机器学习+荧光粉

Machine Learning for Analysis of Time-Resolved Luminescence Data

Opportunities for Next-Generation Luminescent Materials through
Artificial Intelligence

 

• Color Shift Failure Prediction for Phosphor-Converted White LEDs by Modeling Features of Spectral Power Distribution with a Nonlinear Filter Approach
• The separation between Sm2+ and hole traps can be described by a standard γ distribution. The photoionization of Sm2+ can be explained by dispersive first-order kinetics. []

玻璃老化

Predicting nonlinear physical aging of glasses from equilibrium relaxation via the material time