Self‐Recoverable, Highly Repeatable, and Thermally Stable Mechanoluminescence for Dual‐Mode Information Storage and Photonic Skin Applications

Author:

Qin Shiye1,Wei Wanyuan1,Tian Birong123,Ma Zhidong1,Fang Shaofan2,Wang Yongsheng1,Zhang Jiachi3,Wang Zhaofeng12ORCID

Affiliation:

1. State Key Laboratory of Solid Lubrication Lanzhou Institute of Chemical Physics Chinese Academy of Sciences Lanzhou Gansu 730000 P. R. China

2. Shandong Laboratory of Advanced Materials and Green Manufacturing at Yantai Yantai Shandong 265503 P. R. China

3. National & Local Joint Engineering Laboratory for Optical Conversion Materials and Technology Lanzhou University Lanzhou Gansu 730000 P. R. China

Abstract

AbstractMechanically driven light generation is an intriguing phenomenon holding great promise in various fields. However, the existing mechanoluminescence (ML) materials always suffer from poor self‐recoverability, low repeatability, and environmental disturbance, severely hindering their practical applications. In this work, a self‐activating ML system based on interfacial triboelectrification is created by compositing the Ca6BaP4O17:0.02Ce3+ powders into a flexible polydimethylsiloxane (PDMS) matrix. With no need for pre‐irradiation, the composite film emits self‐activating and self‐charging ML simultaneously in response to the rubbing or stretching stimuli. Accordingly, the Ca6BaP4O17:0.02Ce3+/PDMS exhibits desirable self‐recovery and repeatable ML performance, which could still be recorded (or observed by naked eyes) even after ca. 1000 stretching cycles with a fast self‐recovery period of < 0.1 s. Furthermore, the Ca6BaP4O17:0.02Ce3+/PDMS possesses good thermal stability in a temperature range from 298 to 473 K. The developed Ca6BaP4O17:0.02Ce3+/PDMS is applicable to various fields, and the dual‐mode information storage and photonic skin devices are created as representatives. Compared to the existing oxide‐based ML materials (the ML signal disappears after only several or tens of rapid mechanics cycles), this work breaks through the bottleneck issues on the self‐recoverability, repeatability, and thermal stability, which significantly advances the ML field.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Gansu Province

Natural Science Foundation of Shandong Province

Publisher

Wiley

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