Structural Investigation and Enriched Catalysis of Cu-Complex-Encapsulated Microporous Catalyst with Pragmatic...

Area of Science:

• Materials Science
• Catalysis
• Chemical Engineering

Background:

• Silicoaluminophosphates (SAPOs) are versatile porous materials with applications in separation, catalysis, and environmental remediation.
• Functionalization of SAPO materials can enhance their catalytic properties.
• Copper complexes are effective catalysts for various organic transformations.

Purpose of the Study:

• To develop a hybrid porous material by immobilizing a copper(II) complex onto base-functionalized SAPO molecular sieves.
• To evaluate the catalytic activity of the hybrid material in the ring-opening reaction of epoxides.
• To apply machine learning techniques for predicting catalytic performance and optimizing industrial applications.

Main Methods:

• Synthesis of a copper(II) complex using 2,9-dimethyl-1,10-phenanthroline and copper nitrate.
• Post-synthetic immobilization of the copper complex onto amine-functionalized SAPO-34 and SAPO-5.
• Characterization using single-crystal X-ray diffraction, powder X-ray diffraction, N2 adsorption-desorption, FTIR, NMR, SEM, and TGA.
• Evaluation of catalytic activity in epoxide ring-opening reactions.
• Application of machine learning models (linear regression, SVM, kNN) for prediction.

Main Results:

• The copper complex was structurally confirmed via single-crystal X-ray diffraction.
• Amine-functionalized SAPO materials effectively immobilized the copper complex, yielding active catalysts.
• High conversion efficiencies of 90% (SAPO-34) and 88% (SAPO-5) were achieved in epoxide ring-opening reactions.
• Support Vector Machine (SVM) and k-nearest neighbors (kNN) demonstrated strong predictive performance for conversion and selectivity.
• Validation of ML model accuracy using MSE, MAPE, and R score metrics.

Conclusions:

• The developed hybrid porous material demonstrates effective catalytic activity for epoxide ring-opening reactions.
• The integration of functionalized SAPOs with immobilized copper complexes offers a promising catalytic system.
• Machine learning tools can successfully predict and optimize catalytic performance, paving the way for industrial applications.