. (f) Corresponding\nsurface topography at the fiber junction. (g) Surface current map at another fiber junction by C-AFM. (h)\nCorresponding surface topography at the fiber junction.\n[Manuscript]\nPage 8, Lines 20-22.\nConsistently, additional multi-site KPFM mappings and conductive-AFM measurements\nfurther support junction-enabled conduction within the dielectric network under compression\n(Supplementary Fig. S9).\n[Supplementary Information]\ntopography of the single fiber. (c) Surface potential map at a fiber junction. (f) Corresponding surface\ntopography at the fiber junction. (e) Surface current map at a fiber junction by C-AFM. (d) Corresponding\nsurface topography at the fiber junction. (g) Surface current map at another fiber junction by C-AFM. (h)\nCorresponding surface topography at the fiber junction.\n5) Porous dielectric structures generally exhibit compression–release hysteresis, but aside from\nlong-cycle stability, no loading–unloading curves are presented. Quantifying hysteresis is\nessential for evaluating practical usability and mechanism consistency.\nResponse: We thank the reviewer for highlighting the importance of loading–unloading hysteresis\nin porous dielectric structures. In the revised manuscript, we have therefore added representative\nloading–unloading compressive stress–compressive stress–strain curves in addition to the long-cycle electrical stability\ndata.\nAs shown in Figure R18, the porous fibrous network exhibits a clear hysteresis between the\nloading and unloading paths, which is characteristic of highly porous dielectric structures and\nreflects contact rearrangement and frictional energy dissipation during compression. Importantly,\nthe stress–strain responses obtained from successive loading–unloading cycles (1st–3rd) show\nsimilar profiles over the tested pressure range up to 8000 kPa, indicating that the hysteresis behavior\nis repeatable across cycles under the applied loading conditions.\nthat the porous structure exhibits stable and reproducible compression–release behavior, despite the\npresence of hysteresis. The corresponding loading–unloading curves have been included in the\nrevised manuscript and Supplementary Information.\n1st 2nd 3rd\n8000\n) \na\nP a) \nk P\ns \n( 6000 s ( k40\ns s\nr\ne str e\ne s t 4000 si v e 20\nv e s\nsi pr\ns m\ne o 0\nr 2000 C\np\nm 0 25 50\no Compressive strain (kPa)\nC\n0\n0 30 60 90\nCompressive strain (kPa)\nFigure R18 | Repeated compressive stress–strain curves of the sensor structure measured up to 8000 kPa\nover multiple loading–unloading cycles.\n[Manuscript]\nPage 13, Lines 17-20.\nIn addition, representative loading–unloading curves were measured to quantify hysteresis. The\nporous fiber network exhibits a stable and reproducible hysteresis loop with closely overlapped\nresponses over successive cycles Within the sensing range of 8MPa (Supplementary Figure S20).\n[Supplementary Information]\n55\n1st 2nd 3rd\n8000\n) \na\nP a) \nk P\ns ( 6000 s ( k40\ns s\ne st\nr\ne\n4000 si v e\nstr e\n20\nv e s\nsi pr\ns m\nr e 2000 C o 0\np\nm 0 25 50\no Compressive strain (kPa)\nC\n0\n0 30 60 90\nCompressive strain (kPa)\nFigure S20 | Repeated compressive stress–strain curves of the sensor structure measured up to 8000 kPa over\nmultiple loading–unloading cycles.\n6) The reported porosity (~90%) lacks methodological detail. It is unclear whether it was\ndetermined via gravimetry, image-based analysis, mercury porosimetry, nitrogen adsorption, or\nanother technique. Explicitly stating the measurement method is necessary for reproducibility.\nResponse: We thank the reviewer for pointing out the lack of methodological clarity regarding the\nporosity estimation. In the revised manuscript, we have explicitly clarified that the reported porosity\n(~90%) was estimated using a density-based method, calculated from the measured apparent density\nof the porous structure and the theoretical density of the polymer matrix. The corresponding\nmeasurement and calculation procedures have been added to the Methods and Supplementary\nInformation to ensure reproducibility.\n[Manuscript]\nPage 7, Line 24.\nThese layers were fabricated through an airflow-spinning process under identical processing\nconditions, followed by thermal imidization of poly(amic acid) into polyimide (PI) with excellent\n56\ntemperature resistance (Supplementary Figures S3 and S4), with a structural porosity of up to 90%,\nestimated using a density-based method.\nPage 28, Lines 17-22.