Advances in Palladium-Based Membrane Research: High-Throughput Techniques and Machine Learning Perspectives

Abstract

The separation of high-purity hydrogen from mixed gasses using dense metallic alloy membranes is essential for advancing a hydrogen-based economy. Palladium-based membranes exhibit outstanding catalytic activity and theoretically infinite hydrogen selectivity, but their high cost and limited performance in contaminant-rich environments restrict their widespread use. This study addresses these limitations by exploring strategies to develop cost-effective, high-performance alternatives. Key challenges include the vast compositional design space, lack of systematic design principles, and the slow pace of traditional material development. This review emphasizes the potential of high-throughput and combinatorial techniques, such as composition-spread alloy films and the statistical design of experiments (DoE), combined with machine learning and materials informatics, to accelerate the discovery, optimization, and characterization of palladium-based membranes. These approaches reduce development time and costs while improving efficiency. Focusing on critical properties such as surface catalytic activity, resistance to chemical and physical stresses, and the incorporation of low-cost base metals, this study introduces domain-specific descriptors to address data scarcity and improve material screening. By integrating computational and experimental methods, future research can identify hidden material correlations and expedite the rational design of next-generation hydrogen separation membranes.