ArticleThe journal of physical chemistry. B2026
AI-Assisted Physics-Informed Predictions of Degradation Behavior of Polymeric Anion Exchange Membranes.
Article in The journal of physical chemistry. B, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- AI in Membrane Design and Optimization for Hydrogen Fuel Cells.Membranes · 2026Review
- Machine learning in next-generation AEM fuel cells: a systematic review.RSC advances · 2026Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 h) from minimal early time experimental data. This capability significantly reduces experimental burdens and may expedite the design of high-performance, durable AEM materials.
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Registered trials
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