CAREER: SusChEM: Unlocking local solvation environments for energetically efficient hydrogenations with quantum chemistry

该项目致力于通过电催化将温室气体二氧化碳(CO2)还原为甲醇来生产碳中性液体燃料。具体地说，这项研究试图通过增进对水溶液如何参与原子尺度上的整体反应机理的理解，来提高当前基于溶剂的电化学过程的效率和选择性。这项研究将与教育推动相结合，使8-12年级的学生学习可再生能源催化和计算化学。研究的重点将是整合高级电子结构理论、分子动力学和机器学习，以定量了解溶剂分子、盐和共溶物(即“局部溶剂化环境”)之间的相互作用如何调节燃料中二氧化碳减少(CO2R)的基本机制。四个与芳香族N-杂环存在时的CO2R有关的基本科学问题将在这里以分子的形式和作为氮掺杂的碳电极来研究。这些是1)确定参与能量高效的CO2R转化为甲醇的最可能的化学官能团(即Lewis碱、Brnsted酸、H原子给体、氢化物给体)；2)在不同的局部溶剂化环境中对不同CO2加氢过程的自由能垒进行定量预测；3)对在现实的电化学环境中可靠地预测加氢热力学和动力学所需的计算模拟水平的精细理解；以及4)对局部溶剂化环境可以被调节的程度的概括的洞察，以促进低值含碳原料向液体燃料的转化。研究生和本科生将开发结合可再生能源概念的教学模块，并介绍计算化学建模。然后将对这些模块进行测试，以确定它们是否有能力让匹兹堡公立学区的学生了解STEM领域的机会并激发他们的兴趣。

The project addresses the production of carbon-neutral liquid fuels via electrocatalytic reduction of the greenhouse gas carbon dioxide (CO2) to methanol. Specifically, the study seeks to improve the efficiency and selectivity of current solvent-based electrochemical processes by advancing understanding of how aqueous electrolytes participate in the overall reaction mechanisms at the atomic scale. The research will be coupled with educational thrusts that engage students in grades 8-12 in learning about renewable energy catalysis and computational chemistry.The focus of the study will be to integrate high-level electronic structure theory, molecular dynamics, and machine learning to quantitatively understand how interactions between solvent molecules, salts, and co-solutes (i.e. "local solvation environments") regulate fundamental mechanisms of CO2 reduction (CO2R) into fuels. Four basic scientific questions will be addressed related to CO2R in the presence of aromatic N-heterocycles, here studied in the form of molecules and as nitrogen-doped carbon electrodes. These are 1) the identification of the most likely chemical functionalities (i.e. Lewis base, Brønsted acid, H-atom donor, hydride donor) that participate in energetically efficient CO2R into methanol; 2) quantitative predictions of the free energy barriers for different CO2 hydrogenation processes in different local solvation environments; 3) refined understanding of the level of computational modeling needed to reliably predict hydrogenation thermodynamics and kinetics in realistic electrochemical environments; and 4) generalized insight into the degree to which local solvation environments can be tuned to enhance the conversion of low-value carbon-containing feedstocks to liquid fuels. Graduate and undergraduate students will develop educational modules that combine concepts in renewable energy and introduce computational chemistry modeling. These modules will then be tested to determine their capacity to engage and excite students in the Pittsburgh Public School District about opportunities in STEM fields.