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DOI 10.1002/smll.74340

Медицинская библиотека · Small (Weinheim an der Bergstrasse, Germany) · 2026

Self-Powered MXene@Perovskite Thermoelectric Skin for Multimodal Mid-Infrared Sensing and Human Signal Recognition.

Wang Qiang, Zhao Yinghao, Wang Xiaoyue, Li Yiqiang, Li Gen, Cao Xiaojuan, He Xiaonan, Zhang Yuankun, Chen Fei, Yang Lixue

СтатьяПоддержка исследования: вне правительства СШАПеревод пока не подготовлен

Аннотация

The effective detection of human thermal radiation (8-14 µm) was critical for next-generation human-machine interactions (HMIs). However, conventional mid-infrared sensors were typically constrained by external power requirements and limited capability in decoding complex multimodal signals. Here, we developed a bio-inspired, self-powered thermoelectric skin based on a V2C MXene@CsPbBr3 heterostructure. By combining the local surface plasmon resonance (LSPR) of V2C MXene with the photothermoelectric effect (PTE) of perovskite, efficient photothermal conversion and self-driven signal generation within the atmospheric window were achieved. The platform supported multimodal signal processing, including gesture recognition, Morse code encryption, and real-time motion tracking (velocity and displacement). In addition, ultrasensitive, bias-free detection of human pulse signals (66-100 bpm) was enabled, and high responsivity, excellent operational stability (>10 000 cycles), and precise capture of subtle physiological dynamics were demonstrated. Furthermore, this skin integrated with a CNN-bidirectional long short-term memory-attention neural network realized silent speech recognition with 95.9% accuracy. This work established a synergistic LSPR-PTE framework, offering a scalable strategy for intelligent electronic skins and contactless HMIs.

Для цитирования: Wang Qiang, Zhao Yinghao, Wang Xiaoyue, Li Yiqiang, Li Gen, Cao Xiaojuan, He Xiaonan, Zhang Yuankun, Chen Fei, Yang Lixue. Self-Powered MXene@Perovskite Thermoelectric Skin for Multimodal Mid-Infrared Sensing and Human Signal Recognition.. Small (Weinheim an der Bergstrasse, Germany). 2026. DOI: 10.1002/smll.74340.

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