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

该项目涉及通过将温室气体二氧化碳电催化还原为甲醇来生产碳中性液体燃料。 具体而言，该研究旨在通过推进对水性电解质如何在原子尺度上参与整体反应机制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。 该研究将结合教育推动力，让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，和共溶质（即“局部溶剂化环境”）调节将CO2还原（CO2 R）成燃料的基本机制。 四个基本的科学问题将解决有关CO2 R在芳香族N-杂环的存在下，在这里研究的分子和氮掺杂的碳电极的形式。 这些是1）最可能的化学功能的鉴定（即刘易斯碱、布朗斯台德酸、H原子供体、氢化物供体）参与能量有效的CO2 R转化为甲醇; 2）定量预测不同局部溶剂化环境中不同CO2加氢过程的自由能垒; 3）精确理解在现实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;和4）对局部溶剂化环境可被调节以增强低价值含碳原料向液体燃料的转化的程度的一般性洞察。研究生和本科生将开发教育模块，结合联合收割机概念，在可再生能源和介绍计算化学建模。然后，这些模块将进行测试，以确定其吸引和激发匹兹堡公立学区学生对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.