Feng Long, Zhou Yunpeng, Cheng Kaige, Song Hongxin, Guo Ziyi, Zhang Meiguang, Zhao Lei
School of Physics and Optoelectronic Technology, Collaborative Innovation Center of Rare-Earth Optical Functional Materials and Devices Development, Baoji University of Arts and Sciences, Baoji, Shaanxi 721016, China.
Inorg Chem. 2024 Dec 23;63(51):24342-24350. doi: 10.1021/acs.inorgchem.4c04348. Epub 2024 Dec 11.
Mechanoluminescent (ML) materials have shown promising applications in visualized mechanical sensing, imaging, and real-time monitoring because of their unique mechanics-optics conversion. However, the short emission lifetime of transient ML presents challenges in overcoming temporal and spatial constraints in practical applications. In this work, a mechanically charged persistent ML material was created by compositing the CaNaMg(PO):Tb and flexible poly(dimethylsiloxane) (PDMS) matrix. Without preirradiation, the composite film emits self-activating and persistent ML (Pers-ML), simultaneously responding to the rubbing or stretching stimuli. Mechanical stimulation not only generates transient ML but also effectively fills traps through excitation of the interface triboelectric field during the process. Once the mechanical stimulus is removed, the release of trapped carriers leads to Pers-ML. In addition, the CaNaMg(PO):Tb/PDMS also exhibits self-recoverable ML and good thermal stability in the range of 298-573 K. Compared to traditional ultraviolet (UV)-charged long-persistent luminescent materials, the self-charging Pers-ML proposed in this work overcomes the temporal and spatial challenges of transient ML, further expanding the application scope of ML materials.
由于其独特的力学-光学转换特性,机械发光(ML)材料在可视化机械传感、成像和实时监测方面展现出了广阔的应用前景。然而,瞬态ML的短发射寿命给实际应用中克服时间和空间限制带来了挑战。在这项工作中,通过将CaNaMg(PO):Tb与柔性聚二甲基硅氧烷(PDMS)基体复合,制备出了一种机械充电的持久性ML材料。在没有预辐照的情况下,复合薄膜发出自激活的持久性ML(Pers-ML),同时对摩擦或拉伸刺激做出响应。机械刺激不仅产生瞬态ML,还在过程中通过激发界面摩擦电场有效地填充陷阱。一旦去除机械刺激,陷阱中载流子的释放会导致Pers-ML。此外,CaNaMg(PO):Tb/PDMS在298 - 573 K范围内还表现出自恢复ML和良好的热稳定性。与传统的紫外(UV)充电长余辉发光材料相比,这项工作中提出的自充电Pers-ML克服了瞬态ML的时间和空间挑战,进一步拓展了ML材料的应用范围。