Aromatic compounds‐mediated synthesis of anatase‐free hierarchical TS‐1 zeolite: Exploring design strategies via machine learning and enhanced catalytic performance

Abstract

Abstract Simultaneous achievement of constructing mesopores and eliminating anatase is a long‐term pursuit for enhancing the catalytic performance of TS‐1. Here, we developed an aromatic compounds‐mediated synthesis method to prepare anatase‐free and hierarchical TS‐1 for olefin epoxidation. A series of hierarchical TS‐1 zeolites were prepared by introducing aromatic compounds containing different functional groups via the crystallization process. The formation of intercrystalline mesopores and insertion of titanium into framework were facilitated at different extent. The synergistic coordination of carboxyl and hydroxyl in aromatic compounds with Ti(OH)4 realizes the uniform distribution of titanium species and eliminates the generation of anatase. Noteworthily, eight machine learning models were trained to reveal the mechanism of additive functional groups and preparation conditions on anatase formation and microstructure optimization. The prediction accuracy of most models can reach more than 80%. Benefiting from the larger mesopore volumes (0.37 cm3·g−1) and higher content of framework Ti species, TS‐DHBDC‐48h samples exhibit a higher catalytic performance than other zeolites, giving 1‐hexene conversion of 49.3% and 1,2‐epoxyhenane selectivity of 99.9%. The paper provides a facile aromatic compounds‐mediated synthesis strategy and promotes the application of machine learning toward the design and optimization of new zeolites.

Keywords

• 1‐hexene epoxidation
• aromatic compounds
• machine learning
• selectivity
• TS‐1 zeolites