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Updated: Jul 3, 2026
06:08
Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
Published on: May 5, 2011
Self-Powered MXene@Perovskite Thermoelectric Skin for Multimodal Mid-Infrared Sensing and Human Signal Recognition
Qiang Wang1, Yinghao Zhao1, Xiaoyue Wang1
1College of New Materials and Chemical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China.
Small (Weinheim an Der Bergstrasse, Germany)
|July 1, 2026
Summary
Researchers developed a self-powered thermoelectric skin for advanced human-machine interactions. This bio-inspired electronic skin efficiently detects thermal radiation, enabling multimodal signal processing and silent speech recognition.
Area of Science:
- Materials Science
- Nanotechnology
- Biomedical Engineering
Background:
- Effective detection of human thermal radiation (8-14 µm) is crucial for next-generation human-machine interactions (HMIs).
- Conventional mid-infrared sensors face limitations due to external power needs and restricted multimodal signal decoding capabilities.
Purpose of the Study:
- To develop a bio-inspired, self-powered thermoelectric skin for advanced HMIs.
- To enable efficient photothermal conversion and self-driven signal generation within the atmospheric window.
- To achieve multimodal signal processing, including gesture recognition, Morse code, and motion tracking.
Main Methods:
- Fabrication of a V₂C MXene@CsPbBr₃ heterostructure thermoelectric skin.
- Integration of V₂C MXene's local surface plasmon resonance (LSPR) with perovskite's photothermal-electric (PTE) effect.
- Utilizing a CNN-bidirectional long short-term memory-attention neural network for silent speech recognition.
Main Results:
- Achieved efficient photothermal conversion and self-driven signal generation.
- Demonstrated multimodal signal processing for gestures, Morse code, and motion tracking.
- Enabled ultrasensitive, bias-free human pulse detection (66-100 bpm) with high responsivity and stability (>10,000 cycles).
- Realized silent speech recognition with 95.9% accuracy.
Conclusions:
- Established a synergistic LSPR-PTE framework for intelligent electronic skins.
- Developed a scalable strategy for advanced, contactless human-machine interactions.
- Highlighted the potential of thermoelectric skin in diverse sensing and HMI applications.
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