Structural Investigation and Enriched Catalysis of Cu-Complex-Encapsulated Microporous Catalyst with Pragmatic Modeling for Prediction of Activity by Using Machine Learning

Silicoaluminophosphates (SAPOs) are structurally diverse materials widely used in separation, catalysis, and environmental applications. In this study, a simple post-synthetic method is used to create a hybrid porous material by immobilizing a copper(II) complex onto base-functionalized SAPO molecular sieves. The copper complex, synthesized using 2,9-dimethyl-1,10-phenanthroline and copper nitrate, is structurally confirmed through single-crystal X-ray diffraction. The effective activity in ring-opening reaction of epoxide is achieved when this complex is anchored on amine-functionalized SAPO materials. Characterization techniques such as powder X-ray diffraction, N₂ adsorption-desorption, Fourier transform infrared spectroscopy, nuclear magnetic resonance, scanning electron microscope, and thermogravimetric analysis confirm the structural integrity, surface properties, and thermal stability of the materials. High conversion efficiencies of 90% and 88% are achieved using copper-complex-immobilized SAPO-34 and SAPO-5, respectively. To enhance industrial applicability, machine learning techniques are applied to predict product conversion and selectivity. Methods such as linear regression, support vector machine (SVM), and k-nearest neighbors (kNN) are evaluated, with SVM and kNN showing strong predictive performance. Error metrics like mean-squared error, mean absolute percentage error, and R score validate the model accuracy. This work highlights the effective integration of functionalized SAPOs with ML tools for catalytic optimization and industrial-scale applications.