TY - JOUR A1 - Abed, Jehad A1 - Bai, Yanh A1 - Persaud, Daniel A1 - Kim, Jiheon A1 - Witt, Julia A1 - Hattrick.Simpers, Jason A1 - Sargent, Edward T1 - AMPERE: automated modular platform for expedited and reproducible electrochemical testing N2 - Rapid and reliable electrochemical screening is critical to accelerate the development of catalysts for sustainable energy generation and storage. This paper introduces an automated and modular platform for expedited and reproducible electrochemical testing (AMPERE), designed to enhance the efficiency and reliability of multivariate optimization. The platform integrates a liquid-handling robot with custommade modular array reactors, offering sample preparation and electrochemical testing in the same platform. Additionally, we use offline inductively coupled plasma optical emission spectroscopy (ICPOES) to measure metal concentrations in the electrolyte after the reaction, which serves as a proxy for assessing the electrochemical stability. We use the platform to conduct 168 experiments continuously in less than 40 hours to examine the influence of catalyst ink formulation on the performance of Ir, Ru, IrO2, and RuO2 for the oxygen evolution reaction (OER) in acid. We specifically investigate the role of solvent type and concentration, catalyst concentration, and binder content on the performance. We find that Ru/RuO2 catalysts show improvements in activity that are not directly linked to improvements in the electrochemical surface area or inversely correlated to Ru dissolution. This suggests a complex interplay between the catalytic performance of the drop-casted catalyst film and ink formulation. AMPERE simplifies catalyst preparation and testing at large scale, making it faster, more reliable, and accessible for widespread use. KW - Electrochemical screening KW - Automated platform KW - Catalysis KW - Energy generation and storage KW - Oxygen evolution reaction (OER) KW - MAPz@BAM PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-614724 DO - https://doi.org/10.1039/d4dd00203b SP - 1 EP - 10 PB - Royal Society of Chemistry (RSC) AN - OPUS4-61472 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Zhang, Runze A1 - Sur, Debashish A1 - Li, Kangming A1 - Witt, Julia A1 - Black, Robert A1 - Whittingham, Alexander A1 - Scully, John R. A1 - Hattrick-Simpers, Jason T1 - Bayesian assessment of commonly used equivalent circuit models for corrosion analysis in electrochemical impedance spectroscopy N2 - Electrochemical Impedance Spectroscopy (EIS) is a crucial technique for assessing corrosion of metallic materials. The analysis of EIS hinges on the selection of an appropriate equivalent circuit model (ECM) that accurately characterizes the system under study. In this work, we systematically examined the applicability of three commonly used ECMs across several typical material degradation scenarios. By applying Bayesian Inference to simulated corrosion EIS data, we assessed the suitability of these ECMs under different corrosion conditions and identified regions where the EIS data lacks sufficient information to statistically substantiate the ECM structure. Additionally, we posit that the traditional approach to EIS analysis, which often requires measurements to very low frequencies, might not be always necessary to correctly model the appropriate ECM. Our study assesses the impact of omitting data from low to medium-frequency ranges on inference results and reveals that a significant portion of low-frequency measurements can be excluded without substantially compromising the accuracy of extracting system parameters. Further, we propose simple checks to the posterior distributions of the ECM components and posterior predictions, which can be used to quantitatively evaluate the suitability of a particular ECM and the minimum frequency required to be measured. This framework points to a pathway for expediting EIS acquisition by intelligently reducing low-frequency data collection and permitting on-the-fly EIS measurements. KW - Electrochemical Impedance Spectroscopy (EIS) KW - MAPz@BAM KW - Bayesian Inference KW - Corrosion PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-623392 DO - https://doi.org/10.1038/s41529-024-00537-8 VL - 8 SP - 120 PB - Springer Materials AN - OPUS4-62339 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -