Recent Advances in Mechanoluminescence of Doped Zinc Sulfides

Mechanoluminescence (ML) is the light generation of a material under mechanical stimuli. For more than 400 years, many compounds including inorganic and organic materials present this phenomenon. The general mechanism for the ML emission is the stress‐induced electrification effect combined with the...

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Bibliographic Details
Published in:Laser & photonics reviews 2021-12, Vol.15 (12), p.n/a
Main Authors: Qasem, Abdulkareem, Xiong, Puxian, Ma, Zhijun, Peng, Mingying, Yang, Zhongmin
Format: Article
Language:English
Subjects:
Current carriers
Display devices
Electrification
Mechanoluminescence
Organic materials
Piezoelectricity
sensor
Synthesis
trap
Zinc compounds
Zinc sulfide
ZnS
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Summary:Mechanoluminescence (ML) is the light generation of a material under mechanical stimuli. For more than 400 years, many compounds including inorganic and organic materials present this phenomenon. The general mechanism for the ML emission is the stress‐induced electrification effect combined with the piezoelectric field that lead to trapping and detrapping of the charge carriers. However, an in‐depth investigation and research are required to understand and hypothesize the ML mechanisms and optimum design and synthesis methods for better ML properties. Among the most common materials possessing this phenomenon is the zinc sulfide (ZnS) compounds that show high luminescence intensity and reproducibility. Such properties make them best candidates to be used in potential applications such as light and display devices, stress sensors, and human health monitoring devices. Therefore, this minireview focuses on the recent development and progress in doped ZnS ML compounds, including mechanism, design and synthesis, and the practical applications of such materials. This review summarizes the recent development and progress in mechanoluminescence (ML) of doped ZnS, concerning the design and synthesis methods, mechanism, characterization instrumentations, and its applications. It further explains and tries to understand how this phenomenon works aiming at hypothesizing ML principles and developing brand new ML materials.
ISSN:1863-8880
1863-8899
DOI:10.1002/lpor.202100276