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。具体而言，该研究旨在通过提高对水电解质如何参与原子尺度上的整体反应机制的理解，提高当前溶剂基电化学过程的效率和选择性。这项研究将与教育重点相结合，让8-12年级的学生学习可再生能源催化和计算化学。该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，以定量地了解溶剂分子、盐和共溶质（即溶质）之间的相互作用。“局部溶剂化环境”)调节二氧化碳还原（CO2R）成燃料的基本机制。在芳香n杂环存在的情况下，将解决与CO2R相关的四个基本科学问题，这里以分子形式和氮掺杂碳电极的形式进行研究。这些包括：1)识别最可能的化学官能团（即刘易斯碱、Brønsted酸、h原子供体、氢化物供体），这些官能团参与了高效的CO2R转化为甲醇；2)定量预测了不同局部溶剂化环境下不同CO2加氢过程的自由能垒；3)提高了在实际电化学环境中可靠预测氢化热力学和动力学所需的计算建模水平；4)对局部溶剂化环境可以在多大程度上进行调整，以提高低价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和引入计算化学建模的教育模块。然后将对这些模块进行测试，以确定它们吸引和激发匹兹堡公立学区学生参与STEM领域机会的能力。

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.