Understanding the Effects of Anode Catalyst Conductivity and Loading on Catalyst Layer Utilization and Performance for Anion Exchange Membrane Water Electrolysis.

Anion exchange membrane water electrolysis (AEMWE) is a promising technology to produce hydrogen from low-cost, renewable power sources. Recently, the efficiency and durability of AEMWE have improved significantly due to advances in the anion exchange polymers and catalysts. To achieve performances and lifetimes competitive with proton exchange membrane or liquid alkaline electrolyzers, however, improvements in the integration of materials into the membrane electrode assembly (MEA) are needed. In particular, the integration of the oxygen evolution reaction (OER) catalyst, ionomer, and transport layer in the anode catalyst layer has significant impacts on catalyst utilization and voltage losses due to the transport of gases, hydroxide ions, and electrons within the anode. This study investigates the effects of the properties of the OER catalyst and the catalyst layer morphology on performance. Using cross-sectional electron microscopy and in-plane conductivity measurements for four PGM-free catalysts, we determine the catalyst layer thickness, uniformity, and electronic conductivity and further use a transmission line model to relate these properties to the catalyst layer resistance and utilization. We find that increased loading is beneficial for catalysts with high electronic conductivity and uniform catalyst layers, resulting in up to 55% increase in current density at 2 V due to decreased kinetic and catalyst layer resistance losses, while for catalysts with lower conductivity and/or less uniform catalyst layers, there is minimal impact. This work provides important insights into the role of catalyst layer properties beyond intrinsic catalyst activity in AEMWE performance.

Protocol for assembling and operating bipolar membrane water electrolyzers.

Renewable energy-driven bipolar membrane water electrolyzers (BPMWEs) are a promising technology for sustainable production of hydrogen from seawater and other impure water sources. Here, we present a protocol for assembling BPMWEs and operating them in a range of water feedstocks, including ultra-pure deionized water and seawater. We describe steps for membrane electrode assembly preparation, electrolyzer assembly, and electrochemical evaluation. For complete details on the use and execution of this protocol, please refer to Marin et al. (2023).1.

PolyID: Artificial Intelligence for Discovering Performance-Advantaged and Sustainable Polymers.

A necessary transformation for a sustainable economy is the transition from fossil-derived plastics to polymers derived from biomass and waste resources. While renewable feedstocks can enhance material performance through unique chemical moieties, probing the vast material design space by experiment alone is not practically feasible. Here, we develop a machine-learning-based tool, PolyID, to reduce the design space of renewable feedstocks to enable efficient discovery of performance-advantaged, biobased polymers. PolyID is a multioutput, graph neural network specifically designed to increase accuracy and to enable quantitative structure-property relationship (QSPR) analysis for polymers. It includes a novel domain-of-validity method that was developed and applied to demonstrate how gaps in training data can be filled to improve accuracy. The model was benchmarked with both a 20% held-out subset of the original training data and 22 experimentally synthesized polymers. A mean absolute error for the glass transition temperatures of 19.8 and 26.4 °C was achieved for the test and experimental data sets, respectively. Predictions were made on polymers composed of monomers from four databases that contain biologically accessible small molecules: MetaCyc, MINEs, KEGG, and BiGG. From 1.4 × 106 accessible biobased polymers, we identified five poly(ethylene terephthalate) (PET) analogues with predicted improvements to thermal and transport performance. Experimental validation for one of the PET analogues demonstrated a glass transition temperature between 85 and 112 °C, which is higher than PET and within the predicted range of the PolyID tool. In addition to accurate predictions, we show how the model's predictions are explainable through analysis of individual bond importance for a biobased nylon. Overall, PolyID can aid the biobased polymer practitioner to navigate the vast number of renewable polymers to discover sustainable materials with enhanced performance.