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%.
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