Reducing energy requirements of gas separations by computational material design of metal-organic frameworks

通过金属有机框架的计算材料设计降低气体分离的能量需求

• 批准号: 2268783
• 金额: --
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2268783
• 关键词: Reducing; energy; requirements; gas; separations

Gas separations are some of the most energy-intensive chemical processes worldwide. The environmental footprint of these separations would be immensely reduced by replacing the conventional cryogenic distillation with an adsorption-based process [1]. The main reason why this has not yet happened is the lack of adsorbent materials with the ideal characteristics to make each gas separation process viable. This project aims to deploy a synergistic effort between experiments and computational modelling to explore new nanoporous Metal-Organic Framework (MOF) materials for challenging gas separations. These are crystalline organometalic frameworks that allow for tunable design of both pore structure and surface chemistry to suit particular applications. Exciting developments have been recently reported on using MOFs that contain Coordinatively Unsaturated Sites (CUS) [2]. These sites arise on MOF materials after the removal of solvent molecules attached to the metal sites during synthesis. Material activation thus yields "vacant" sites that can bind strongly and specifically to electron-donating molecules through coordination-type bonds, dramatically increasing the material's selectivity. The wide variety of different MOF structures that exist (~70000) or can potentially be synthesised (>500000) makes it impossible to screen over such a large number of materials using expensive and time-consuming experiments. Computational tools like molecular simulation allow fast and cheap screening of MOFs for a particular application [3]. However, the main hurdle preventing high-throughput screening efforts to be turned into de facto computational material design is the lack of appropriate molecular models that can accurately describe the interactions between gas molecules and the CUS. Indeed, it has been shown that conventional models are unable to handle this kind of interaction, and that multi-scale approaches that combine quantum chemistry (QM) with classical Monte Carlo (MC) simulations are needed to address such complex problems [4]. The present project will build upon a hybrid QM/MC model developed in the Jorge group [5] and apply it to predict adsorption in a large number of MOFs with CUS under industrially relevant separation conditions [1]. Case studies will involve separations in the presence of water vapour (e.g. carbon capture), which are among the most challenging processes to design. The results of these simulations will be used to train a machine-learning algorithm, in collaboration with the group of Gómez-Gualdrón at Colorado [3], to explore the full set of existing and hypothetical MOFs. Once a small subset of MOFs with the most promising performance is identified, they will be experimentally synthesised, characterised and tested for adsorption separations in the Fletcher lab, thus closing the loop on the material design cycle. [1] Sholl, and Lively; Nature 2016, 532, 435-437.[2] Bachman et al.; J. Am. Chem. Soc. 2017, 139, 15363-15370.[3] Anderson et al.; Chem. Mater. 2018, 30, 6325-6337.[4] Fischer et al.; Mol. Simul., 2014, 40, 537-556.[5] Jorge, M.; Ind. Eng. Chem. Res., 2014, 53, 15475-15487.

气体分离是全球能源密集型化学工艺之一。这些分离的环境足迹将通过用基于吸附的工艺代替传统的低温蒸馏而大大减少[1]。这种情况尚未发生的主要原因是缺乏具有理想特性的吸附剂材料，以使每个气体分离过程可行。该项目旨在部署实验和计算建模之间的协同努力，以探索新的纳米多孔金属有机框架（MOF）材料，用于具有挑战性的气体分离。这些是结晶有机骨架，其允许孔结构和表面化学的可调设计以适合特定应用。最近报道了使用含有配位不饱和位点（CUS）的MOF的令人兴奋的发展[2]。这些位点在合成过程中除去附着在金属位点上的溶剂分子后出现在MOF材料上。因此，材料活化产生“空”位点，这些位点可以通过配位型键与供电子分子强烈且特异性地结合，从而显著增加材料的选择性。 存在（~70000）或可以潜在地合成（>500000）的各种不同的MOF结构使得不可能使用昂贵且耗时的实验来筛选如此大量的材料。计算工具，如分子模拟，允许快速和廉价的筛选特定应用的MOFs [3]。然而，阻止高通量筛选工作转化为事实上的计算材料设计的主要障碍是缺乏能够准确描述气体分子与CUS之间相互作用的适当分子模型。事实上，已经表明传统模型无法处理这种相互作用，并且需要将联合收割机量子化学（QM）与经典蒙特卡罗（MC）模拟相结合的多尺度方法来解决此类复杂问题[4]。本项目将建立在Jorge组[5]开发的混合QM/MC模型的基础上，并将其应用于预测工业相关分离条件下大量含CUS的MOF中的吸附[1]。案例研究将涉及在存在水蒸气的情况下进行分离（例如碳捕获），这是最难设计的工艺之一。这些模拟的结果将用于训练机器学习算法，与科罗拉多的Gómez-Gualdrón小组合作[3]，探索现有和假设的MOF的全部集合。一旦确定了一小部分具有最有前途性能的MOFs，它们将在弗莱彻实验室进行实验合成、表征和吸附分离测试，从而完成材料设计周期的循环。[1]Sholl和Lively; Nature 2016，532，435-437. [2]Bachman等人; J. Am. 2017，139，15363-15370中所述。[3]安德森等人;化学材料2018，30，6325-6337. [4]Fischer等人;摩尔同时，2014，40，537-556。[5]Jorge，M.;工业工程化学研究，2014，53，15475-15487中所述。