Quantifying the interfacial triboelectricity in inorganic-organic composite mechanoluminescent materials

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 () and Rong-Jun Xie ()
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Xin Pan: China University of Geosciences Beijing
Yixi Zhuang: Xiamen University
Wei He: Xiamen University
Cunjian Lin: Japan Advanced Institute of Science and Technology
Lefu Mei: China University of Geosciences Beijing
Changjian Chen: Xiamen University
Hao Xue: Xiamen University
Zhigang Sun: Ningbo University
Chunfeng Wang: Shenzhen University
Dengfeng Peng: Shenzhen University
Yanqing Zheng: Ningbo University
Caofeng Pan: Chinese Academy of Sciences
Lixin Wang: Fudan University
Rong-Jun Xie: Xiamen University

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-46900-w

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