Optimization of Red Luminescent Intensity in Eu3+-Doped Lanthanide Phosphors Using Genetic Algorithm.
Ruichan LvLiyang XiaoXue JiangMiao FengFan YangJie TianPublished in: ACS biomaterials science & engineering (2018)
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.
Keyphrases
- energy transfer
- quantum dots
- pet ct
- genome wide
- sensitive detection
- monte carlo
- machine learning
- light emitting
- deep learning
- high resolution
- physical activity
- computed tomography
- copy number
- metal organic framework
- body composition
- highly efficient
- loop mediated isothermal amplification
- high intensity
- single molecule
- randomized controlled trial
- image quality
- real time pcr
- gene expression
- study protocol