In Situ Fluorescence Detection for the High-Throughput Screening of Oxygen Evolution Catalysts

The oxygen evolution reaction (OER, 4OH^- → O₂ + 2H₂O + 4e^- in alkaline electrolytes) is common to many sustainable processes critical for decarbonization, including water electrolysis to produce H₂as an energy carrier, CO₂ reduction to produce liquid fuels and value-added hydrocarbons, and electrolysis of metal oxides to produce pure metals. However, even the most active OER catalysts that have been discovered are at least an order of magnitude less active than oxygen evolving complexes in biological systems,¹ demonstrating the potential that OER catalysts could achieve with the correct choice in materials. With state-of-the-art OER catalysts becoming increasingly complex to improve their activity and stability, the status quo of experimental materials discovery (where Edisonian-like, model system-based, or descriptor-based approaches have dominated past breakthroughs) limits the chemical space that can be effectively explored.² These human intuition driven approaches can also lead to biases in how the available composition space is traversed, resulting in systematic exclusions of entire classes of materials.² Furthermore, with the recent advent of high-throughput virtual screening methods for materials discovery harnessing the predictive power of machine learning and artificial intelligence (ML/AI), generating high-quality data of material properties at scale is pivotal to ensure accurate and high-fidelity predictions. As such, recent years has seen the emergence of high-throughput experimental methods for the screening and/or testing of a large chemical space (e.g., &gt;10⁶ unique compositions)³that would be intractable to assess using traditional experimental evaluation methods in efforts to discover new materials or provide the large volume of high-quality data necessary and evaluate predictions for ML/AI models.<br/><br/>In this work, we present a custom-built fluorescence-based screening setup for the testing of OER electrocatalysts with a minimum O₂ detection limit of 50 nmols. We demonstrate that our setup can not only evaluate the OER activity of discrete compositional arrays but can also be used to measure the OER activity of continuous compositional gradient films of various facets, allowing for the screening of a vast compositional space and active site motifs with near infinite compositional resolution simultaneously. Through in situ fluorescence intensity kinetic measurements coupled with electrochemical OER, we devised a method to directly relate the measured fluorescence intensity of tested samples with the rate of oxygen evolution. We applied the setup to composition gradient films of La_0.6Sr_0.4Co_1-xFe_xO₃ with (001) and (111) facets and found that a maximum in OER activity is achieved when x ≈ 0.2, where the most active composition is over 50x more active than that of the least. We further utilized density functional theory calculations of select equi-spaced compositions within the La_0.6Sr_0.4Co_1-xFe_xO₃ gradient to assess the accuracy of the fluorescence setup and demonstrate how such a setup can effectively accelerate both experimental and computational materials discovery workflows for OER electrocatalysts.<br/><br/><b>References</b><br/><br/>1. Kuznetsov, D. A.; Han, B.; Yu, Y.; Rao, R. R.; Hwang, J.; Román-Leshkov, Y.; Shao-Horn, Y. Tuning Redox Transitions via Inductive Effect in Metal Oxides and Complexes, and Implications in Oxygen Electrocatalysis. <i>Joule</i> <b>2018</b>, <i>2</i> (2), 225–244.<br/>2. Peng, J.; Schwalbe-Koda, D.; Akkiraju, K.; Xie, T.; Giordano, L.; Yu, Y.; Eom, C. J.; Lunger, J. R.; Zheng, D. J.; Rao, R. R.; Muy, S.; Grossman, J. C.; Reuter, K.; Gómez-Bombarelli, R.; Shao-Horn, Y. Human- and Machine-Centred Designs of Molecules and Materials for Sustainability and Decarbonization. <i>Nat. Rev. Mater. 2022 712</i> <b>2022</b>, <i>7</i> (12), 991–1009.<br/>3. Davies, D. W.; Butler, K. T.; Jackson, A. J.; Morris, A.; Frost, J. M.; Skelton, J. M.; Walsh, A. Computational Screening of All Stoichiometric Inorganic Materials. <i>Chem</i> <b>2016</b>, <i>1</i> (4), 617–627.