<figure><img src="image_1.png" /></figure>

HAL
open science

# Nonlinear Conductive Graphene Composites for Pressure Sensing with a Linear Response and Voltage-Driven Thermal Correction

Feng Luo, Artur Ciesielski, Paolo Samorì

► To cite this version:

Feng Luo, Artur Ciesielski, Paolo Samorì. Nonlinear Conductive Graphene Composites for Pressure Sensing with a Linear Response and Voltage-Driven Thermal Correction. Advanced Materials, 2025, 37 (32), pp.2503867. 10.1002/adma.202503867. hal-05213136

HAL Id: hal-05213136

https://hal.science/hal-05213136v1

Submitted on 18 Aug 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_3.png" /></figure>

# Nonlinear Conductive Graphene Composites for Pressure Sensing with a Linear Response and Voltage-Driven Thermal Correction

Feng Luo, Artur Ciesielski,* and Paolo Samori*

Thermal fluctuations pose a significant challenge to the signal stability of nanomaterial-based piezoresistive pressure sensors, limiting their effectiveness in applications such as electronic skin and robotics. Conventional temperature compensation strategies often rely on additional thermal sensors or complex calibration algorithms. Here, a flexible pressure sensor is reported featuring a nonlinear conductive graphene composite layer within a bilayer architecture, enabling bias voltage-controlled sensitivity without structural redesign. The sensor achieves ultra-high sensitivity ($742.3 \text{ kPa}^{-1}$), a broad linear sensing range of up to $800 \text{ kPa}$ ($R^2 = 0.99913$), and excellent long-term durability over 10 000 cycles. Crucially, the unique nonlinear characteristics enable the bias voltage to function as an internal remote control for correcting temperature drifts between 25 and 60 °C, as demonstrated by precise manipulation in robotic grippers under varying temperature conditions. This work offers a universal strategy for building environmentally adaptive sensors, advancing the development of robust and high-precision wearable electronics.

## 1. Introduction

Wearable pressure sensors demonstrated disruptive potential for applications in human-machine interfaces,[1] robotic systems,[2] and healthcare[3] due to their high sensitivity in the required detection range. However, combining high sensitivity with a linear response throughout the entire sensing range remains a major challenge yet it is highly sought after to enable multi-modal applications.[4] While mechano-responsive design principles combined with morphology engineering techniques can enhance sensitivity, they often lead to a limited sensing range

and nonlinear response when low Young's modulus and micro-structured materials are used.[4b-d,5] Conversely, electrical design strategies employing conductivity modulation in multi-layered structures offer a promising solution for achieving a wide linear sensing range.[6] Post-fabrication tuning of the sensitivity cannot be exploited in conventional flexible pressure sensors, requiring structural or material redesigns to meet specific performance criteria,[7] thereby limiting their practical applications. Typically, pressure-sensitive nanomaterials exhibit high and stable conductivity, where charge transport follows Ohmic behavior, characterized by a linear current-voltage (I-V) relationship.[8] However, inhomogeneous composite materials may display significantly nonlinear I-V characteristics.[9] Since the conductivity of these composite materials depends on the applied bias voltage, achieving a controlled nonlinear conductivity at low operating voltages (<10 V) is challenging but crucial for applications in low-power wearable electronics. Therefore, incorporating nonlinear conductive materials into pressure sensors could enable tunable performance through bias voltage adjustment, eliminating the need for additional redesign efforts.

Temperature variations can lead to sensitivity drift in pressure sensors, significantly reducing their accuracy, as the active nanomaterial's conductivity is sensitive to temperature changes.[1b,10] Integrating hardware compensation strategies,[11] such as temperature sensors and circuits, and software solutions[12] through the implementation of calibration models and algorithms, can effectively address signal drifts in pressure sensors caused by thermal fluctuations. However, these external compensation and re-calibration approaches inherently increase both the complexity and cost of the sensors or require precise calibration data. Therefore, exploring the influence of sensor operational parameters on the device performance holds promise as a novel strategy to correct for thermal fluctuations. This approach could pave the way for the development of pressure sensors with inherent thermal correction capability by exploring the interplay between sensor performance, temperature, and additional parameters.

Herein, we present a flexible pressure sensor with voltage-driven thermal correction capability, achieved by integrating a nonlinear conductive graphene composite (NcGc) layer and laser-reduced graphene oxide (LrGO) conductive layer in a bilayer

F. Luo, A. Ciesielski, P. Samori
Université de Strasbourg
CNRS
ISIS UMR7006
8 Allée Gaspard Monge, Strasbourg F-67000, France
E-mail: ciesielski@unistra.fr; samori@unistra.fr

The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/adma.202503867

© 2025 The Author(s). Advanced Materials published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

DOI: 10.1002/adma.202503867
structure. The bottom NcGc layer is carefully engineered by blending highly resistive chemically modified graphene oxide (CmGO) with conductive LrGO at a critical volume fraction (22%) near the percolation threshold ($\phi_c$ = 23.5%), to introduce distinct nonlinear conduction behavior. This bilayer design, characterized by a pronounced conductivity contrast between layers, enables the sensor to achieve ultra-high sensitivity of 742.3 kPa⁻¹, a broad linear sensing range up to 800 kPa ($R^2$ = 0.99913), and excellent mechanical durability over 10 000 cycles at 210 kPa. Crucially, the nonlinear I–V response of the NcGc layer allows for bias voltage-dependent sensitivity modulation without requiring structural changes. As a result, the bias voltage functions as an internal tuning parameter that actively (through manual voltage changes) compensates for temperature-induced sensitivity drift, guaranteeing reliable operation across temperatures from 25 to 60 °C. This work establishes a novel device strategy for realizing bias-tunable sensing and inherent thermal compensation in pressure sensors, offering a generalizable pathway toward high-precision, environmentally adaptive wearable electronics for electronic skin and robotic applications.

## 2. Results and Discussion

### 2.1. Nonlinear Conductive Graphene Composite Layer for Pressure Sensor

The LrGO was obtained by rapid laser reduction of GO aerogel (Figure 1a; Figure S1, Supporting Information). This reduction process propagated spontaneously throughout the entire bulk GO, completing in 200 ms (Figure S2 and Movie S1, Supporting Information). The CmGO was synthesized via covalent functionalization of GO flakes using 3-aminopropyl)triethoxysilane (APTES) through an epoxy ring-opening reaction (Figure 1a; Figure S3, Supporting Information). In this process, the amine groups (-NH₂) of APTES reacted with the epoxy group on GO, yielding the secondary amines (-NH-). Comparative analyses utilizing X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR) on GO, LrGO, CmGO, and APTES (Figures S4–S7, Supporting Information) confirmed the reduction of GO and the incorporation of the secondary amine group. XRD data indicated an increase in interlayer spacing following the chemical functionalization (Figure S6, Supporting Information). The FTIR spectra displayed characteristic amine absorption bands within the 3150–3500 cm⁻¹ range. In the case of APTES, two distinct peaks were observed, corresponding to the -NH₂ group, while CmGO showed a single peak attributed to the -NH- group (Figure S7, Supporting Information). More detailed discussions on material characterizations of LrGO and CmGO are presented in Note S5 (Supporting Information).

