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Perspectives on Graphene-Based Materials in Polymer Electrolyte Membrane Fuel Cells: Improved Membrane Electrode Assembly and Sustainable Biographene Alternatives

IMPACT SIGNAL70/100
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Information from the abstract

Abstract Polymer electrolyte membrane fuel cells (PEMFCs) are energy conversion devices that harness clean energy from electrochemical reactions, consistent with Sustainable Development Goal 7 (SDG 7). The core of PEMFCs, known as the membrane electrode assembly (MEA), is where the major processes occur and is highly dependent on the material used. Therefore, the selection of materials for MEA development is vital as inefficient materials may lead to reduced performance. In this context, one material that stands out is graphene, a carbon allotrope, owing to its good mechanical and electrochemical properties. Hence, this review aims to highlight recent findings on the development of graphene-based MEAs for improved PEMFC performance. Systematically arranged according to the three major components in the MEA, this review focuses on graphene-based diffusion layers, catalysts, and membranes, in which the first, despite gaining attention in fuel cell research, is usually excluded from the other two in review discussions. Additionally, this paper addresses the current barriers and future potential of graphene-based MEA studies, particularly the potential of biomass-derived graphene or biographene development. Overall, the discussions on graphene-based MEAs presented in this paper can be a beneficial guide for future research related to graphene development to enhance MEA performance in PEMFCs.

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Why this record is monitored

This record has an Impact Signal of 70/100 based on recency, source, collaboration, and bibliographic signals. It prioritizes monitoring and is not a judgment of research quality.

Related topics: Fuel Cells and Related Materials · Electrocatalysts for Energy Conversion · Graphene research and applications

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Thai researcher and institutional participation

Mahnoush Beygisangchin · Chiang Mai University

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Data limitations

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