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Multilevel Trap‐Controlled Bright Mechanoluminescence of Sr/CaGa 2 S 4 : Eu 2+ for Stress Sensing Applications
Author(s) -
Shao Yuhe,
Chang Jianqing,
Liu Hongzhen,
Song Zhen,
Zhang JunCheng,
Liu Quanlin
Publication year - 2025
Publication title -
laser and photonics reviews
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.778
H-Index - 116
eISSN - 1863-8899
pISSN - 1863-8880
DOI - 10.1002/lpor.202500015
Subject(s) - mechanoluminescence , trap (plumbing) , stress (linguistics) , materials science , optoelectronics , physics , phosphor , linguistics , philosophy , meteorology
Abstract Brighter, recoverable, and more practical green mechanoluminescence (ML) materials are urgently required in diverse fields such as visual stress sensing and advanced display lighting. Ce 3+ /Eu 2+ doping, leveraging efficient f ‐ d transition, remains one of the most promising approaches for achieving high‐performance green ML. However, intense competition between afterglow and ML necessitates precise control of trap depths to develop high‐brightness and recoverable ML materials in Ce 3+ /Eu 2+ doped trap‐controlled systems. In this work, the concept of multilevel trap control is introduced into Eu 2+ ‐doped MGa 2 S 4 ( M = Sr, Ca) materials and, for the first time, elucidate the mechanism underlying irradiation‐induced ML recovery in powdered MGa 2 S 4 : Eu 2+ ( M = Sr, Ca) materials dominated by shallow‐level traps. By optimizing activator doping concentration, depth and distribution of multilevel traps are effectively fine‐tuned, achieving remarkable ML peak intensity compared with renowned SrAl 2 O 4 materials (~57.3% for SrAl 2 O 4 : Eu 2+ , Dy 3+ , and ~84.3% for (Sr, Ca)Al 2 O 4 : Eu 2+ , Dy 3+ ) while minimizing afterglow interference. Finally, the practical application potential of SrGa 2 S 4 :Eu 2+ is demonstrated by employing fluorescent coatings to visualize crack propagation and stress distribution in construction materials. This study explores broader potential applications of SrGa 2 S 4 : Eu 2 ⁺ in ML technologies and offers novel insights and strategies for developing high‐performance ML materials.
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