High-throughput parallelized testing of membrane electrode assemblies for CO2 reduction

Abstract

High-throughput characterization of electrochemical reactions can accelerate discovery and optimization cycles, and provide the data required for further acceleration via machine-learning guided experiment planning. There are a range of high-throughput methods available for catalyst discovery. However, the development and testing of electrochemical systems – integrated electrocatalysts, membranes, and electrodes – currently relies on serial, labor-intensive lab processes. Membrane electrode assembly (MEA) cells have shown particular promise in carbon dioxide (CO₂) reduction, providing commercially viable reaction rates. Experimental testing of MEAs is slow, requiring a serial assembly process that can result in electrode compression levels that are non-uniform over the cell area and challenging to reproduce. Here we demonstrate a new MEA testing system that offers an accelerated, parallelized assembly process and enables high-throughput electrochemical system testing. The approach accelerates electrochemical system testing, controls compression and improves repeatability and reliability. We benchmark our system with CO₂ reduction to ethylene, running 10 MEA experiments in parallel, demonstrating an acceleration factor up to 12× over conventional approaches, and achieving a cell-to-cell gas selectivity deviation of ±2.5%.