Low-Crosstalk Dual-Channel Temperature-Pressure Sensor Array Based on PEDOT:PSS/MXene/Carbon Foam Composites for Multimodal Recognition
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Updated: Nov 7, 2026

Microfluidic Channel-Based Soft Electrodes and Their Application in Capacitive Pressure Sensing
Published on: March 17, 2023
Mouhu Wu1, Bin Wang1, Weijiang Zhao1
1School of Materials Science & Engineering, Key Laboratory of Functional Textile Material and Product, Ministry of Education, Xi'an Polytechnic University, Xi'an710048, China.
Abstract:
Reliable electronic skin requires thermal and mechanical inputs to be resolved in the same soft device, but stacked sensor architectures often add fabrication complexity and interfacial coupling. Here, we presented a low-crosstalk dual-channel temperature-pressure sensor based on PEDOT:PSS/MXene/carbonized melamine foam (PMCF) composites, in which pressure and temperature are transduced through distinct physical processes. Reversible contact reconstruction within PMCF defined a piezoresistive channel that modulated conductance under pressure, whereas the PEDOT:PSS/MXene network determined a thermoelectric channel that converted a temperature gradient into voltage. The integrated architecture enabled pressure-dominated conductance and temperature-dominated voltage readouts within the PMCF composites. The thermoelectric channel produced a linear voltage response within ΔT of 0-50 K, a Seebeck coefficient of 37.9 μV K-1, and stable output over 12,000 s of thermal cycling, whereas the piezoresistive channel presented a linear response over 1-20 kPa, with a sensitivity of 1.6409 ± 0.016 kPa-1 and stable output during load-unload cycles for 7000 s. The minimum temperature-difference resolution was 0.1 K. Temperature/pressure channels retained their dominant stimulus origins, with pressure-to-voltage and temperature-to-current crosstalk ratios below 0.40% and 0.35%, respectively. The PMCF sensor facilitated subtle contact acoustic-vibration sensing and array-level multimodal input. A CNN-BiLSTM-attention model from machine learning was employed to classify five acoustic-vibration signals with an accuracy of 97.9%. A CNN model, applied in a 3 × 3 sensor array, recognized digit position, input mode, and joint digit-mode patterns with accuracies of 97.4%, 99.1%, and 96.4%, respectively, indicating that the PMCF sensor array supported low-crosstalk multimodal sensing and array-level human-machine input under controlled conditions.