The CmGO and LrGO dispersions with equal concentrations were combined at different volume ratios of LrGO. The NcGc layer was assembled with a 22% LrGO volume ratio using vacuum filtration and transfer printing techniques. This NcGc layer formed a percolation network, enabling electron transport along the conductive LrGO flakes (Figure 1b). The resistivity measurements revealed that the LrGO and NcGc layers exhibited quantifiable resistivity, whereas CmGO proved too insulating for pre-

cise measurement. The resistivity of the NcGc layer resulted 1.8 × 10⁶ times greater than that of the LrGO layer (Figure 1c). Furthermore, the NcGc layer displayed a pronounced nonlinear I–V behavior, whereas the LrGO layer exhibited a linear I–V response (Figure 1d).

The pressure sensors were fabricated by assembling an NcGc layer with a pre-assembled LrGO film supported onto flexible polyethylene terephthalate (PET) substrates. Due to their ultrathin architecture and flexible design, the sensors can conform closely to the human skin, making them suitable for wearable electronics applications (Figure 1e). The pressure response mechanism relies primarily on a gradual shift in the current flow path from the bottom NcGc layer to the top conductive LrGO layer as external force is applied (Figure 1f). This mechanism was experimentally verified by the evolution of the I–V curves under increasing pressure: the sensor exhibited a highly nonlinear I–V response in its unpressurized state but transitioned to a linear behavior with increased current, consistent with the proposed current path shift from the NcGc to the LrGO layer (Figure 1g).

### 2.2. Percolation Theory and Charge Transfer Mechanism

Graphene composite layers were assembled by mixing LrGO and CmGO dispersions at varying LrGO volume ratios, ranging from 10% to 100%. The mixed dispersion was vacuum-filtrated onto a polytetrafluoroethylene (PTFE) membrane, forming a flat and thin layer. While still wet with ethanol, this layer was flipped over and transferred onto a PET substrate exposing pre-patterned gold-interdigitated electrodes (Au-IDEs). The membrane was then dried using nitrogen flow and carefully peeled from the substrate (Figure 2a). This process achieved a complete transfer of the graphene composite layer, which adhered firmly to the PET substrate through physical interaction (Figures S8 and S9, Supporting Information), showing exceptionally low surface roughness (root mean square roughness, $R_q$ = 68.82 ± 5.97 nm).

The output current of each graphene composite layer was analyzed as a function of LrGO volume fractions (Figure 2b), with data being fitted using the 3D percolation theory[13] (Figure S10 and Note S1, Supporting Information), revealing a percolation threshold at the LrGO ratio of 23.5%. The degree of nonlinearity (DoNL) was calculated from the I–V curves of composite layers, with the highest DoNL (7.96 ± 0.29) occurring at a LrGO ratio of 22%, slightly below the percolation threshold (Figure 2c). I–V curves of composite layers with LrGO ratios of 15%, 22%, and 30% confirmed that the most prominent nonlinear conductivity characteristics appeared near the percolation threshold (Figure S11a, Supporting Information). A schematic illustration of the LrGO flake distribution and the formation of the percolation network demonstrates the enhanced electron transport with increasing LrGO fraction (Figure S11b, Supporting Information). To further assess the thickness effect on DoNL and electrical performance, the NcGc layers were assembled using varying dispersion volumes, and the resulting thicknesses were quantified. Output current at a 1 V bias voltage increased with the dispersion volume (Figure S11c,d, Supporting Information). Notably, the I–V curves revealed that the NcGc layer fabricated with a 75 µL dispersion (469.7 ± 16.2 nm thick) possesses the highest nonlinearity (Figure 2d).
<figure><img src="image_4.png" /><figcaption>**Figure 1.** Nonlinear conductive graphene composites for bias voltage-tunable pressure sensors with inherent capability for correcting thermal fluctuations. a) Schematic illustrating the preparation process of LRGO and the structure of CmGO. b) Composition of the NcGc layer, showing a percolating network formed by embedding LRGO flakes within a CmGO matrix. c) Measured resistivity of LRGO and NcGc layers; CmGO resistivity could not be quantified, being below the detection limits. Data are presented as mean values ± standard deviation across three samples. d) Comparison of I–V characteristics for LRGO and NcGc layers, highlighting the NcGc layer’s nonlinear I–V behavior versus the linear response of the LRGO layer. e) Scheme of the double layer-based sensor encapsulated in-between PET foils. f) Cartoon of the sensor’s working principle, showing how the current flow path shifts from the bottom NcGc layer to the conductive LRGO top layer as an external force is applied. g) I–V curves of the sensor at various applied pressures. The inset highlights the nonlinear I–V curve observed in the absence of applied pressure.</figcaption></figure>

To investigate the underlying charge transfer mechanism within the NcGc layer, temperature-dependent electrical measurements were conducted over a range from 100 to 320 K. The recorded I–V curves showed an increase in current with the rising temperature while retaining their nonlinear characteristics (Figure S12, Supporting Information). The conductivity ratios ($\sigma/\sigma_{300 K \& 1 V}$) decreased with increasing $T^{-1}$ under various bias voltages from 150 to 320 K (Figure 2e), indicating that the temperature-dependent electrical behavior within the NcGc layer is governed by electron hopping rather than electron tunneling.[14] The activation energies ($E_a$) were calculated using the Arrhenius function[15] with data from Figure 2e, revealing an exponential decay in $E_a$ (46.9 meV at 1 V) with the increasing bias voltage (Figure 2f; Note S2, Supporting Information). This suggests that the hopping energy barriers decrease upon increasing the bias voltages, allowing more electrons to participate in the hopping process,[16] which contributes to the observed nonlinear conductivity behavior in the NcGc layer. Moreover, variable-range hopping (VRH) provides a compelling explanation for the charge transfer mechanism in the disordered composite materials system.[9a,17] The experimental data were analyzed using three charge transfer models:[17a,18] nearest-neighbors hopping (NNH), Efros–Shklovskii VRH (ES-VRH), and Mott-VRH. The 3D Mott-VRH model provided the best fit ($R^2$ = 0.9923), showing an inverse relationship between the logarithmic conductivity ratio and $T^{-1/4}$ (Figure 2g; Figure S13a–c, Supporting Information).

This variable-range electron hopping transport mechanism. This variable-range electron hopping transport mechanism is schematically illustrated in Figure S13d (Supporting Information), with electrons mobilizing between conductive localized states (isolated LRGO regions within the NcGc layer).

## 2.3. Sensing Performance of the NcGc-Based Pressure Sensors

The NcGc layer-based pressure sensor was fabricated by superimposing pre-assembled NcGc and LRGO layers, thereby creating a physical contact interface between them (Figure 3a; Figure S14, Supporting Information). Both layers were prepared using identical vacuum filtration and transfer printing techniques, resulting in a highly flat and uniform structure, with surface morphologies closely resembling that of the PTFE membrane (Figure S15, Supporting Information). Once those layers were transferred to the flexible PET substrates, the assembled sensor demonstrated excellent flexibility (Figure S17, Supporting Information), which is attributed to its unique architecture that relies solely on electrical conductivity design without incorporating mechanical microstructures (Table S2, Supporting Information).

