4.7 Article

5d → 4f transition of a lanthanide-activated MGa2S4 (M = Ca, Sr) semiconductor for mechanical-to-light energy conversion mediated by structural distortion

Journal

DALTON TRANSACTIONS
Volume 51, Issue 27, Pages 10457-10465

Publisher

ROYAL SOC CHEMISTRY
DOI: 10.1039/d2dt00883a

Keywords

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Funding

  1. National Natural Science Foundation of China [61875136, 22075284, 51872287, U2030118]
  2. Youth Innovation Promotion Association CAS [2019304]
  3. Fundamental Research Project of Guangdong Province [2020A1515011315]
  4. Guangdong Provincial Science Fund for Distinguished Young Scholars [2022B1515020054]

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Materials exhibiting mechanoluminescence have significant applications in various electronic devices. This study presents a new family of semiconductor host materials and proposes a novel mechanism for mechanoluminescence, achieving efficient mechanical-to-light energy conversion.
Materials exhibiting mechanoluminescence (ML) are a class of smart materials capable of mechanical-to-light energy conversion. Thus, ML materials have been widely used in various electronic applications such as smart sensors, security systems, human-machine interfaces, and energy harvesting systems. Herein, we report a centrosymmetric ML semiconductor host material family MGa2S4 (M = Ca, Sr), which features in-layered structures constructed with unique distorted bi-tetrahedral [Ga2S2S4/2] lattice units. It exhibited similar structural characteristics to the well-known ML semiconductor host ZnS. Remarkably, the lanthanide ions of 5d -> 4f transition-activated hosts showed sensitive and high ML luminance under natural lighting upon mechanical stimulation; thus, an efficient mechanical-to-light energy conversion of a self-powered display was achieved. Moreover, because of structural distortion and strain-gradient-induced electrical polarization in the ML host material upon mechanical stimulation, a ML mechanism based on the synergy effect between local electronic polarization and flexoelectricity was proposed. This study facilitates a deeper understanding of the relationship between the structure and underlying ML, and promotes further development of ML-material-based products and technologies.

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