Acquiring chemical intuition into the catalytic properties of UiO-type monolithic frameworks using machine learning techniques

使用机器学习技术获得对 UiO 型整体框架催化特性的化学直觉

• 批准号: EP/Y023447/1
• 负责人: David Fairen-Jimenez
• 金额: $ 25.55万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY023447%2F1
• 关键词: Acquiring; chemical; intuition; into; catalytic

This project aims to enhance the knowledge about the link between the structural and catalytic properties of monolithic metal-organic frameworks. Herein, I combine the latest developed experimental and computational methods to gain insights into the effect of synthetic conditions on the structure of the obtained frameworks and the structural properties of the frameworks on their catalytic properties for biomass conversion, consecutively.Monolithic metal-organic frameworks (MOFs) are a novel group of porous materials that were first reported in the Adsorption & Advanced Materials, University of Cambridge, led by Prof. Fairen-Jimenez. The fascination around these monolithic materials stems from their unique features, including the synthesis of nanosized MOF particles during the sol-gel synthesis method, providing a way forward to unlock a major issue in the translation of MOFs to industry: their shaping into useful materials with high porosity while maintaining excellent packing and therefore high density. All these properties, on top of the inherent unique properties of MOFs (e.g. unprecedented high surface areas, catalytic single-site dispersion, and chemical tunability) make monolithic MOFs extremely promising for catalytic applications. Here, two monolithic frameworks, UiO-66 and UiO-67, are proposed in this study to be used for biomass conversion, i.e. glyoxal conversion to glycolic acid. This project aims to solve two important challenges: a) how to control the structure of these monolithic frameworks by tuning the synthetic conditions; b) how are the chemo-structural properties of the monolithic frameworks interrelated with their catalytic performance. This is particularly important when considering that the synthetic parameters for the sol-gel monolithic synthesis are more complicated than conventional solvothermal reactions used for standard MOFs. In this case, our knowledge about controlling monolithic structures is even less developed than the knowledge we have on the synthesis of conventional materials. To solve the above challenges, I propose to use design of experiments (DOE) followed by machine learning (ML) techniques to gain intuition into how to: 1) control the structure of monolithic frameworks, especially UiO-type frameworks, to gain optimal structural properties, and 2) gain control over the structural properties of monolithic MOFs and their functionality for glyoxal conversion to glycolic acid. Such knowledge will be further used to design and synthesize efficient catalysts for chemical reactions.

该项目旨在提高对整体金属有机框架的结构和催化性能之间联系的认识。在此，我联合收割机结合最新的实验和计算方法，以获得洞察的影响，合成条件对所获得的框架结构和框架的结构性质对其催化性能的生物质转化，连续monolithic metal-organic frameworks（MOFs）是一类新型的多孔材料，首先报道在吸附与先进材料，剑桥大学，由Fairen-Jimenez教授领导。围绕这些整体材料的魅力源于其独特的功能，包括在溶胶-凝胶合成方法中合成纳米级MOF颗粒，为解决MOF转化为工业的主要问题提供了一种方法：将其成型为具有高孔隙率的有用材料，同时保持良好的填充和高密度。所有这些性质，加上MOFs固有的独特性质（例如前所未有的高表面积，催化单位点分散和化学可调性），使得单片MOFs非常有希望用于催化应用。在此，本研究中提出了两种整体框架UiO-66和UiO-67用于生物质转化，即乙二醛转化为乙醇酸。该项目旨在解决两个重要的挑战：a）如何通过调整合成条件来控制这些整体框架的结构; B）整体框架的化学结构性质如何与其催化性能相关。当考虑到用于溶胶-凝胶整体合成的合成参数比用于标准MOF的常规溶剂热反应更复杂时，这是特别重要的。在这种情况下，我们关于控制整体结构的知识甚至不如我们在常规材料合成方面的知识发达。为了解决上述挑战，我建议使用实验设计（DOE），然后使用机器学习（ML）技术来获得如何实现以下目标的直觉：1）控制整体框架的结构，特别是UIO型框架，以获得最佳的结构特性，以及2）控制整体MOF的结构特性及其将乙二醛转化为乙醇酸的功能。这些知识将进一步用于设计和合成化学反应的有效催化剂。