Study of the Relationship between Synthesis Descriptors and the Type of Zeolite Phase Formed in ZSM-43 Synthesis by Using Machine Learning

摘要

Small-pore zeolites possess pores that are constructed of eight tetrahedral atoms. ZSM-43 is a small-pore zeolite with two- dimensional eight-ring channels. The preparation of ZSM-43 is influenced by the molar concentration of hydroxide ions, choline based organic structure-directing agents (OSDA) and inorganic structure-directing agents (ISDA) such as sodium, potassium and cesium. The synthetic conditions yield a range of products such as ZSM-43, amorphous, UZM-15, and other zeolites. There is a significant challenge in relating synthetic descriptors to their implications in zeolite phase formation. As a proof-of-concept study, we correlated the type of product formed with the gel molar composition in complex ZSM-43 synthesis using machine learning algorithms. Seven different supervised machine learning algorithms have demonstrated an accuracy of >95 percent and an F1 score of >95 percent. The decision tree algorithm (DT) demonstrates the relationship between what type of phase is produced by what concen- tration of ISDA and hydroxide ions, as well as the effects of changing these parameters on phase transformation. DT provides structural and physicochemical insights into zeolite chemistry. From the experimental data of ZSM-43, it is difficult to obtain detailed information regarding the chemistry of all phase formations. However, machine learning algorithms aid in recognizing hidden patterns in the data, facilitating deeper understanding of zeolite chemistry and phase transformations.