The NcGc layer was first optimized by adjusting LRGO fractions to achieve maximum nonlinearity. Building on this, sensor performance was further enhanced by varying the thicknesses of the LRGO and NcGc layers and exploring different device configurations. As discussed in Note S3 (Supporting Information), the
<figure><img src="image_5.png" /><figcaption>**Figure 2.** Investigation of the percolation theory and charge transfer mechanism underlying nonlinear conductivity behavior. a) Schematic illustration of the fabrication process for graphene composite layers on gold IDEs via vacuum filtration and transfer printing. b) Output current of the graphene composite layers with varying LrGO ratios (from 10% to 100%) at a 1 V bias voltage. The dashed line marks the percolation threshold at 23.5%. c) DoNL derived from I–V curves as it varies with different LrGO ratios. d) I–V curves of the NcGc layer fabricated with varying dispersion volumes. e) Conductivity ratio as a function of $T^{-1}$ under different bias voltage across a temperature range of 100 to 320 K. f) $E_a$ determined from the Arrhenius equation using the data (150–320 K) presented in (e), plotted as a function of bias voltage. The red line represents an inverse exponential fit to the data. g) Conductivity ratio ($\sigma/\sigma_{300 K}$) as a function of $T^{-1/4}$. The red line represents a linear fit to the data points. Data in (e–g) were obtained using the NcGc layer with a 22% LrGO ratio. Data in (b,c) are presented as mean values ± standard deviation from three samples.</figcaption></figure>

initial current readout is determined by the bottom NcGc layer, while the output current measured upon applying the pressure to the sensor is primarily dominated by the top LrGO layer and the contact resistance between them. Therefore, the sensitivity of the sensor can be enhanced either by decreasing the thickness of the NcGc layer or increasing the thickness of the LrGO layer. The double-layered pressure sensor was optimized to exhibit a highly nonlinear I–V response in its unpressurized state while maintaining high sensitivity across a wide linear sensing range, where the broad linearity is a desirable and effective feature that simplifies data processing by eliminating the need to segment the response curve into multiple pressure regions. This optimization was implemented through precise control of the double-layer characteristics, by using 200 µL of LrGO dispersions to form a 500.1 ± 33.2 nm thick conductive layer (Figure S18, Supporting Information), coupled with 75 µL of mixed dispersions to create a

469.7 ± 16.2 nm thick NcGc layer (Figures S11c,d,S19, and S20, Supporting Information).

Furthermore, systematic optimization of the sensor's interfacial architecture revealed that the face-to-face physical contact between the NcGc and LrGO layers offered the most effective configuration among the evaluated interfaces (NcGc-LrGO, NcGc-IDEs, and seamless interlayer contact), enabling enhanced electromechanical coupling and improved pressure response (Figures S21 and S22, Supporting Information). Given the minimal thicknesses and the absence of a need for complex structural design protocols in both LrGO and NcGc layers, their resistance changes remain relatively constant in both in-plane and out-of-plane directions (Figure S23, Supporting Information). During the compression process, the contact resistance ($R_c$) becomes the dominating factor in the overall resistance change (Note S4, Supporting Information). This leads to a proportional relationship
<figure><img src="image_6.png" /><figcaption>**Figure 3.** Sensing performance of the optimized pressure sensor. a) Schematic representation of the electrically designed double-layer pressure sensor. b) Relative current change showing a linear relationship with pressure up to 800 kPa. The red line represents the linear fit to the sensor data. c) Cyclic test performance at various pressures ranging from 13 to 710 kPa. d) Loading and unloading response times of the sensor across a pressure range from 77 to 324 kPa. e) Stepwise loading response over a pressure range from 0 to 780 kPa. f) Long-term durability test results for the pressure sensor over 10 000 cycles within a pressure range from 0 to 210 kPa. The inset provides a magnified view of the cyclic signals at the initial, middle, and final four cycles. g) Comparison of the sensitivity and linear sensing range as reported in the relevant literature and this work. h) Schematic depicting the conformal, wearable characteristics of NcGc layer-based sensor. i) Distinct wrist arterial pulse wave demonstrating a heart rate of ≈75 bpm. Data presented in (b–f) were measured at 1 V bias voltage and room temperature.</figcaption></figure>

between the relative current change and the applied pressure change, resulting in a linear pressure-sensing response.

The optimized sensor demonstrated outstanding performance, achieving a high sensitivity of $742.3 \text{ kPa}^{-1}$ across a broad linear sensing range of 800 kPa ($R^2 = 0.99913$) (Figure 3b). This sensitivity was calculated from the slope of the relative current change ($\Delta I/I_0$) with respect to pressure change ($\Delta P$). The pressure sensor exhibited excellent reproducibility, with consistent performance observed across four independently fabricated devices (Figure S24a, Supporting Information). The sensor also displayed excellent repeatability when subjected to cyclic pressures ranging from 13 to 710 kPa (Figure 3c) and featured rapid load-

ing and unloading response times of 40 and 30 ms, respectively, for pressure changes from 77 to 324 kPa (Figure 3d). Upon stepwise loading with various pressures ranging from 0 to 780 kPa, the sensor provided a fast and stable response to each increase in applied pressure (Figure 3e). The sensor is capable of detecting minor variations in pressures with a detection threshold as low as 0.8 kPa (Figure S24b, Supporting Information). Benefiting from the reliable sensor design and robust sensing materials, the sensor exhibited outstanding durability, maintaining consistent performance even after enduring 10 000 pressure cycles at 210 kPa (Figure 3f). In-depth analyses of material fatigue under high-pressure cycling (2500 cycles at 710 kPa) revealed no
significant structural degradation or loss of conductivity (Figure S24c, Supporting Information), confirming the mechanical and electrical resilience of the NcGc and LrGO layers, as well as the overall sensor structure. This broad linear sensing range and high sensitivity of our NcGc layer-based pressure sensor surpass those of most pressure sensors reported to date (Figure 3g; Table S1, Supporting Information), and the relevant literature is indicated in Figure S25 (Supporting Information).

High-performance pressure sensors hold great promise for various wearable applications, enabling reliable detection of pressures ranging from subtle wrist arterial pulses (1–10 kPa) to significant body weight (200–800 kPa). The sensor non-invasively monitored the arterial pulse wave, revealing a healthy heart rate of ≈75 beats per minute (Figure 3h,i; Figure S26a, Supporting Information). The sensor was also mounted on the soles of the feet to detect the pressure generated by the body weight. During a four-phase sit-to-stand movement sequence – feet off ground, feet on the ground, body flexion, and standing, performed by a subject – the sensor demonstrated a fast and stable response to each phase, completing a sit-stand-sit cycle (Figure S26b, Supporting Information). To meet the demands of practical applications in human-machine interface applications, a wireless data visualization system was developed (Figure S27a, Supporting Information). To facilitate the detection of large-area pressure or spatial pressure distribution, the sensor was scaled up to a 4 × 4 sensor array (Figure S28a,b, Supporting Information). The pressure sensor array was subjected to the pressure applied to two different sensors, and the visualized data displayed in the histogram demonstrated a crosstalk-free pressure response (Figure S27b,c and Movie S2, Supporting Information). The sensor array exhibited high resolution, effectively monitoring complex large-area pressure distributions. For example, when “UNISTRA” letter-shaped pressure was applied, the sensor array produced distinct visualizations of these letter shapes in the heat maps (Figure S28c–e, Supporting Information).

