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Aromatic compounds-mediated synthesis of anatase-free hierarchical TS-1 zeolite: Exploring design strategies via machine learning and enhanced catalytic performance
Aggregate ( IF 13.9 ) Pub Date : 2023-01-28 , DOI: 10.1002/agt2.318 Chang'an Wang 1 , Guoqing An 1 , Jing Lin 1 , Xiaowei Zhang 2 , Zhiyuan Liu 1 , Yibin Luo 3 , Siqi Liu 1 , Zhixiang Cheng 1 , Tingting Guo 1 , Hongyi Gao 1, 4 , Ge Wang 1, 5 , Xingtian Shu 3
Aggregate ( IF 13.9 ) Pub Date : 2023-01-28 , DOI: 10.1002/agt2.318 Chang'an Wang 1 , Guoqing An 1 , Jing Lin 1 , Xiaowei Zhang 2 , Zhiyuan Liu 1 , Yibin Luo 3 , Siqi Liu 1 , Zhixiang Cheng 1 , Tingting Guo 1 , Hongyi Gao 1, 4 , Ge Wang 1, 5 , Xingtian Shu 3
Affiliation
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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.
中文翻译：
芳香族化合物介导的无锐钛矿分级 TS-1 沸石的合成：通过机器学习和增强催化性能探索设计策略
同时实现构建介孔和消除锐钛矿是增强TS-1催化性能的长期追求。在这里，我们开发了一种芳香族化合物介导的合成方法来制备用于烯烃环氧化的无锐钛矿和分级的TS-1。通过结晶过程引入含有不同官能团的芳香族化合物，制备了一系列多级孔TS-1沸石。不同程度地促进了晶间介孔的形成和钛嵌入骨架中。芳香族化合物中羧基和羟基与Ti(OH) 4的协同配位实现了钛物种的均匀分布，消除了锐钛矿的产生。值得注意的是，训练了八个机器学习模型，以揭示添加官能团和制备条件对锐钛矿形成和微观结构优化的机制。大多数模型的预测准确率可以达到80%以上。受益于较大的介孔体积（0.37 cm 3 ·g -1）和较高的骨架Ti物质含量，TS-DHBDC-48h样品表现出比其他沸石更高的催化性能，1-己烯转化率为49.3%和1,2 -环氧己烷选择性为99.9%。该论文提供了一种简便的芳香族化合物介导的合成策略，并促进了机器学习在新型沸石设计和优化中的应用。
更新日期：2023-01-28
中文翻译：
芳香族化合物介导的无锐钛矿分级 TS-1 沸石的合成：通过机器学习和增强催化性能探索设计策略
同时实现构建介孔和消除锐钛矿是增强TS-1催化性能的长期追求。在这里，我们开发了一种芳香族化合物介导的合成方法来制备用于烯烃环氧化的无锐钛矿和分级的TS-1。通过结晶过程引入含有不同官能团的芳香族化合物，制备了一系列多级孔TS-1沸石。不同程度地促进了晶间介孔的形成和钛嵌入骨架中。芳香族化合物中羧基和羟基与Ti(OH) 4的协同配位实现了钛物种的均匀分布，消除了锐钛矿的产生。值得注意的是，训练了八个机器学习模型，以揭示添加官能团和制备条件对锐钛矿形成和微观结构优化的机制。大多数模型的预测准确率可以达到80%以上。受益于较大的介孔体积（0.37 cm 3 ·g -1）和较高的骨架Ti物质含量，TS-DHBDC-48h样品表现出比其他沸石更高的催化性能，1-己烯转化率为49.3%和1,2 -环氧己烷选择性为99.9%。该论文提供了一种简便的芳香族化合物介导的合成策略，并促进了机器学习在新型沸石设计和优化中的应用。
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