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.

该项目旨在增强对整体金属有机框架的结构和催化性能之间联系的了解。在此，我结合最新开发的实验和计算方法，依次深入了解合成条件对所得框架结构的影响以及框架的结构特性对其生物质转化催化性能的影响。 MOF）是一组新颖的多孔材料，由 Fairen-Jimenez 教授领导的剑桥大学吸附与先进材料首次报道。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 向工业转化中的一个主要问题提供了一种方法：将它们塑造成有用的材料高孔隙率，同时保持良好的填充性，从而保持高密度。所有这些特性，加上 MOF 固有的独特性能（例如前所未有的高表面积、催化单中心分散性和化学可调性），使得整体式 MOF 在催化应用中极具前景。在这里，本研究提出两种整体框架 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特性如何与其催化性能相互关联。当考虑到溶胶-凝胶整体合成的合成参数比用于标准 MOF 的传统溶剂热反应更复杂时，这一点尤其重要。在这种情况下，我们关于控制整体结构的知识甚至比我们对传统材料合成的知识还不够发达。为了解决上述挑战，我建议使用实验设计（DOE）和机器学习（ML）技术来直观地了解如何：1）控制整体框架的结构，特别是UiO型框架，以获得最佳结构性质，2) 控制整体式 MOF 的结构性质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高效催化剂。