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Mechanoluminescent (ML) materials, which have the ability to convert mechanical energy to optical energy, have found huge promising applications such as in stress imaging and anti-counterfeiting. However, the main reported ML phosphors are based on trap-related ones, thus hindering the practical applications due to the requirement of complex light pre-irradiation process. Here, a self-recoverable near infrared (NIR) ML material of LaAl1-xO3: xCr3+ (x=0.2 ​%, 0.4 ​%, 0.6 ​%, 0.8 ​%, 1.0 ​%, and 1.2 ​%) has been developed. Based on the preheating method and corresponding ML performance analysis, the influences of residual carriers are eliminated and the detailed dynamic luminescence process analysis is realized. Systematic experiments are conducted to reveal the origin of the ML emissions, demonstrating that ML is dictated more by the non-centrosymmetric piezoelectric crystal characteristic. In general, this work has provided significant references for exploring more efficient NIR ML materials, which may provide potential applications in anti-counterfeiting and bio-stress sensing.

Mechanoluminescent (ML) materials, which have the ability to convert mechanical energy to optical energy, have found huge promising applications such as in stress imaging and anti-counterfeiting. However, the main reported ML phosphors are based on trap-related ones, thus hindering the practical applications due to the requirement of complex light pre-irradiation process. Here, a self-recoverable near infrared (NIR) ML material of LaAl1-xO3: xCr3+ (x = 0.2 %, 0.4 %, 0.6 %, 0.8 %, 1.0 %, and 1.2 %) has been developed. Based on the preheating method and corresponding ML performance analysis, the influences of residual carriers are eliminated and the detailed dynamic luminescence process analysis is realized. Systematic experiments are conducted to reveal the origin of the ML emissions, demonstrating that ML is dictated more by the non-centrosymmetric piezoelectric crystal characteristic. In general, this work has provided significant references for exploring more efficient NIR ML materials, which may provide potential applications in anti-counterfeiting and bio-stress sensing. [ABSTRACT FROM AUTHOR]