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Bimodal Mechanoluminescence Enables Multicolor Luminescent Tailing for Ultra-Accurate Loading Speed Visualization and

Shulong Chang1,2,3, Wenjin Liu3, Yalei Wang2

  • 1School of Advanced Manufacturing and Robotics, Peking University, Beijing, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|January 5, 2026
PubMed
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This study introduces MgF2 phosphors for mechanoluminescence (ML) sensing. These phosphors show speed-dependent color changes, enabling accurate visualization and real-time detection of loading speed using machine learning.

Area of Science:

  • Materials Science
  • Solid-State Physics
  • Sensing Technologies

Background:

  • Mechanoluminescence (ML) converts mechanical stress into light, offering a power-independent sensing method.
  • Integrating ML sensors with image analysis enables dynamic mechanical performance evaluation.
  • Characterizing ML lifetime has been a significant challenge in the field.

Purpose of the Study:

  • To develop novel MgF2 phosphors exhibiting dual-mode mechanoluminescence (instantaneous and persistent).
  • To investigate the speed-dependent mechanoluminescent properties of MgF2 for visualization.
  • To establish a machine learning-based approach for real-time loading speed detection using ML imaging.

Main Methods:

  • Solid-state sintering method for MgF2 phosphor synthesis.
Keywords:
dynamic mechanicsmachine learningmechanoluminescencemulticolor

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  • Characterization of ML properties under varying loading speeds, focusing on color distribution and intensity.
  • Development and integration of a machine learning algorithm with ML imaging for data analysis.

    • Main Results:

    • MgF2 phosphors demonstrated instantaneous blue ML and persistent orange ML without doping.
    • A unique speed-dependent ML color distribution was observed: blue under slow sliding, multicolor (blue/orange) under fast sliding.
    • Preliminary ML lifetime evaluation was achieved by analyzing luminescent trailing.
    • Real-time bearing speed detection with 100% accuracy was realized using ML algorithms and MgF2 ML imaging.

    Conclusions:

    • MgF2 phosphors offer a bimodal ML capability for advanced mechanical sensing.
    • The observed speed-dependent ML provides a visual method for loading speed detection and characterization.
    • This work presents a novel strategy for ultra-accurate loading speed detection and visualization, advancing ML-based dynamic mechanical sensing.