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Mechanoluminescent Visualization of Crack Propagation for Joint Evaluation
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Ultraprecise Sign Language Recognition Realized by Self-Recoverable Near-Infrared Mechanoluminescent Materials.

Xue Meng1, Panlai Li1, Mingxin Zhou1

  • 1National-Local Joint Engineering Laboratory of New Energy Photoelectric Devices, Hebei Key Laboratory of Optic-electronic Information and Materials, College of Physics Science & Technology, Hebei University, Baoding, China.

Advanced Materials (Deerfield Beach, Fla.)
|May 30, 2026
PubMed
Summary

Researchers developed self-healing near-infrared mechanoluminescence (ML) materials, ZnGa1-mAlmInO4:Cr3+, overcoming limitations of traditional sensors. These novel ML materials show high stability and self-powering capabilities for advanced human-machine interaction.

Keywords:
intelligent neural networksmechanoluminescencephosphor

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Area of Science:

  • Materials Science
  • Nanotechnology
  • Optoelectronics

Background:

  • Flexible sensors for human-machine interaction demand high stability, anti-interference, and self-powering capabilities.
  • Traditional electrical sensors face limitations in complex, long-term applications.
  • Existing mechanoluminescence (ML) materials require pre-radiation charging and lack cycling stability.

Purpose of the Study:

  • To develop novel self-recovering near-infrared (NIR) mechanoluminescent (ML) materials.
  • To enhance the performance and stability of ML materials for practical applications.
  • To explore the integration of ML materials with photoelectric sensors and neural networks.

Main Methods:

  • Synthesized a series of self-recovering NIR ML materials: ZnGa1-mAlmInO4:Cr3+.
  • Controlled crystal field strength by adjusting Al3+ doping concentration to enhance photoluminescence.
  • Tested material stability through thousands of mechanical stimulation cycles and integrated with photoelectric sensors for application testing.

Main Results:

  • Achieved a 40.65-fold enhancement in photoluminescence intensity by optimizing Al3+ doping.
  • The self-healing NIR ML material retained 98% of its initial luminescence intensity after thousands of cycles.
  • Demonstrated high accuracy (99.46%) in sign language recognition and effective intelligent road monitoring when integrated with photoelectric sensors and CNNs.

Conclusions:

  • ZnGa1-mAlmInO4:Cr3+ offers a promising solution for self-healing, stable, and efficient NIR ML materials.
  • The developed materials overcome key limitations of existing ML technologies.
  • This work provides a foundation for designing advanced NIR ML materials and their integration into intelligent systems.