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A Novel Technique for Generating and Observing Chemiluminescence in a Biological Setting
Published on: March 9, 2017
Research progress in ratiometric mechanoluminescent materials
Haojin Liu1, Yuan Yin1, Ziyi Guo1
1School of Physics and Opto-Electronic Technology, Collaborative Innovation Center of Rare-Earth Optical Functional Materials and Devices Development, Baoji University of Arts and Sciences, Baoji, Shaanxi 721016, P. R. China. yinyuan8008@126.com.
Abstract:
Optical sensing based on luminescent materials provides a powerful route for detecting temperature, stress, radiation, and biological microenvironmental information. However, conventional single-intensity readouts are susceptible to excitation fluctuation, device inhomogeneity, environmental disturbance, and instrumental drift, which limits their quantitative reliability. Ratiometric mechanoluminescence (ML) has recently emerged as a self-referencing strategy by constructing two or more mechanically responsive emission channels and using intensity ratios, lifetime ratios, or color variation as output parameters. In this review, recent advances in ratiometric ML materials are summarized from the perspectives of crystal-field-environment regulation, trap-level-assisted regulation, physical-structure design, and multimodal external-field-assisted strategies. The underlying mechanisms, including stress-induced crystal-field modulation, valence-state-dependent emission, carrier trapping/release, energy transfer, and interfacial triboelectric excitation, are discussed in relation to signal differentiation and self-referencing. Representative applications in flexible electronic skins, dynamic anti-counterfeiting, industrial monitoring, structural health diagnosis, and ML-assisted thermometry are further highlighted. Finally, current challenges, including material universality, emission brightness, color distinguishability, pre-excitation dependence, device integration, and standardized evaluation, are discussed. Future perspectives on high-precision, visualized, and intelligent ratiometric ML sensing platforms are also proposed.

