Trap-controlled mechanoluminescent materials

Mechanoluminescence (ML) is generated during exposures of certain materials to mechanical stimuli. Many solid materials produce ML during their fracturing, however, the irreversibility of fracto-induced ML limits the practical applications of these materials. In 1999, Chao-Nan Xu discovered an inten...

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Bibliographic Details
Published in:Progress in materials science 2019-06, Vol.103, p.678
Main Authors: Zhang, Jun-Cheng, Wang, Xusheng, Marriott, Gerard, Xu, Chao-Nan
Format: Article
Language:English
Subjects:
Biomechanics
Composite materials
Design optimization
Light sources
Materials science
Mechanoluminescence
Sensors
Skin
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Summary:Mechanoluminescence (ML) is generated during exposures of certain materials to mechanical stimuli. Many solid materials produce ML during their fracturing, however, the irreversibility of fracto-induced ML limits the practical applications of these materials. In 1999, Chao-Nan Xu discovered an intense and reproducible ML from trap-controlled materials, including ZnS:Mn2+ and SrAl2O4:Eu2+, and introduced the principles and applications of hybrid inorganic/organic mechanoluminescent (ML) composites, and related sensors to visualize stress/strain in target structures. This discovery has triggered intense research interest in trap-controlled ML materials and composites over the past 2 decades. Notable achievements of this research include the development of trap-controlled materials that exhibit bright ML emission from the ultraviolet to the near infra-red, and multiscale mechano-optical sensitivities. This research has also increased our understanding of the mechanisms of ML phenomena, enabling the rational design of trap-controlled ML materials. Practical applications of ML are also being driven by the discovery that ML composites can serve as “mechano-optical sensitive skin” for structural health diagnosis, stress sensors for biomechanics, and mechanically-activated light sources. This review focuses on the design, synthesis, characterization, optimization and application of trap-controlled ML materials, and concludes with discussions on future directions of ML research and specific challenges to improve ML materials for real-world applications.
ISSN:0079-6425
1873-2208
DOI:10.1016/j.pmatsci.2019.02.001