Journal
SMALL
Volume 17, Issue 46, Pages -Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/smll.202103441
Keywords
heterostructures; mechanoluminescence; repeatable energy conversion; self-recoverable modes; trap-controllable modes
Categories
Funding
- Natural Science Foundation of China [11804255, 12074298, 21771156]
- Natural Science Foundation of Jiangsu Province [BK20190212]
- Early Career Scheme (ECS) fund from the Research Grant Council (RGC) in Hong Kong [PolyU 253026/16P]
- Japan Society for the Promotion of Science (JSPS) [19H00835, 17H06374]
- Grants-in-Aid for Scientific Research [19H00835] Funding Source: KAKEN
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Mechanoluminescence (ML) is achieved through mechanical to optical energy conversion. The synthesis of Li0.5Na0.5NbO3: Pr3+ effectively couples trap structures and piezoelectric property to realize highly repeatable ML performance without traditional preirradiation process. Doping of Na+ ions in LiNbO3 optimizes trap distributions and concentrations, improving electron transfer efficiency to promote ML performances.
Mechanoluminescence (ML) is a striking optical phenomenon that is achieved through mechanical to optical energy conversion. Here, a series of Li1-xNaxNbO3: Pr3+ (x = 0, 0.2, 0.5, 0.8, 1.0) ML materials have been developed. In particular, due to the formation of heterostructure, the synthesized Li0.5Na0.5NbO3: Pr3+ effectively couples the trap structures and piezoelectric property to realize the highly repeatable ML performance without traditional preirradiation process. Furthermore, the ML performances measured under sunlight irradiation and preheating confirm that the ML properties of Li0.5Na0.5NbO3: Pr3+ can be ascribed to the dual modes of luminescence mechanism, including both trap-controllable and self-recoverable modes. In addition, DFT calculations further confirm that the doping of Na+ ions in LiNbO3 leads to electronic modulations by the formation of the heterostructures, which optimizes the trap distributions and concentrations. These modulations improve the electron transfer efficiency to promote ML performances. This work has supplied significant references for future design and synthesis of efficient ML materials for broad applications.
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