Aromatic compounds‐mediated synthesis of anatase‐free hierarchical TS‐1 zeolite: Exploring design strategies via machine learning and enhanced catalytic performance
Chang'an Wang,
Guoqing An,
Jing Lin,
Xiaowei Zhang,
Zhiyuan Liu,
Yibin Luo,
Siqi Liu,
Zhixiang Cheng,
Tingting Guo,
Hongyi Gao,
Ge Wang,
Xingtian Shu
Affiliations
Chang'an Wang
Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing Key Laboratory of Function Materials for Molecule and Structure Construction, School of Materials Science and Engineering, University of Science and Technology Beijing Beijing P. R. China
Guoqing An
Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing Key Laboratory of Function Materials for Molecule and Structure Construction, School of Materials Science and Engineering, University of Science and Technology Beijing Beijing P. R. China
Jing Lin
Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing Key Laboratory of Function Materials for Molecule and Structure Construction, School of Materials Science and Engineering, University of Science and Technology Beijing Beijing P. R. China
Xiaowei Zhang
Institute of Advanced Materials, Beijing Normal University Beijing P. R. China
Zhiyuan Liu
Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing Key Laboratory of Function Materials for Molecule and Structure Construction, School of Materials Science and Engineering, University of Science and Technology Beijing Beijing P. R. China
Yibin Luo
Research Institute of Petroleum Processing, SINOPEC Beijing P. R. China
Siqi Liu
Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing Key Laboratory of Function Materials for Molecule and Structure Construction, School of Materials Science and Engineering, University of Science and Technology Beijing Beijing P. R. China
Zhixiang Cheng
Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing Key Laboratory of Function Materials for Molecule and Structure Construction, School of Materials Science and Engineering, University of Science and Technology Beijing Beijing P. R. China
Tingting Guo
Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing Key Laboratory of Function Materials for Molecule and Structure Construction, School of Materials Science and Engineering, University of Science and Technology Beijing Beijing P. R. China
Hongyi Gao
Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing Key Laboratory of Function Materials for Molecule and Structure Construction, School of Materials Science and Engineering, University of Science and Technology Beijing Beijing P. R. China
Ge Wang
Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing Key Laboratory of Function Materials for Molecule and Structure Construction, School of Materials Science and Engineering, University of Science and Technology Beijing Beijing P. R. China
Xingtian Shu
Research Institute of Petroleum Processing, SINOPEC Beijing P. R. China
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.
