Size-Controllable Eu-MOFs through Machine Learning Technology: Application for High Sensitive Ions and...

Area of Science:

• Materials Science
• Chemistry
• Nanotechnology

Background:

• Metal-organic frameworks (MOFs) with aggregation-induced emission (AIE) show promise in energy and biomedical fields.
• Controlling MOF particle size is crucial for optimizing AIE and expanding applications.

Purpose of the Study:

• To synthesize Eu-MOFs with varied particle sizes by adjusting synthesis parameters.
• To investigate the relationship between synthesis parameters, particle size, and AIE activity using machine learning.

Main Methods:

• Single factor analysis and gradient boosting decision tree (GBDT) regression model were employed.
• GBDT model (R²=0.9535) identified Eu-precursor concentration as the dominant factor influencing particle size.
• Correlation analysis revealed synergistic effects of synthesis parameters on Eu-MOF particle size.

Main Results:

• Eu-MOF particle size was successfully controlled by manipulating synthesis parameters.
• Larger Eu-MOF particle sizes and enhanced structural stability led to higher AIE activities.
• A screen-printed pattern using "120-0.3-6" Eu-MOFs displayed bright red fluorescence.

Conclusions:

• Eu-MOF particle size significantly impacts AIE performance, with larger sizes yielding stronger emission.
• The developed Eu-MOFs demonstrate potential for fabricating fluorescent patterns and sensing various ions and citric acid.