Electrochemical CO2 reduction is emerging as a compelling route for renewable energy storage and carbon neutrality. Focus on improving catalyst selectivity and energy efficiency resulted in a surge of catalysis-centered research. The advent of artificial intelligence and high-throughput screening enables parallelized catalyst characterization to accelerate discovery, but their implementation into application-relevant device configurations is challenging. We present a scalable, high-throughput platform based on infrared thermography that preserves realistic electrochemical environments from lab to industrially relevant scales. We demonstrate the spatial and electrochemical homogeneity of a 16-well parallel electrolyzer and validate a combinatorial testing approach using copper-based catalysts with varied loadings and precursor chemistries. The results highlight how activity trends can be rapidly mapped under controlled conditions, while also revealing the limitations of activity-only combinatorial testing, particularly for multiproduct electrochemical applications in complex environments like CO2 electrolysis on Cu. This platform thus provides an efficient pre-screening tool to accelerate catalyst discovery when analyzed appropriately and paired with follow-up single catalyst testing.
Montfort et al. (2026) studied this question.