2025/10/14 by William Schertzer, Schertzer, William, Mohamed Al Otmi +7 · 1 citation
Engineering · #Advanced Battery Technologies Research #FOS: Physical sciences #Fuel Cells and Related Materials #Membrane-based Ion Separation Techniques #Soft Condensed Matter (cond-mat.soft)
paper · pdf · doi:10.48550/arxiv.2510.12655
openalex publication_date 2025/10/14 · openalex created_date 2025/10/17 · openalex updated_date 2026/07/28
The global transition to hydrogen-based energy infrastructures faces significant hurdles. Chief among these are the high costs and sustainability issues associated with acid-based proton exchange membrane fuel cells. Anion exchange membrane (AEM) fuel cells offer promising cost-effective alternatives, yet their widespread adoption is limited by rapid degradation in alkaline environments. Here, we develop a framework that integrates mechanistic insights with machine learning, enabling the identification of generalized degradation behavior across diverse polymeric AEM chemistries and operating conditions. Our model successfully predicts long-term hydroxide conductivity degradation (up to 10,000 hours) from minimal early-time experimental data. This capability significantly reduces experimental burdens and may expedite the design of high-performance, durable AEM materials.