## 2.4. Inherent Thermal Correction by Bias Voltage

To investigate the inherent capability for thermal drift correction of the NcGc layer-based pressure sensor, we established a test platform integrated with a heating plate and a thermometer to assess pressure sensor performance across different temperatures (Figure 4a). Benefitting from the highly nonlinear I-V behavior of the NcGc layer, the sensor exhibited a nonlinear I–V characteristic in its unpressurized state, indicating that the initial current of the sensor can be tuned by the applied bias voltage (Figure 1g). As a result, under a constant room temperature of 25 °C, the sensor’s output performance was strongly dependent on the bias voltage, yet still maintained a linear response to pressure, showing a pronounced decrease in sensitivity as the bias voltage increased from 0.5 to 7 V (Figure 4b). The logarithmic sensitivity was found to be inversely proportional to the applied bias voltage (Figure 4c).

Furthermore, in addition to the electric field, carrier transport within the NcGc layer is thermally activated, as described by the Mott-VRH model, indicating that its conductivity is highly temperature-dependent. Consequently, the initial current of the pressure sensor exhibited a strong positive correlation with temperature over the range of 25–70 °C under various bias voltages

of 1, 3, and 5 V (Figure 4d). As the temperature rises, the hopping rate of charge carriers also increases, leading to a reduction in the effective resistance of the NcGc layer. Under a constant 1 V bias voltage, the sensor’s output sensitivity displayed a downward shift with increasing temperature, though they retained a broad and stable linear response across the measurement range, illustrating robustness despite thermal fluctuations (Figure 4e). A preliminarily evaluation of the long-term thermal stability revealed that the sensor’s I–V characteristics remain stable without significant drift after 12 h at 50 °C (Figure S29a, Supporting Information), demonstrating the robust thermal and electrical stability of the NcGc layer under prolonged elevated temperatures. The Joule heating effect of the NcGc layer, which dominates conduction in the unpressurized state, was evaluated under an elevated bias voltage of 7 V. A slight current increase, corresponding to a minor temperature rise (≈0.2 °C), was observed, followed by rapid signal stabilization and saturation (Figure S30, Supporting Information). Importantly, this behavior remained stable over repeated cycles, with no evidence of material degradation under high-voltage stress.

Due to the high dependence of the sensor’s output performance on bias voltage and temperature, the bias voltage can be utilized as an internal parameter to inherently compensate for thermal drift errors (Figure 4f). The calculated sensitivities were found to decrease with increasing temperature and bias voltage (Figure S29b, Supporting Information). This trend was visualized in a contour map, where contour lines represented consistent output sensitivity at different temperatures and bias voltages (Figure 4g). To demonstrate the inherent thermal correction capability, data from the contour line at 3 V was extracted (Figure 4h). The initial conditions were set at 25 °C and 3 V, allowing for temperature drift error to be compensated by decreasing the bias voltage (Figure 4i). Conventional hardware and software compensation strategies, which rely on additional thermal sensors or cumbersome calibration algorithms, often suffer from poor precision once the device’s accuracy starts to degrade. In contrast, for proof-of-concept, our inherent thermal correction approach was achieved by manually tuning the bias voltage, which maintained consistently high compensation accuracy and enhanced measurement reliability under varying temperature conditions (Table S3, Supporting Information). This approach is notably superior to software compensation, which primarily relies on calibration data and cannot ensure the accuracy of the results. For practical deployment, this method can be translated into a closed-loop control system that dynamically adjusts the bias voltage in real-time via a microcontroller-based feedback mechanism, enabling fully autonomous, self-adaptive operation and significantly improving sensor robustness across diverse environmental conditions.

## 2.5. Precise Robotic Gripper Demonstration

The pressure sensor with built-in thermal correction capability holds significant potential for robotic manipulation applications, ensuring reliable control across varying temperature conditions. To demonstrate its application, we developed an automated gripping system that monitors and displays both the gripping angle and the pressure sensor’s output current. The gripper
<figure><img src="image_7.png" /><figcaption>**Figure 4.** Performance investigation of pressure sensors with inherent thermal correction. a) Test platform for pressure sensor measurements at various temperatures, calibrating the stage temperature using a thermometer. b) $\Delta I/I_0$ of the sensor as a function of pressure under varying bias voltages at room temperature. c) Logarithmic relationship between sensitivity and bias voltage, demonstrating an inverse proportionality. d) Output current ratio as a function of temperature at different bias voltages. Linear fits to the data are represented by solid lines. e) $\Delta I/I_0$ as a function of pressure at a 1 V bias voltage under various temperatures ranging from 25 to 70 °C. f) Inherent temperature compensation achieved through internal bias voltage adjustment, enabling precise correction to restore sensor performance. g) Contour map showing output sensitivity across a range of bias voltages and temperatures. Contour lines represent constant sensitivity values despite changes in temperature and bias voltage. h) Contour line extracted from (g) (highlighted in red), starting from the initial condition point at 25 °C and 3 V. i) Thermal drift error at different temperatures with and without compensation by adjusting the bias voltage based on the contour line.</figcaption></figure>

continued closing until the current reached a preset level, triggering the start of the lifting action (Figure S31, Supporting Information). The working principle of inherent correction of thermal fluctuations consisted in eliminating temperature drift error through bias voltage adjustment. The temperature-induced current rise can be compensated by decreasing the bias voltage. Consequently, at a fixed preset current value ($I_{set}$), the corresponding force applied to the pressure sensor (which is also the gripping force) decreased at higher temperatures. This force is then restored to its original level by reducing the bias voltage (Figure S32, Supporting Information).

To simulate a high-temperature environment, we developed a flexible heater based on the laser-scribed graphene (LSG), created by the laser reduction of a GO film, to provide a stable heat source (Figure S33, Supporting Information). This flexible heater was mounted on the backside of the pressure sensor, enabling the generated heat transfer to the pressure sensor via thermal conduction (Figure 5a). The heater's surface temperature reached 70 °C with a power input of 0.12 W, while the surface temperature of the pressure sensor was stabilized at 50 °C (Figure S34, Supporting Information). The combined heater and pressure sensor assembly were then mounted on a gripper finger to measure the
<figure><img src="image_8.png" /><figcaption>**Figure 5.** Proof of concept application in a reliable gripping system. a) Schematic cross-sectional view of the pressure sensor and flexible heater, illustrating the heat generated by the heater being transferred to the pressure sensor, simulating an increase in ambient temperature. b) Photograph of the gripper equipped with the pressure sensor and a flexible heater attached to the finger. The gripper angle indicates the degree of closure. c) Schematic illustration of the robotic gripper's working conditions: initial conditions at 3 V and 25 °C (black dot), increased temperature from 25 to 50 °C (blue dot), and compensated thermal drift error by reducing the bias voltage from 3 to 2 V (yellow dot). d) Successful completion of the lifting action under initial working conditions at 25 °C and 3 V bias voltage. e) Lifting action failed when the working temperature increased to 50 °C. f) Completed the lifting operation when the thermal drift error was compensated by reducing the bias voltage to 2 V. Snapshots of the robotic gripping system in various states: g) Successfully lifting the object under the initial condition of 25 °C and 3 V bias voltage. h) Failed to lift the object when the temperature increased to 50 °C due to the temperature drift error. i) Successfully lifted the object when the temperature-induced error was compensated by decreasing the bias voltage from 3 to 2 V.</figcaption></figure>

gripping force, with the gripper angle indicating the degree of finger closure (Figure 5b).

