Computational modelling of advanced electrocatalysts for CO2 electroreduction using DFT and machine learning.

Carbon capture and utilization (CCU), primarily the CO2 electroreduction technology, can convert CO2 into a variety of valuable products, using renewable electricity. However, the path to widespread adoption of the CO2 electroreduction technology in industrial settings is met with several challenges primarily the cost of electricity, efficiency and selectivity of the desired product.
One of the key factors that can help address these challenges and promote industrial application of this process is the choice of a proper electrocatalyst used in the CO2 electrolyzer. The role of electrocatalysts is pivotal, and their design is central to making the CO2 electroreduction technology both sustainable and economically viable. In light of these issues, this proposal focuses on the development of an innovative approach towards the design of efficient electrocatalysts which offer higher selectivity, lower consumption of electricity and increased efficiency by using Density Functional Theory (thermodynamic and microkinetic approach) and machine learning techniques.
This project will serve as the first step in initiating student exchange and research cooperation between Concordia University and Technical University of Denmark. By focusing on sustainability, the research aligns with sustainability science, addressing environmental concerns and guiding eco-friendly technology development in both Canada and Denmark.

Faculty Supervisor:

Yaser Khojasteh-Salkuyeh

Student:

Partner:

Technical University of Denmark

Discipline:

Engineering

Sector:

Education

University:

Concordia University

Program: