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Related Experiment Video

Updated: Jan 13, 2026

Hybrid Printing for the Fabrication of Smart Sensors
08:35

Hybrid Printing for the Fabrication of Smart Sensors

Published on: January 31, 2019

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Fully-Printed Optical-Electric Dual Mode Flexible Sensor.

Hui Zhou1,2, Yue Qiu3, Pei He3

  • 1Hunan Key Laboratory for Super-microstructure and Ultrafast Process, School of Physics, Central South University, Changsha, China.

Advanced Materials (Deerfield Beach, Fla.)
|January 9, 2026
PubMed
Summary

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This summary is machine-generated.

Researchers developed a novel flexible sensor using mechanoluminescence ink for enhanced smart home control. This optical-electric dual-mode sensor offers high-resolution stress detection, improving human-computer interaction security.

Area of Science:

  • Materials Science
  • Sensor Technology
  • Human-Computer Interaction

Background:

  • Smart home control systems (SHCS) rely on human-computer interaction (HCI) interfaces for control.
  • Current HCI interfaces have security vulnerabilities due to static and singular data collection.
  • There is a need for advanced HCI interfaces with enhanced sensing capabilities.

Purpose of the Study:

  • To develop a high-performance mechanoluminescence (ML) ink for advanced sensing applications.
  • To create an optical-electric dual-mode flexible sensor (OEDM-FS) for precise stress detection.
  • To enhance security and functionality of HCI interfaces in SHCS.

Main Methods:

  • Formulation of high-resolution ZnS:Mn2+ mechanoluminescence (ML) ink (100 µm resolution).
Keywords:
high spatial resolutionhuman‐computer interactionlow thresholdmechanoluminescencestress concentration

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  • Fabrication of an OEDM-FS using 2D and 3D printing, integrating a capacitive pressure sensor (CPS) and ML sensor.
  • Utilized machine learning algorithms to analyze ML signals for stress distribution identification.

    • Main Results:

    • Achieved a low stress detection threshold of 0.01 MPa with the ML ink.
    • The OEDM-FS demonstrated high stability (±0.3% signal fluctuation over 10,000 cycles) and high-resolution stress mapping (200 µm).
    • Machine learning accurately identified stress distribution with 98.5% accuracy.

    Conclusions:

    • The developed OEDM-FS offers a robust and high-resolution sensing solution.
    • This technology significantly improves stress detection accuracy and spatial resolution for HCI.
    • The OEDM-FS shows great potential for secure and advanced HCI applications in smart home control systems.