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

本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，我将最新发展的实验和计算方法结合起来，依次深入了解合成条件对所获得的框架结构的影响，以及框架的结构性质对其催化生物质转化性能的影响。单片金属有机框架（MOFs）是一种新型的多孔材料，由剑桥大学Fairen-Jimenez教授领导，在《吸附与先进材料》杂志上首次报道。这些单片材料的魅力源于其独特的特性，包括在溶胶-凝胶合成方法中合成纳米MOF颗粒，为解决MOF向工业转化的主要问题提供了一条前进的道路：它们被塑造成具有高孔隙率的有用材料，同时保持良好的包装和高密度。所有这些特性，加上mof固有的独特特性（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mof在催化应用中非常有前景。在这里，本研究提出了两个整体框架，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与它们的催化性能是如何相互关联的。考虑到溶胶-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，这一点尤为重要。在这种情况下，我们对控制单片结构的知识甚至比我们对传统材料合成的知识更不发达。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)控制单片框架的结构，特别是uio型框架，以获得最佳的结构特性，以及2)控制单片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有效的化学反应催化剂。

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.