Mechanoluminescence (ML) materials are featured with the characteristic of “force to light” in response to external stimuli, which have made great progress in artificial intelligence and optical sensing. However, how to effectively enable ML in the material is a daunting challenge. Here, a Lu 3 Al 2 Ga 3 O 12 :Cr 3+ (LAGO: Cr 3+ ) near infrared (NIR) ML material peaked at 706 nm is reported, which successfully realizes the key to unlock ML by the lattice-engineering strategy Ga 3+ substitution for Al 3+ to “grow” oxygen vacancy (O v ) defects. Combined with thermoluminescence measurements, the observed ML is due to the formation of defect levels and the ML intensity is proportional to it. It is confirmed by X-ray photoelectron spectroscopy and electron paramagnetic resonance that such a process is dominated by O v , which plays a crucial role in turning on ML in this compound. In addition, potential ML emissions from 4 T 2 and 2 E level transitions are discussed from both experimental and theoretical aspects. This study reveals the mechanism of the change in ML behavior after cation substitution, and it may have important implications for the practical application of O v defect-regulated turn-on of ML.
Shuaishuai Yu,Shaofan Fang,Lei Zhao,Yongqing Bai,Runkang Wang,Zhaofeng Wang
Mechanoluminescence (ML) materials have shown widespread application prospects in various fields like structural health monitoring, biomechanics sensing, wearable electronics, passive displaying and imaging. However, very limited ML materials can emit near-infrared light, and most of the ML materials need a pre-charging process, which severely restrict the practical operability. In this work, the attractive near-infrared ML (NIR-ML) of a transition metal ion-doped gallium, SrGa 12 O 19 :Cr 3+ , is reported. This material exhibits self-recoverable and cyclically stable NIR-ML, overcoming the pre-charging and degradation limitations in the conventional systems. Through the study of thermoluminescence, cathodoluminescence and triboelectric properties, it is proved that the ML performance mainly comes from the electron bombardment process induced by the interfacial triboelectrification. Moreover, a self-activation strategy is designed to further enhance the NIR-ML intensity about 250 times, based on which the dual-mode mechanics visualization and biomechanics detection applications are achieved.
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