Quantifying the interfacial triboelectricity in inorganic-organic composite mechanoluminescent materials.

IF 14.7 1区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Xin Pan, Yixi Zhuang, Wei He, Cunjian Lin, Lefu Mei, Changjian Chen, Hao Xue, Zhigang Sun, Chunfeng Wang, Dengfeng Peng, Yanqing Zheng, Caofeng Pan, Lixin Wang, Rong-Jun Xie
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Abstract

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.

Abstract Image

量化无机-有机复合机械发光材料的界面三电性。
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的机遇。然而,由于对机械光子转换机制了解不够,迄今报道的机械发光材料的发射效率仍无法满足日益增长的应用需求。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(定义为三电系列),并揭示了无机-有机界面三电性是决定无机-有机复合材料 ML 的关键因素。在一系列复合材料中,三电量序列差异与 ML 强度之间建立了正相关,通过选择合适的无机-有机组合,最终使 ML 强度提高了 20 倍。在包括无机荧光粉-有机基体和有机基体-施力器界面在内的多界面系统中,界面三电调节的 ML 得到了进一步证实,并再次通过在连续机械刺激下的自氧化和发射中心的减少得到了证实。这项工作不仅为 ML 的基本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。
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来源期刊
Nature Communications
Nature Communications Biological Science Disciplines-
CiteScore
24.90
自引率
2.40%
发文量
6928
审稿时长
3.7 months
期刊介绍: Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.
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