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Optimized Multimetal Sensitized Phosphor for Enhanced Red Up-Conversion Luminescence by Machine Learning.

Fan Yang¹, Yanxing Wang¹, Xue Jiang¹

¹Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China.

ACS Combinatorial Science

|April 15, 2020

Summary

Machine learning optimized phosphors for enhanced red up-conversion luminescence (UCL) intensity. The genetic algorithm (GA) improved brightness 4.91-fold, quantum yield 6.40-fold, and tissue penetration depth by 5 mm.

Keywords:

machine learning metal modulation multisensitizer up-conversion luminescence

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Area of Science:

Materials Science
Optoelectronics
Computational Chemistry

Background:

Low up-conversion luminescence (UCL) intensity is a challenge in phosphor development.
Optimizing phosphor composition for enhanced red UCL emission is crucial for applications like bioimaging and lighting.

Purpose of the Study:

To address the low UCL intensity problem by finding optimal phosphors with enhanced red emission.
To utilize machine learning algorithms for efficient optimization of multielement phosphors.

Main Methods:

Employed machine learning algorithms, specifically the genetic algorithm (GA) and support vector machine (SVM).
Investigated multielement K/Li/Mn metal modulation for enhanced red UCL emission.
Studied the effect of single and multiple dopants (Yb/Er/K+Li+Mn) on upconversion intensity.

Main Results:

The GA optimized phosphors showed a 4.91-fold increase in brightness, a 6.40-fold higher relative quantum yield, and a 5 mm enhanced tissue penetration depth compared to first-generation phosphors.
Optimized K+Li+Mn concentration was found to be 6.03%, with intensity increasing and then decreasing with dopant content.
SVM algorithm confirmed the stability of GA-driven brightness optimization with high classification accuracy.

Conclusions:

Machine learning, particularly GA, is effective in optimizing phosphors for significantly enhanced red UCL properties.
The optimized phosphors demonstrate potential for advanced applications in bioimaging and phosphor-LEDs.
The study validates a computational approach for accelerated discovery of high-performance phosphors.