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Pushing the Boundaries of CO2 electrocatalysis to fuels using Automatized Catalyst Development (ElectroFuel)

21st national competition for scientific and technical research

Climate Change and Renewable Energy

Senior Researcher : Julio Lloret-Fillol

Research Centre or Institution : Institut Català d'Investigació Química (ICIQ)

Abstract

Developing greener production methods is essential for a future sustainable society. On this ground, electrochemical processes powered by renewable energy sources (sunlight, wind, etc.) can transform abundant molecules (water, CO2, etc.) into synthetic fuels and chemicals sustainably.

In particular, electrocatalytic CO2 reduction (CO2RR) promises to deliver sustainable and economically viable industrial production of renewable CO2-neutral fuels. However, current electrocatalysts still lack the requirements for industrial development. In this regard, ElectroFuel will develop new highly active, selective, and robust catalysts based on covalent organic frameworks for electrocatalytic CO2RR to learn basic principles of the CO2 reduction mechanisms that will guide its rational design.

However, discovering new catalysts is a demanding, tedious, and time-consuming task that requires decades of research and development. The time and cost of bringing advanced catalysts to market can be reduced by embracing high throughput screening (HTE), autonomous laboratory, digitalization, and artificial intelligence. Automating the research at the laboratory is the first and key step towards this goal. For example, addressing the automatic electrocatalytic CO2RR requires a detailed analysis of reaction samples to reach detection limits.

In ElectroFuel, we envision accelerating the catalyst discovery by HTE and autonomous study of electrocatalytic reactions for CO2RR, and by digitalizing the chemical research related to electrochemistry. ElectroFuel plans to construct an automated platform for high-throughput experimentation in CO2 reduction research. The platform will be built to be open and replicable. Data will be stored and available for machine learning.

 

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