Pan Xin, Zhuang Yixi, He Wei, Lin Cunjian, Mei Lefu, Chen Changjian, Xue Hao, Sun Zhigang, Wang Chunfeng, Peng Dengfeng, Zheng Yanqing, Pan Caofeng, Wang Lixin, Xie Rong-Jun
School of Materials Sciences and Technology, China University of Geosciences Beijing, Beijing, China.
College of Materials, Xiamen University, Xiamen, China.
Nat Commun. 2024 Mar 26;15(1):2673. doi: 10.1038/s41467-024-46900-w.
Mechanoluminescence (ML) sensing technologies open up new opportunities for intelligent sensors, self-powered displays and wearable devices. However, the emission efficiency of ML materials reported so far still fails to meet the growing application requirements due to the insufficiently understood mechano-to-photon conversion mechanism. Herein, we propose to quantify the ability of different phases to gain or lose electrons under friction (defined as triboelectric series), and reveal that the inorganic-organic interfacial triboelectricity is a key factor in determining the ML in inorganic-organic composites. A positive correlation between the difference in triboelectric series and the ML intensity is established in a series of composites, and a 20-fold increase in ML intensity is finally obtained by selecting an appropriate inorganic-organic combination. The interfacial triboelectricity-regulated ML is further demonstrated in multi-interface systems that include an inorganic phosphor-organic matrix and organic matrix-force applicator interfaces, and again confirmed by self-oxidization and reduction of emission centers under continuous mechanical stimulus. This work not only gives direct experimental evidences for the underlying mechanism of ML, but also provides guidelines for rationally designing high-efficiency ML materials.
机械发光(ML)传感技术为智能传感器、自供电显示器和可穿戴设备带来了新机遇。然而,由于对机械到光子的转换机制理解不足,迄今为止报道的ML材料的发光效率仍无法满足不断增长的应用需求。在此,我们提出量化不同相在摩擦下获得或失去电子的能力(定义为摩擦电序列),并揭示无机-有机界面摩擦电是决定无机-有机复合材料中ML的关键因素。在一系列复合材料中建立了摩擦电序列差异与ML强度之间的正相关关系,最终通过选择合适的无机-有机组合使ML强度提高了20倍。在包括无机磷光体-有机基质和有机基质-力施加器界面的多界面系统中进一步证明了界面摩擦电调节的ML,并在连续机械刺激下通过发射中心的自氧化和还原再次得到证实。这项工作不仅为ML的潜在机制提供了直接的实验证据,还为合理设计高效ML材料提供了指导。