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

Electrosynthesis of Chemicals using Greener Feedstocks: Development of a Theoretical-Experimental Framework to Accelerate Electrocatalyst Discovery
使用绿色原料电合成化学品:开发理论实验框架以加速电催化剂的发现
批准号:
RGPIN-2022-04840
负责人:
Ponnurangam, Sathish
金额:
$2.04万
依托单位:
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2022
资助国家:
加拿大
项目状态:
已结题
起止时间:
2022-01-01 至 2023-12-31

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中文摘要
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英文摘要
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.
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