The gripper was programmed to operate in five stages: contact, lifting, holding for 10 s, lowering, and release. The heater and pressure sensor assembly were evaluated under three conditions: initial settings of 3 V and 25 °C, increased temperature from 25 to 50 °C, and compensation for thermal drift error by reducing the bias voltage from 3 to 2 V (Figure 5c). Under the initial conditions, the gripper successfully completed the lifting action with a gripper angle of 68° (Figure 5d,g; Movie S3, Supporting Information). When the temperature was raised to 50 °C using the flexible heater, the gripper failed to perform the lifting action due to the preset current value being reached prematurely (Figure 5e,h; Movie S4, Supporting Information), resulting in a reduced gripping angle of 66°. However, after compensating for the thermal-

induced error by decreasing the bias voltage to 2 V, the gripping force and angle were restored to the original values, enabling the gripper to complete the lifting action (Figure 5f,i; Movie S5, Supporting Information). This demonstrated the unique inherent thermal correction capability of pressure sensors in robotic gripper systems, ensuring stable and accurate manipulation across varying temperature conditions.

### 3. Conclusion

In summary, we propose a new sensor design paradigm that integrates a nonlinear conductive graphene composite layer with a bilayer structural configuration, offering a powerful platform for high-performance and adaptive pressure sensing. This approach
enabled the device to achieve exceptional sensing performance, including ultra-high sensitivity (742.3 kPa⁻¹), outstanding linearity across a wide pressure range (R² = 0.99913 up to 800 kPa), and excellent mechanical durability over 10 000 cycles, making it well-suited for demanding wearable applications. The core innovation lies in leveraging the nonlinear conductivity of the NcGc layer, engineered to operate near its percolation threshold. This enables bias-voltage-mediated tunability of sensor behavior, offering intrinsic temperature compensation without requiring additional structural modifications. We demonstrate this capability through robust performance in a robotic gripper under varying thermal conditions.

Importantly, this nonlinear conductive material strategy – when coupled with a bilayer design – opens new avenues for sensor adaptability in complex environments. The proposed framework is broadly extendable to compensate for other environmental factors, such as humidity or multi-physics interference, by modulating internal electrical parameters. This work therefore establishes a universal, scalable, and versatile platform for next-generation intelligent wearable sensors that are not only high-performing but also environmentally resilient and commercially promising.

**4. Experimental Section**

**Preparation of LrGO:** The GO aerogel was obtained by freezing an aqueous dispersion of graphene oxide (4 mg mL⁻¹, monolayer content > 95%, Graphenea) in liquid nitrogen for 20 min and drying in a lyophilization unit (Alpha 2–4 LSCbasic, Martin Christ) for 2 days. The GO aerogel was then irradiated in an ambient atmosphere using a commercial laser engraver (KZ 300, NEJE) at a distance of ≈6 cm. The wavelength and power of the irradiated laser were 445 nm and 87 mW, respectively. The laser power was calibrated with a compact optical power meter (PM100A, Thorlabs) and a compact photodiode power sensor (S302C, Thorlabs). Interestingly, the entire bulk GO aerogel was spontaneously reduced after instantaneous excitation of the laser energy, and the reduction process took only hundreds of milliseconds. The LrGO aerogel was dispersed in N,N-dimethylformamide (DMF) at a concentration of 0.2 mg mL⁻¹ and magnetically stirred at 80 °C for 20 h to ensure adequate exfoliation and uniform dispersion.

**Preparation of CmGO:** 10 mL of aqueous GO dispersion (4 mg mL⁻¹) was diluted with 50 mL of ethanol in a round-bottomed flask and sonicated (with power of 550 W, S60H, Elmasonic) for 30 min to disperse uniformly. Then, 0.2 mL of APTES (99%, Sigma–Aldrich) was added dropwise to the diluted GO dispersion under magnetic stirring, followed by stirring for 16 h at 78 °C under reflux in an oil bath. After cooling to room temperature, the mixture was centrifuged (Centrifuge 5804, Eppendorf) at 8000 rpm for 15 min and the supernatant was discarded to remove excess APTES. Next, the sediment was re-dispersed into ethanol and the mixture was sonicated for 20 min and then centrifuged again under the same conditions. This sonication and centrifugation washing process was repeated three times. After the final centrifugation step, the CmGO was dispersed in 40 mL of ethanol to reach a concentration of 1 mg mL⁻¹ and subsequently sonicated for 30 min to ensure optimal dispersion for further experimental procedures.

**Materials Characterizations:** To facilitate comparative analysis, the material properties of APTES, GO, LrGO, and CmGO were characterized using a series of fundamental techniques on solid powder samples. Elemental composition analysis was performed via X-ray photoelectron spectroscopy using a Thermo Scientific K-Alpha X-ray photoelectron spectrometer. The set-up employed an aluminum X-ray source (energy: 1.4866 keV) and operated at a vacuum level of 10⁻⁸–10⁻⁹ mbar within the main chamber. A fixed X-ray beam spot size of 400 µm was utilized for all measurements. All acquired XPS spectra were calibrated using the C—C peak

at a binding energy of 284.8 eV as a reference. Raman spectra were acquired under ambient conditions using a Renishaw inVia RE04 spectrometer equipped with a 532 nm laser excitation source and a high-resolution 2400 gr mm⁻¹ grating with a 100× objective (numerical aperture 0.85). To mitigate potential local heating and damage effects, the laser power was maintained below 1 mW. X-ray powder diffraction patterns were obtained using a Bruker D8 X-ray diffractometer operating in Bragg-Brentano geometry. The diffractometer utilized a Cu Kα1 monochromatic X-ray source (λ = 0.15 406 nm) and a LynxEye_2 detector in 1D mode. A nickel filter was employed to attenuate Cu Kβ radiation. XRD data were collected over a 2θ range of 4° to 60° with a step size of 0.01° and a scanning rate of 0.1 s/step. Fourier-transform infrared spectroscopy was performed using a JASCO FT/IR-4700 Fourier Transform Infrared Spectrometer equipped with an attenuated total reflectance (ATR) diamond accessory.

