Wang Tianli, Zhang Pengfei, Xiao Jianqiang, Guo Ziyi, Xie Xiongwu, Huang Jiahao, Zheng Jiaojiao, Xu Xuhui, Zhao Lei
School of Physics and Opto-Electronic Technology, Collaborative Innovation Center of Rare-Earth Optical Functional Materials and Devices Development, Baoji University of Arts and Sciences, Baoji, Shaanxi, 721016, P. R. China.
College of Materials Science and Engineering, Key Laboratory of Advanced Materials of Yunnan Province, Kunming University of Science and Technology, Kunming, Yunnan, 650093, P. R. China.
Adv Sci (Weinh). 2025 Jan;12(3):e2410673. doi: 10.1002/advs.202410673. Epub 2024 Nov 27.
Strain sensors utilizing mechanoluminescent (ML) materials have garnered significant attention and application due to their advantages, such as self-powering, non-contact operation, and real-time response. However, ML-based strain sensing techniques typically rely on the establishing of a mathematical relationship between ML intensity and mechanical parameters. The absolute ML intensity is vulnerable to environmental factors, which can result in measurement errors. Herein, an color-resolved visualized dynamic ML and self-referencing strain sensing is investigated in CaAl(PO): Tb, Mn. By analyzing the ML performance under various mechanical stimulations and adjustable strain parameters, a relationship between strain and the ML intensity ratio of Tb/Mn is aimed to bed established. This will enable the development of a self-referencing and visualized strain sensing technology. Through a comparison of luminescence characteristics under continuous mechanical stimulation (stretching) and continuous X-ray irradiation, it is discovered that the ratiometric dynamic ML is primarily driven by the dynamic filling and continuous release of carriers form traps, which compensates for the ML of Mn. Leveraging the self-referencing and color-resolved (from green to red) visualized ML characteristics, an application scenario for monitoring human joint movement is developed. This approach offers new insights into the use of dynamic ML materials in strain sensing and human-machine interaction.
利用机械发光(ML)材料的应变传感器因其自供电、非接触操作和实时响应等优点而备受关注并得到广泛应用。然而,基于ML的应变传感技术通常依赖于建立ML强度与机械参数之间的数学关系。绝对ML强度易受环境因素影响,这可能导致测量误差。在此,研究了在CaAl(PO):Tb,Mn中进行颜色分辨的可视化动态ML和自参考应变传感。通过分析各种机械刺激和可调应变参数下的ML性能,旨在建立应变与Tb/Mn的ML强度比之间的关系。这将推动自参考和可视化应变传感技术的发展。通过比较连续机械刺激(拉伸)和连续X射线照射下的发光特性,发现比率动态ML主要由陷阱中载流子的动态填充和持续释放驱动,这补偿了Mn的ML。利用自参考和颜色分辨(从绿色到红色)的可视化ML特性,开发了一种监测人体关节运动的应用场景。这种方法为动态ML材料在应变传感和人机交互中的应用提供了新的见解。