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

Mechanoluminescence (ML), the emission of light under mechanical stimuli, shows great potential in passive sensing, wearable devices, and biomedical diagnostics. However, the practical application of ML materials is hindered by low intensity and poor self-recoverable performance. Herein, a Mn⁴⁺→Mn²⁺ self-reduction strategy is presented to significantly enhance the self-recoverable ML performance of CaZnOS by inducing lattice defects and promoting distortion in its noncentrosymmetric hexagonal structure. This approach enhances the internal piezoelectric response and increases the maximum ML intensity up to 4 times. X-ray absorption near-edge structure, extended X-ray absorption fine structure, electron paramagnetic resonance, piezoresponse force microscopy, and density functional theory calculations reveal that the composite defects involving <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:msubsup><mml:mi>V</mml:mi> <mml:mi>O</mml:mi> <mml:mrow><mml:mo>·</mml:mo> <mml:mo>·</mml:mo></mml:mrow> </mml:msubsup> <mml:annotation>${\mathrm{V}}_{\mathrm{O}}^{\cdot \cdot}$</mml:annotation></mml:semantics> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:msubsup><mml:mi>V</mml:mi> <mml:mi>Zn</mml:mi> <mml:msup><mml:mrow></mml:mrow> <mml:mrow><mml:mo>'</mml:mo> <mml:mo>'</mml:mo></mml:mrow> </mml:msup> </mml:msubsup> <mml:annotation>${\mathrm{V}}_{{\mathrm{Zn}}}^{{\mathrm{^{\prime\prime}}}}$</mml:annotation></mml:semantics> </mml:math> are the key to the significant enhancement of ML. Furthermore, this strategy is successfully extended to rare-earth ions codoped systems, achieving a general enhancement of near-infrared ML emission. Based on these findings, a multilayer orthodontic sensor is developed, capable of real-time occlusal mapping and bite-force monitoring. The device exhibits sensitive response across 0-12 N and achieves 96.89% accuracy in occlusal localization through neuromorphic image recognition. This work offers a generalizable route toward ML performance optimization and paves the way for the development of advanced intelligent sensing technologies.