**Fabrication of the Graphene Composite Layers:** LrGO and CmGO were separately diluted with ethanol to a concentration of 0.05 mg mL⁻¹. Subsequently, both dispersions were sonicated for 10 min at 50% amplitude using a QSonica sonicator (20 kHz, 700 W) with a one-second on/off pulse, ensuring uniform material distribution. The conductive LrGO dispersion was then mixed with the non-conductive CmGO dispersion at LrGO volume ratios ranging from 10% to 100%. The resulting mixtures were sonicated for 3 min at 50% amplitude with a one-second on/off pulse to ensure homogeneity. Finally, 75 µL of the mixed dispersion was vacuum filtrated through a PTFE membrane filter (0.45 µm pore size, Millipore). A homemade polydimethylsiloxane (PDMS, Sylgard 184, Dow Corning) elastomer mask was used to define a uniform square deposition area of 5 × 5 mm². While the PTFE membrane remained saturated with ethanol, the graphene composite layer deposited on its surface was transfer-printed onto a substrate and the residual ethanol was removed via nitrogen blow-drying. The substrate consisted of a flexible PET film patterned with 60 nm thick gold IDEs, 250 µm width/spacing, the detailed dimensions displayed in Figure S16 (Supporting Information). The IDEs were fabricated via thermal evaporation using a Plassys MEB 300 system and a custom stainless-steel mask.

**Characterizations of the Graphene Composite Layers:** Surface morphology of the NcGc layer, LrGO layer, and PTFE membrane was characterized using scanning electron microscopy (SEM, FEI Quanta 250 FEG) at an accelerating voltage of 20 kV, a gun pressure of ≈10⁻⁸ mbar, and a chamber pressure of ≈10⁻⁶ mbar. Layer thickness and surface roughness of the graphene composite layers were measured using a DektakXT stylus profilometer (Bruker) under ambient conditions. The conductivity of conductive LrGO was calculated from the square resistance, which was measured by a four-point probe system (RM3000, Jandel). Optical images of the NcGc layer and PTFE membrane were acquired using an Olympus BX51 optical microscope equipped with an Olympus DP73 camera system. The electrical characteristics of the graphene composite layer on PET/IDEs were measured using a Keithley 2636A source meter to acquire *I*–*V* curves with the bias voltage swept from −10 V to +10 V in steps of 0.1 V.

**Temperature-Dependent Measurements of NcGc Layer:** NcGc layer-based devices were characterized using a two-probe configuration in a customized Janis cryogenic probe station connected to a Keithley 4200A SCS semiconductor parameter analyzer to obtain *I*–*V* characteristics. The cryogenic chamber pressure was kept at ≈10–6 mbar. A Sumitomo RDK-408D2 cryogen-free closed cycle was thermally anchored to the copper sample holder, enabling temperature measurements within the range of 100 to 320 K in steps of 10 K. Temperature was controlled using a cryogenic temperature controller (Model 336, Lake Shore) operating in proportional-integral-derivative (PID) mode.

**Fabrication of Double-Layered Pressure Sensor:** LrGO layers were fabricated using the same vacuum filtration and transfer printing techniques utilized for the NcGc layers, but they were transferred onto pristine PET substrates. Pressure sensors were fabricated through a layered assembly process. The bottom NcGc layer and top LrGO layer were carefully aligned and physically stacked face-to-face, followed by encapsulation with Scotch tape (3 M), ensuring a stable interface between the layers. Electrical connections were established using copper wire (0.21 mm in diameter, RS PRO) and silver conductive paste (SCP03B, Electrolube). The thicknesses
of the PET substrate and the assembled pressure sensor were measured using a caliper.

**Mechano-Responsive and Electrical Testing:** The fabricated pressure sensor was mounted onto a glass slide and positioned on a test stage beneath a motorized compression plate. A digital force gauge (M7-5, Mark-10) integrated with a motorized test stand (ESM 303, Mark-10) was employed to apply controlled normal pressure to the sensors. Force data were recorded using the MESUR Gauge Puls software (Mark-10). Simultaneously, the pressure-induced electrical response of the pressure sensors was quantified using a Keithley 2636A source meter and recorded using KickStart2 software (Keithley). The change in current versus applied pressure was measured under a motor speed of 0.5 mm min⁻¹ until a maximum force of 20 N was reached. Cyclic tests under various pressures, as well as long-term stability tests, were performed with a motor speed of 2 mm min⁻¹. All measurements were performed at a bias voltage of 1 V unless otherwise specified.

**Wearable Pressure Sensor Applications:** For measuring subtle pressure, such as wrist arterial pulse, the pressure sensor was attached to the wrist directly over the radial artery. Wrist pulse recordings were obtained using a Keithley 2636A source meter at a bias voltage of 1 V while the human subject was at rest. For measuring large pressures, such as foot pressure, the pressure sensor was mounted on the sole to detect pressure changes during a four-phase sit-to-stand movement. These phases included feet off-ground, feet on the ground, body flexion, and standing (as illustrated schematically in the inset of Figure S26b, Supporting Information). The output current response to changes in foot pressure was recorded using a Keithley 2636A source meter with a bias voltage of 1 V.

**Fabrication of Pressure Sensor Array:** The pressure sensor array is constructed as a 4 × 4 matrix, comprising 16 individual sensors integrated into a single unit. First, the interdigital electrodes for the sensor array were fabricated directly by evaporating 60 nm gold onto a PET substrate through an elaborately designed stainless-steel mask. Subsequently, the bottom NcGc layers were sequentially transfer-printed onto the IDEs patterns. The pressure sensor array was then completed by assembling LrGO layers on top of the NcGc layers, using the same process employed for individual pressure sensor fabrication. Finally, the pressure sensor array was wired for further performance investigation. The optimal volumes for fabricating the NcGc and LrGO layers were determined to be 75 and 200 µL, respectively.

**Wireless Data Visualization System:** A signal readout system was constructed for the real-time visualization of pressure distribution applied to the sensor array. The system comprised a voltage divider circuit, an Arduino UNO microcontroller board, an HC-05 Bluetooth module, and a computer. Digital switch multiplexers (CD4051BE, Texas Instruments) were used to regulate the connection of each pressure sensor through the loop scanning of the ON/OFF state of each electrode. The readout voltage, divided across a fixed resistor (R₀) and representing the resistance change of each pressure sensor, was recorded by the microcontroller unit (MCU) during each scan cycle, which encompassed a complete signal scan of all 16 pressure sensors. The analog readout voltage signal was then converted to a digital signal using a 10-bit analog-to-digital converter (ADC). The digital data was then wirelessly transmitted to a computer in real-time via the Bluetooth Low Energy (BLE) communication protocol. Received voltage (V) data were converted to current (I) values using the relationship $I = V/R_0$, where $R_0$ represents the fixed resistor value. The resulting current values were then reshaped into a 4 × 4 matrix, thereby enabling the visualization of the pressure distribution as both the 2D heat maps and the 3D histograms.

**Inherent Temperature Compensation Test:** The pressure sensor was mounted on a heating plate with a PID temperature controller (TC200, Thorlabs). The temperature varied from 25 to 70 °C, and a thermometer (RS42, RS PRO) was used to calibrate the top surface temperature of the heating plate. At each temperature point, a 3-min stabilization period was allowed to elapse. The heating plate, with the pressure sensor attached, was positioned beneath the compressing stage of a force gauge (M7-5, Mark-10) to measure the output current as a function of pressure under varying bias voltages (from 0.5 to 7 V) and temperatures (same methods for investigating the individual pressure sensor).

