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

被引:10
作者
Wang, Chang'an [1 ]
An, Guoqing [1 ]
Lin, Jing [1 ]
Zhang, Xiaowei [2 ,6 ]
Liu, Zhiyuan [1 ]
Luo, Yibin
Liu, Siqi [1 ]
Cheng, Zhixiang [1 ]
Guo, Tingting [1 ]
Gao, Hongyi [1 ,4 ,7 ]
Wang, Ge [1 ,5 ,7 ]
Shu, Xingtian [3 ,8 ]
机构
[1] Univ Sci & Technol Beijing, Beijing Adv Innovat Ctr Mat Genome Engn, Sch Mat Sci & Engn, Beijing Key Lab Funct Mat Mol & Struct Construct, Beijing, Peoples R China
[2] Beijing Normal Univ, Inst Adv Mat, Beijing, Peoples R China
[3] SINOPEC, Res Inst Petr Proc, Beijing, Peoples R China
[4] SINOPEC Changling Branch Co, Yueyang, Peoples R China
[5] Univ Sci & Technol Beijing, Shunde Grad Sch, Shunde, Peoples R China
[6] Beijing Normal Univ, Inst Adv Mat, Beijing 100875, Peoples R China
[7] Univ Sci & Technol Beijing, Beijing Adv Innovat Ctr Mat Genome Engn, Sch Mat Sci & Engn, Beijing Key Lab Funct Mat Mol & Struct Construct, Beijing 100083, Peoples R China
[8] SINOPEC, Res Inst Petr Proc, Beijing 100083, Peoples R China
来源
AGGREGATE | 2023年 / 4卷 / 04期
关键词
1-hexene epoxidation; aromatic compounds; machine learning; selectivity; TS-1; zeolites; ASSISTED SYNTHESIS; OXIDATION; EPOXIDATION; TITANIUM; H2O2; CRYSTALLIZATION; NANOSHEETS; SITES;
D O I
10.1002/agt2.318
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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 cm(3)center dot 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.
引用
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页数:14
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