ReviewRSC advances2026
Machine learning in next-generation AEM fuel cells: a systematic review.
Review in RSC advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fuel cells have lately garnered interest as a potentially advantageous technology for clean and efficient energy conversion. One type that has caught people's attention is the anion exchange membrane fuel cell (AEMFC), which can run on a variety of fuels and operates at low and high temperatures. Exploring its basic working principles, important materials, obstacles, and recent breakthroughs, this perspective presents a comprehensive introduction to AEMFC technology. The anion exchange membrane (AEM) and the electrodes of the AEMFC work together to improve the cell performance and the efficiency of the system as a whole. Furthermore, this review emphasizes the ways in which AEMFC technology is being improved by ML and AI technologies. Through the identification of crucial parameters and the improvement of the membrane electrode assembly (MEA), these technologies have the potential to optimize the performance of AEMFCs while drastically cutting down on the time and effort needed for experimental testing. Finally, we take a look at the possibilities and threats for further study of fuel cell technology-based sustainable energy generation using AEMs in conjunction with new electrode materials. This article introduces a structured framework and categorizes the following key concepts: need for anion exchange membranes (AEM) > mechanisms of anion conductivities > ORR (oxygen reduction reaction) > interfacial phenomena at the electrode-AEM interface > water management > integration of artificial intelligence (AI)/machine learning > neural networks (NN) > schemes for learning > predictive modeling > optimization algorithms and optimizing algorithms > AI in fault detection > AI in maintenance of fuel cells and in materials discovery.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.