Optimization of Red Luminescent Intensity in Eu3+-Doped Lanthanide Phosphors Using Genetic Algorithm
Source abstract
Abstract
In this research, four steps including synthesis experiment, brightness evaluation, optimized calculation using brightness as fitness reference, and new calculated composition for the next preparation have been proceeded to find the brightest Eu3+ doped phosphors combined with chemical experiments and genetic algorithm (GA) calculation. The evolutionary operations, such as elitism, selection, crossover, and mutation, are applied to the compound combination. Feasible optimized combination would be obtained until the phosphor is found to be satisfactory. Through GA calculation and thd experimental process, the final luminescence enhancement factor of the optimal phosphor is up to 141% compared with the best one in the first generation. Thus, the GA calculation could be well applied to combinatorial chemistry to find the better phosphor. Additionally, the optimized phosphor is potentially applied as the fingerprint detection nanoparticle and dual-modal imaging agent of the CT/luminescent agent with high penetration and resolution.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Luminescence Properties of Advanced Materials · Luminescence and Fluorescent Materials · Radiation Detection and Scintillator Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an, Shaanxi 710071, China
Key Laboratory of Molecular Imaging of Chinese Academy of Sciences, Institute of Automation, Chinese Academy of Sciences, Beijing 100190 China
Funding
Ministry of Science and Technology of the People's Republic of China · Ministry of Education of the People's Republic of China · National Natural Science Foundation of China
References (32)
Show source-provided references
1. 10.1002/adma.20170313510.1002/adma.201703135 ↗
2. 10.1039/C7SC02217D10.1039/C7SC02217D ↗
3. 10.1038/nmat447610.1038/nmat4476 ↗
4. 10.1038/nmat342610.1038/nmat3426 ↗
5. 10.1021/cr400159410.1021/cr4001594 ↗
6. 10.1073/pnas.160939711310.1073/pnas.1609397113 ↗
7. 10.1038/nmat390810.1038/nmat3908 ↗
8. 10.1021/acsnano.6b0786610.1021/acsnano.6b07866 ↗
9. 10.1039/C4CS00293H10.1039/C4CS00293H ↗
10. 10.1038/nnano.2013.19910.1038/nnano.2013.199 ↗
11. 10.1021/nn506757x10.1021/nn506757x ↗
12. 10.1021/acs.chemrev.5b0014810.1021/acs.chemrev.5b00148 ↗
13. 10.1039/C4CS00178H10.1039/C4CS00178H ↗
14. 10.1002/adma.20140563410.1002/adma.201405634 ↗
15. 10.1039/C5CS00875A10.1039/C5CS00875A ↗
16. 10.1038/ncomms1043210.1038/ncomms10432 ↗
17. 10.1088/1361-6528/aa8a3d10.1088/1361-6528/aa8a3d ↗
18. 10.1016/j.biomaterials.2016.05.02610.1016/j.biomaterials.2016.05.026 ↗
19. 10.1002/adma.20100012810.1002/adma.201000128 ↗
20. 10.1016/j.biomaterials.2014.03.08010.1016/j.biomaterials.2014.03.080 ↗
21. 10.1002/anie.20130482210.1002/anie.201304822 ↗
22. 10.1039/C4CS00392F10.1039/C4CS00392F ↗
23. 10.1039/C7CC06753D10.1039/C7CC06753D ↗
24. 10.1039/C6CC00922K10.1039/C6CC00922K ↗
25. 10.1002/anie.20160442710.1002/anie.201604427 ↗
26. 10.1021/acs.chemmater.6b0524410.1021/acs.chemmater.6b05244 ↗
27. 10.1021/nn506361310.1021/nn5063613 ↗
28. 10.1021/ic402468k10.1021/ic402468k ↗
29. 10.1021/ja409865c10.1021/ja409865c ↗
30. 10.1021/cc100063x10.1021/cc100063x ↗
31. 10.1002/adfm.20110211810.1002/adfm.201102118 ↗
32. 10.1039/C5DT03604F10.1039/C5DT03604F ↗