**Fabrication of LSG-Based Flexible Heater:** A uniform GO film was prepared by drop-casting 1 mL of GO slurry (4 mg mL⁻¹, Graphenea) onto a flexible polyimide (PI) film (300 HN, Kapton), followed by drying in an oven at 80 °C for one hour under normal pressure to remove residual water. A commercial laser scriber (KZ 300, NEJE) with a 445 nm laser operating at 80% of its maximum power (87 mW) was used to scribe the GO film into a 6 × 13 mm² square, yielding highly conductive LSG. The PI film, displaying the LSG pattern, was then cut into a 15 × 20 mm² square for subsequent utilization. The LSG was electrically contacted on both sides using copper wire (0.21 mm in diameter, RS PRO) and silver conductive paste (SCP03B, Electrolube). The flexible LSG-based heater was powered by a direct current (DC) power supply (LW-3010KDS, LWLONGWEI) at a power output of 0.12 W. The surface temperature generated via Joule heating was measured using a thermal infrared camera (FLIR ONE Pro, FLIR).

**Manipulation of the Robotic Gripper with Inherent Temperature Compensation Function:** The inherent temperature-compensated pressure sensor, with the flexible heater attached to its backside, was affixed to the finger of a robotic gripper. The robotic gripper was installed on a Tinkerkit Braccio robotic arm (Arduino), enabling movement within a vertical plane via two degrees of freedom (DOF) driven by servo motors (SR-311 and SR-431, SpringRC). The robotic arm, powered by an onboard voltage regulator (Braccio shield, Arduino), was controlled using an Arduino UNO controller board. The output current induced by gripping force change was continuously measured using a source meter (Keithley 2636A) at an initial bias voltage of 3 V, and the flexible heater was powered by a DC power supply (LW-3010KDS, LWLONGWEI). The robotic gripper was programmed to undergo a continual closing motion from an open state (with an initial range of 30° to 80°), and the gripper angle, indicative of the degree of closure, was recorded and transmitted to the Arduino controller board. A LabVIEW (National Instruments) program was developed to simultaneously control and acquire data from both the Keithley source meter and the Arduino controller board, which facilitated the simultaneous recording and display of both the output current and the gripper angle, enabling real-time monitoring of the robotic gripper. A flacon tube containing ≈22 mL of water was utilized as the object to be gripped. The output current was subjected to continuous comparison with a preset threshold, and upon reaching this threshold, a command was relayed to the controller board, prompting the robotic arm to execute a lifting action. The object was then lifted and gripped for 10 s, after which it was lowered and gradually released. This gripping manipulation was conducted at three different conditions: 1) Initial conditions: 3 V bias voltage and room temperature of 25 °C. 2) Elevated temperature: 3 V bias voltage and 50 °C temperature. The elevated temperature was achieved via a flexible heater operating at an input power of 0.12 W. 3) Inherent temperature compensation: 2 V bias voltage and temperature of 50 °C. This condition employed a bias voltage adjustment from 3 to 2 V to compensate for temperature-induced drift.

**Experiments on Human Subjects:** The wearable demonstrations were conducted by a research volunteer, an adult male, with ensured safety during the characterization. The volunteer was duly informed of the nature of the tests and provided his consent to participate.

## Supporting Information

Supporting Information is available from the Wiley Online Library or from the author.

## Acknowledgements

This work was supported by the European Union through the ERC project SUPRA2DMAT (GA-833707) and the Horizon Europe research and innovation program HYPERSONIC (GA-101129613) as well as the Agence Nationale de la Recherche through the Interdisciplinary Thematic Institute SysChem via the IdEx Unistra (ANR-10-IDEX-0002) within the program Investissement d'Avenir, the Foundation Jean-Marie Lehn, the Institut Universitaire de France (IUF) and the Chinese Scholarship Council. The authors thank Dr. B. Han for assistance with XPS characterizations, Dr. V.
Montes García for a valuable discussion regarding Raman spectroscopy, and Dr. Y. Jeong for his help with temperature-dependent electrical measurements.

## Conflict of Interest

The authors declare no conflict of interest.

## Author Contributions

F.L., A.C., and P.S. conceived the idea, designed the experiments, wrote the manuscript and all the authors contributed to revisions. F.L. performed all the experiments and data analysis and designed the data visualization and robotic gripper systems. All authors discussed the results and commented on the manuscript.

## Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

## Keywords

flexible pressure sensors, graphene composites, high sensitivity, inherent thermal correction, nonlinear conductivity, precise robotic manipulation, wide linear sensing range

Received: February 25, 2025

Revised: May 6, 2025

Published online: May 27, 2025

[1] a) Z. Liu, X. Hu, R. Bo, Y. Yang, X.u Cheng, W. Pang, Q. Liu, Y. Wang, S. Wang, S. Xu, Z. Shen, Y. Zhang, *Science* **2024**, *384*, 987; b) J. Park, Y. Lee, S. Cho, A. Choe, J. Yeom, Y. G. Ro, J. Kim, D.-H. Kang, S. Lee, H. Ko, *Chem. Rev.* **2024**, *124*, 1464; c) S. Lee, S. Franklin, F. A. Hassani, T. Yokota, M. O. G. Nayeem, Y. Wang, R. Leib, G. Cheng, D. W. Franklin, T. Someya, *Science* **2020**, *370*, 966.

[2] a) W. Cheng, X. Wang, Z.e Xiong, J. Liu, Z. Liu, Y. Jin, H. Yao, T.-S. Wong, J. S. Ho, B. C. K. Tee, *Nat. Mater.* **2023**, *22*, 1352; b) Y. He, Y. Cheng, C. Yang, C. F. Guo, *Nat. Mater.* **2024**, *23*, 1107; c) J. Baek, Y. Shan, M. Mylvaganan, Y. Zhang, X. Yang, F. Qin, K. Zhao, H. W. Song, H. Mao, S. Lee, *Adv. Mater.* **2023**, *35*, 2304070.

[3] a) Y.-C. Huang, Y. Liu, C. Ma, H.-C. Cheng, Q. He, H. Wu, C. Wang, C.-Yi Lin, Y.u Huang, X. Duan, *Nat. Electron.* **2020**, *3*, 59; b) C. M. Boutry, Y. Kaizawa, B. C. Schroeder, A. Chortos, A. Legrand, Z. Wang, J. Chang, P. Fox, Z. Bao, *Nat. Electron.* **2018**, *1*, 314; c) S. Lee, A. Reuveny, J. Reeder, S. Lee, H. Jin, Q. Liu, T. Yokota, T. Sekitani, T. Isoyama, Y. Abe, Z. Suo, T. Someya, *Nat. Nanotechnol.* **2016**, *11*, 472; d) K.-H.o Ha, W. Zhang, H. Jang, S. Kang, L. Wang, P. Tan, H. Hwang, N. Lu, *Adv. Mater.* **2021**, *33*, 2103320.

[4] a) R. Chen, T. Luo, J. Wang, R. Wang, C. Zhang, Y.u Xie, L. Qin, H. Yao, W. Zhou, *Nat. Commun.* **2023**, *14*, 6641; b) X.-M. Wang, L.u-Q.i Tao, M. Yuan, Z.e-P. Wang, J. Yu, D. Xie, F. Luo, X. Chen, C. Wong, *Nat. Commun.* **2021**, *12*, 1776; c) L. Shi, Z. Li, M. Chen, Y. Qin, Y. Jiang, L.