\nPorosity of the sensor: Porosity was estimated using a density-based method. The apparent\ndensity of the porous sample (𝜌 ) was calculated from the measured mass 𝑚and geometric volume\napp\n𝑉. The theoretical density of the solid matrix (𝜌 ) was taken as 1.47 g/cm3. The porosity 𝑃 was then\n0\ncalculated as:\n𝜌\napp\n𝑃 = (1− )×100%.\n𝜌\n7) Performance data appear to be based on single devices without error bars or batch-to-batch\nstatistics. Given the stochastic nature of fiber spinning, CNT distribution, and gradient formation,\nreproducibility is essential to assess manufacturing robustness. Additionally, several parameters\ncritical to reproducibility—such as airflow nozzle size, air temperature, humidity, and fiber\ndiameter distribution—are not reported.\nResponse: We thank the reviewer for emphasizing the importance of reproducibility and\nmanufacturing robustness. In the revised manuscript, we have therefore added batch-to-batch\nreproducibility results from three independently fabricated batches prepared under identical\nprocessing conditions. As shown in Figure R19, all batches exhibit consistent ΔC/C₀–pressure\nresponses over the full pressure range up to 8000 kPa. The extracted high-pressure sensitivity (S₂)\nvalues are 169.84, 158.29, and 176.51 kPa⁻¹ for Batch 1–3, respectively, with an average value of\n168.21 kPa⁻¹ and a batch-to-batch deviation below 6%.\nIn addition, we have expanded the Methods section to explicitly report key fabrication\nparameters relevant to reproducibility, including the airflow nozzle size, gas temperature and\npressure, the humidity range during spinning, and the representative fiber diameter distribution.\nand Supplementary Information.\n1.5×106\nBatch 1 Batch 2 Batch 3\n1.0×106\nS = 169.84 kPa-1\n/\nC S = 176.51 kPa-1\nΔ 2\n5.0×105\nS = 168.21 kPa-1\naverage\nError less than 6%\n0.0\n0 2000 4000 6000 8000\nPressure (kPa)\nFigure R19 | Batch-to-batch reproducibility of the sensor performance, showing ΔC/C₀ as a function of\npressure for three independently fabricated batches and the corresponding extracted sensitivities.\n[Manuscript]\nPage 12, Lines 11-13.\nMoreover, the sensor response is highly reproducible across three independently fabricated\nbatches (<6% variation in sensitivity; Supplementary Figure S14).\n[Supplementary Information]\n58\n1.5×106\nBatch 1 Batch 2 Batch 3\n1.0×106\nS = 169.84 kPa-1\n/\nC S = 176.51 kPa-1\nΔ 2\n5.0×105\nS = 168.21 kPa-1\naverage\nError less than 6%\n0.0\n0 2000 4000 6000 8000\nPressure (kPa)\nFigure S14 | Batch-to-batch reproducibility of the sensor performance, showing ΔC/C₀ as a function of\npressure for three independently fabricated batches and the corresponding extracted sensitivities.\nReviewer #1:\nComment: The revised manuscript is now acceptable for publication. Thank you.\nResponse: We sincerely thank the reviewer for the careful evaluation of our revised manuscript and\nfor the positive recommendation for publication. We greatly appreciate the reviewer’s constructive\ncomments throughout the review process, which have significantly improved the quality and clarity\nof this work.\nReviewer #2:\nComment: The revised manuscript shows an improve quality by reflecting all comments from\nreviewers, so should be ready to be accepted.\nResponse: We are grateful to the reviewer for the thorough assessment and for recognizing the\nimprovements made in response to the comments. We sincerely appreciate the valuable feedback\nprovided during the review process, which has helped strengthen the manuscript.\n1"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The revised manuscript is now acceptable for publication.\nThank you.", "reviewer_2": "The revised manuscript shows an improve quality by reflecting all comments from reviewers, so should be ready to be accepted."}, "author_response": "Response to the Reviewers\nReviewer #1:\nComment: The revised manuscript is now acceptable for publication. Thank you.\nResponse: We sincerely thank the reviewer for the careful evaluation of our revised manuscript and\nfor the positive recommendation for publication. We greatly appreciate the reviewer’s constructive\ncomments throughout the review process, which have significantly improved the quality and clarity\nof this work.\nReviewer #2:\nComment: The revised manuscript shows an improve quality by reflecting all comments from\nreviewers, so should be ready to be accepted.\nResponse: We are grateful to the reviewer for the thorough assessment and for recognizing the\nimprovements made in response to the comments. We sincerely appreciate the valuable feedback\nprovided during the review process, which has helped strengthen the manuscript.\n1"}]

Decision letter