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Related Experiment Videos

Deep learning-enabled self-powered bimodal flexible sensor for intelligent access control.

Jiamin Chen1, Xiaochen Wang1, Nan Wang2

  • 1School of Microelectronics, Shanghai University, No. 20 Chengzhong Road, Jiading District, Shanghai, 201800, China.

Nanotechnology
|June 29, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces a self-powered sensor for intelligent access control, overcoming limitations of traditional biometrics. The innovative device uses a triboelectric nanogenerator and deep learning for high-accuracy material and user identification in smart security systems.

Area of Science:

  • Materials Science
  • Electrical Engineering
  • Computer Science

Background:

  • Intelligent access control systems are vital for smart architecture and urban security.
  • Traditional biometrics face challenges like power dependency, privacy risks, and environmental sensitivity.
  • A need exists for low-power, high-security, and multidimensional sensing solutions.

Purpose of the Study:

  • To develop a self-powered bimodal sensor for intelligent access control.
  • To provide a low-power, high-security, and multidimensional sensing solution.
  • To address the limitations of existing biometric technologies.

Main Methods:

  • A single-electrode triboelectric nanogenerator with a polydimethylsiloxane triboelectric layer and micro-pyramid array was designed.
Keywords:
CNNTENGbimodal sensingsmart securitysurface structure

Related Experiment Videos

  • Contact electrification and electrostatic induction were used to convert mechanical stimuli into electrical signals.
  • A deep learning framework utilizing a convolutional neural network was implemented for signal analysis.

    • Main Results:

    • The sensor achieved high recognition accuracies: 99.83% for material identification and 98.88% for user authentication in single-dimensional tasks.
    • The system maintained a recognition accuracy of 96.15% even in challenging environments.
    • The device effectively extracts material electronegativity and human kinetic information.

    Conclusions:

    • The developed self-powered bimodal sensor offers a robust technological foundation for future smart security.
    • This technology has potential applications in flexible electronic skins and personalized healthcare monitoring.
    • The study demonstrates a novel approach to human-machine interaction in security systems.