Electrosynthesis of Chemicals using Greener Feedstocks: Development of a Theoretical-Experimental Framework to Accelerate Electrocatalyst Discovery

向可持续未来的过渡需要能源和材料制造部门的绿色化。实现这一转变需要解决的两个关键挑战是：(1)用地球上丰富的良性替代品替代构成安全供应风险或环境问题的关键和有毒能源材料（如贵金属、稀土元素）；(2)用地球上丰富的无毒替代品（如CO2、H2O和N2）取代传统的基于化石燃料的原料（如氢、乙烯、CO）。为了解决这些重大的技术挑战，并绕过传统的、昂贵的（电）催化剂开发的试错方法，PI的团队正在开发一种结合实验和理论的筛选方法。这种方法是与大学内外的研究小组合作开发的，用于发现能量转换和存储材料，如非均相电催化剂和热催化剂，电池阴极和电解质以及燃料电池膜电极组件。具体来说，拟议的研究计划的目标是通过将高通量建模方法（密度泛函数理论（DFT））的数据与实验表征（电化学和电子显微镜，x射线光电子和振动光谱，温度编程技术，孔径和表面积测量以及实验尺度电催化性能）的结果相结合来加速材料的开发过程。机器学习模型将被开发和使用，以减少这种高通量材料筛选过程所需要的大量维度。特别是，该方法将用于发现一种多功能电催化剂，用于尿素合成，该催化剂使用绿色原料CO2， H2O和N2在一个锅中。拟议的计划与加拿大的健康环境和健康经济气候计划非常一致，该计划的目标是到2050年实现净零排放，同时保持经济繁荣。考虑到加拿大尿素生产的高二氧化碳足迹（每吨尿素产生5.15吨二氧化碳当量）和对农业和加工食品工业的高度依赖，减少尿素生产中的二氧化碳排放可以显著促进加拿大的气候变化目标。更广泛地说，使用可替代绿色原料的（电）催化工艺的发展有助于阿尔伯塔省推动能源转型和领导地位。

A transition to sustainable future requires greening of energy and material manufacturing sectors. Two key challenges to be addressed for such a transition are, (1) substitute critical and toxic energy materials (e.g., precious metals, rare earth elements) that pose security-supply risks or environmental concerns by earth-abundant benign alternatives, (2) replace traditional fossil fuel-based feedstocks (e.g., hydrogen, ethylene, CO) for chemicals manufacturing with earth-abundant non-toxic alternatives such as CO2, H2O, and N2. To address these grand technological challenges and to by-pass the classical and costly trial-and-error approach for (electro)catalyst development, the PI's group, is developing a combined experimental and theoretical screening approach. This approach is being developed in collaboration with research groups across and outside the university for the discovery of energy conversion and storage materials such as heterogeneous electrocatalysts and thermocatalysts, battery cathodes and electrolytes as well as fuel-cell membrane-electrode assemblies. Specifically, the goal of the proposed research program is to accelerate the materials development process by integrating data from high-throughput modeling methods (density functional theory (DFT)) in conjunction with results from experimental characterization (electrochemical and electron microscopies, x-ray photoelectron and vibrational spectroscopies, temperature programmed techniques, pore size and surface area measurements, and bench-scale electrocatalytic performance). Machine learning models will be developed and employed to reduce the enormous number of dimensions such a high-throughput material screening process entail. In particular, this methodology will be used for the discovery of a multi-functional electrocatalyst for urea synthesis using green feedstock CO2, H2O, and N2 in one pot. The proposed program aligns well Canada's climate plan of Healthy Environment and a Healthy Economy, which targets a net-zero emission by 2050 while maintaining the economic prosperity. Given the high CO2 footprint for urea manufacturing (5.15 ton of CO2 equiv/ton of urea) and high dependence of agricultural and processed food industry in Canada, reducing CO2 emission in urea manufacturing can significantly contribute to the climate change goals of Canada. More generally, development of (electro)catalytic processes that can use the alternative green feedstocks can contribute to Alberta's drive towards energy transition and leadership.