Boosting Mechanoluminescence Performance in Doped CaZnOS by the Facile Self-Reduction Approach

Area of Science:

• Materials Science
• Solid State Physics
• Nanotechnology

Background:

• Mechanoluminescence (ML) materials emit light under mechanical stress, offering potential for sensing and diagnostics.
• Current ML materials suffer from low intensity and poor self-recovery, limiting practical applications.

Purpose of the Study:

• To enhance the self-recoverable mechanoluminescence (ML) performance of CaZnOS materials.
• To investigate a Mn⁴⁺→Mn²⁺ self-reduction strategy for improving ML properties.
• To develop advanced ML-based sensing devices.

Main Methods:

• Mn⁴⁺→Mn²⁺ self-reduction strategy inducing lattice defects and structural distortion.
• Characterization techniques: X-ray absorption near-edge structure (XANES), extended X-ray absorption fine structure (EXAFS), electron paramagnetic resonance (EPR), piezoresponse force microscopy (PFM).
• Density functional theory (DFT) calculations.
• Development of a multilayer orthodontic sensor.

Main Results:

• Achieved a 4-fold increase in ML intensity with significantly enhanced self-recoverable performance.
• Identified composite defects ($V_O^{··}$ and $V_{\mathrm{Zn}}^{{}^{\text{'}\text{'}}}$) as key to ML enhancement.
• Successfully extended the strategy to rare-earth ions codoped systems for near-infrared ML emission.
• Developed an orthodontic sensor with sensitive bite-force monitoring (0-12 N) and high accuracy (96.89%) in occlusal localization.

Conclusions:

• The Mn⁴⁺→Mn²⁺ self-reduction strategy provides a generalizable route for optimizing ML performance.
• This approach enhances piezoelectric response and ML intensity through defect engineering.
• The developed ML sensor demonstrates potential for advanced intelligent sensing technologies in healthcare.