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中国物理学会期刊

高压对力致发光的调控: 总结与展望

CSTR:32037.14.aps.75.20251312

Regulation of high pressure on mechanoluminescence: Summary and Prospects

CSTR:32037.14.aps.75.20251312
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  • 力致发光(mechanoluminescence, ML)是一种在机械刺激下可直接发光的现象. 因其具备高空间选择性、快速响应及多模式发光协同等特性, 在结构健康监测、智能传感与光学防伪等领域展现出巨大的应用潜力. 然而, 该领域仍面临机理尚不明确、性能优异的可应用材料数量有限, 以及测试标准尚未建立等挑战. 近年来, 高压科学技术(尤其是动态加载技术)的持续突破与创新, 为ML研究提供了新的探索途径. 在GPa级高压条件下, 通过调控ML材料的原子间距、电子轨道和晶体结构, 不仅实现了对其发光强度与颜色的高效调控, 还成功捕获到从微秒到秒量级的发光动力学演化过程, 为揭示ML微观机制提供了关键实验数据支撑. 本文概述了高压技术在ML材料性能优化与机理研究方面的应用进展, 总结了其在提高发射强度、调控发光光谱、揭示动态过程等方面的成果, 并对未来高压力致发光研究的发展方向与挑战进行展望.

    Mechanoluminescence (ML) is a phenomenon in which photon emission is produced directly under mechanical stimulation. Owing to its high spatial selectivity, rapid response, and multimodal emission capabilities, ML exhibits great potential for applications in structural health monitoring, intelligent sensing, and optical anti-counterfeiting. However, due to the complexity of ML modes, categories, and underlying kinetic processes, the field still faces several challenges, including the lack of a well-established mechanism, the limited availability of high-performance ML materials, and the absence of standardized testing standards. Existing studies have demonstrated that crystal field strength, band structure, and lattice configuration play crucial roles in governing the ML properties. High-pressure, with its unique ability to adjust these physical quantities, undoubtedly provides new pathways for advancing ML research. Recent breakthroughs in rapid loading techniques have further enabled the exploration of ML behaviors under high-pressure conditions. In the GPa pressure range, modulations of interatomic distances, electronic orbitals, and crystal structures can not only effectively control emission intensity and color, but also capture ML kinetic processes in microsecond-to-second timescales, thereby supplying essential experimental data for revealing the microscopic mechanisms of ML. In this review, we first provide a brief overview of the historical development, classification, and mechanistic understanding of ML, as well as commonly used ML characterization methods under ambient and high-pressure conditions. Then, we summarize the recent progress of high-pressure techniques in optimizing ML performance and elucidating ML mechanisms, with a focus on enhancing emission intensity, adjusting spectral characteristics, and revealing dynamic processes. Finally, the future directions and challenges for high-pressure ML research are discussed.

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