Wu, *Nat. Commun.* **2020**, *11*, 3529; d) Y. Li, J. Long, Y. Chen, Y. Huang, N. Zhao, *Adv. Mater.* **2022**, *34*, 2200517.

[5] L. Pan, A. Chortos, G. Yu, Y. Wang, S. Isaacson, R. Allen, Y.i Shi, R. Dauskardt, Z. Bao, *Nat. Commun.* **2014**, *5*, 3002.

[6] a) Y. Lee, J. Myoung, S. Cho, J. Park, J. Kim, H. Lee, Y. Lee, S. Lee, C. Baig, H. Ko, ACS Nano **2021**, *15*, 1795; b) X. Lin, Y.e Teng, H. Xue, Y.u Bing, F. Li, J. Wang, J. Li, H. Zhao, T. Zhang, *Adv. Funct. Mater.* **2024**, *34*, 2316314; c) K. Li, Z. Li, W. Wang, T. Zhang, X. Yang, ACS Appl. Mater. Interfaces **2024**, *16*, 14171.

[7] a) H. Niu, H. Li, S. Gao, Y. Li, X. Wei, Y. Chen, W. Yue, W. Zhou, G. Shen, *Adv. Mater.* **2022**, *34*, 2202622; b) C. Jeong, J. S. Lee, B. Park, C. S. Hong, J. U. Kim, T. I. Kim, *Adv. Mater.* **2019**, *31*, 1902689; c) C. M. Boutry, L. Beker, Y. Kaizawa, C. Vassos, H. Tran, A. C. Hinckley, R. Pfattner, S. Niu, J. Li, J. Claverie, Z. Wang, J. Chang, P. M. Fox, Z. Bao, *Nat. Biomed. Eng.* **2019**, *3*, 47.

[8] a) M. Jia, C. Yi, Y. Han, et al., *Adv. Sci.* **2022**, *9*, 2105738; b) K. Cao, M. Wu, J. Bai, Z. Wen, J. Zhang, T. Wang, M. Peng, T. Liu, Z. Jia, Z. Liang, L. Jiang, *Adv. Funct. Mater.* **2022**, *32*, 2202360.

[9] a) T. Chen, J. van Gelder, B. van de Ven, S. V. Amitonov, B. de Wilde, H.-C. Ruiz Euler, H. Broersma, P. A. Bobbert, F. A. Zwanenburg, W. G. van der Wiel, *Nature* **2020**, *577*, 341; b) C. Y. Liu, Y. Zheng, B. Zhang, X. Q. Zheng, S. Q. Hu, K. Han, *IEEE Access* **2019**, *7*, 50536; c) W. Yang, J. Wang, S. Luo, S. Yu, H. Huang, R. Sun, C. P. Wong, ACS Appl. Mater. Interfaces **2016**, *8*, 35545.

[10] G. Y. Bae, J. T. Han, G. Lee, S. Lee, S. W. Kim, S. Park, J. Kwon, S. Jung, K. Cho, *Adv. Mater.* **2018**, *30*, 1803388.

[11] a) K. Kwon, H. Wang, J. Lim, K. S. Chun, H. Jang, I. Yoo, D. Wu, A. J. Chen, C. G. Gu, L. Lipschultz, J. U. Kim, J. Kim, H. Jeong, H. Luan, Y. Park, C.-J. Su, Y. Ishida, S. R. Madhvapathy, A. Ikoma, J. W. Kwak, D. S. Yang, A. Banks, S. Xu, Y. Huang, J.-K. Chang, J. A. Rogers, Proc. Natl. Acad. Sci. USA **2021**, *118*, 2020398118; b) Y. Park, H. Luan, K. Kwon, T. S. Chung, S. Oh, J.-Y. Yoo, G. Chung, J. Kim, S. Kim, S. S. Kwak, J. Choi, H.-P. Phan, S. Yoo, H. Jeong, J. Shin, S. M. Won, H.-J. Yoon, Y. H. Jung, J. A. Rogers, *npj Flexible Electron.* **2024**, *8*, 6; c) C. Wu, F. Lin, X. Pan, Y. Zeng, G. Chen, L. Xu, Y. He, G. He, Q. Chen, D. Sun, Z. Hai, *Chem. Eng. J.* **2023**, *457*, 141269.

[12] Y. M. Yang, S. Lin, J. H. Hu, *Sens. Actuators, B* **2023**, *385*, 133721.

[13] a) H. Nassira, A. Sanchez-Ferrer, J. Adamcik, S. Handschin, H. Mahdavi, N. Taheri Qazvini, R. Mezzenga, *Adv. Mater.* **2016**, *28*, 6914; b) Y. Xu, Z. Ye, G. Zhao, Q. Fei, Z. Chen, J. Li, M. Yang, Y. Ren, B. Berigan, Y. Ling, X. Qian, L. Shi, I. Ozden, J. Xie, W. Gao, P.-Y. Chen, Z. Yan, *Nat. Nanotechnol.* **2024**, *19*, 1158.

[14] C. B. Huang, S. Witomska, A. Aliprandi, M. A. Stoeckel, M. Bonini, A. Ciesielski, P. Samori, *Adv. Mater.* **2019**, *31*, 1804600.

[15] S. Ippolito, A. G. Kelly, R. Furlan de Oliveira, M.-A. Stoeckel, D. Iglesias, A. Roy, C. Downing, Z. Bian, L. Lombardi, Y. A. Samad, V. Nicolosi, A. C. Ferrari, J. N. Coleman, P. Samorì, *Nat. Nanotechnol.* **2021**, *16*, 592.

[16] N. S. Singh, F. Mayanglambam, H. B. Nemade, P. K. Giri, *RSC Adv.* **2021**, *11*, 9488.

[17] a) E. Piatti, A. Arbab, F. Galanti, T. Carey, L. Anzi, D. Spurling, A. Roy, A. Zhussupbekova, K. A. Patel, J. M. Kim, D. Daghero, R. Sordan, V. Nicolosi, R. S. Gonnelli, F. Torrisi, *Nat. Electron.* **2021**, *4*, 893; b) J. Syurik, O. A. Ageev, D. I. Cherednichenko, B. G. Konoplev, A. Alexeev, *Carbon* **2013**, *63*, 317.

[18] a) S. Ippolito, F. Urban, W. Zheng, O. Mazzarisi, C. Valentini, A. G. Kelly, S. M. Gali, M. Bonn, D. Beljonne, F. Corberi, J. N. Coleman, H. I. Wang, P. Samorì, *Adv. Mater.* **2023**, *35*, 2211157; b) A. Kovtun, A. Candini, A. Vianelli, A. Boschi, S. Dell'Elce, M. Gobbi, K. H.o Kim, S. Lara Avila, P. Samorì, M. Affronte, A. Liscio, V. Palermo, ACS Nano **2021**, *15*, 2654; c) Y. Q. Zeng, T. Li, Y. G. Yao, T. Y. Li, L. B. Hu, A. Marconnet, *Adv. Funct. Mater.* **2019**, *29*, 1901388.