Evidence map›Paper›PMID 41236123›Full record

ReviewAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

Improving Interlayer Interactions in Proton Exchange Membrane Fuel Cell through Carbon Nanotube-Based Catalyst Design.

Ling Ai, Zhen Xu, Stuart M Holmes, Heng Zhai

Abstract readReview
In one paragraph

Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ling AiDepartment of Chemical Engineering, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK.ORCID https://orcid.org/0009-0002-6293-9817
Zhen XuDepartment of Materials, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK.
Stuart M HolmesDepartment of Chemical Engineering, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK.
Heng ZhaiDepartment of Chemical Engineering, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK.ORCID https://orcid.org/0000-0002-8884-3058

Funding

UK Research Council EPSRC EP/W03395X/1
6 · The paper itself

Abstract

Proton exchange membrane fuel cells (PEMFCs) have gained significant attention due to their efficient use of hydrogen energy, high energy density, and zero emissions, making them ideal for future transportation and portable energy frameworks. Despite these advantages, challenges related to catalyst layer design, material durability, and cost-effectiveness persist. This paper reviews recent advancements in the application of carbon nanotubes (CNTs) in PEMFCs. The study highlights the integration of CNTs at various levels of the membrane electrode assembly (MEA), focusing on both covalent and non-covalent modification schemes. A particular emphasis is placed on vertically aligned carbon nanotube (VACNT) structures due to their potential to improve electron and mass transport pathways, leading to enhanced fuel cell performance and durability. Additionally, the synergistic effects of blending CNTs with carbon black (CB) are explored to address issues related to interlayer interactions and material conductivity. The review identifies gaps in current research, particularly in understanding the comprehensive impact of CNT modifications on the operational state of the MEA, and proposes new insights and strategies for material design aimed at facilitating the commercialization of PEMFC technology.

Indexed as

catalyst layer designCNTinterlayer interactionPEMFCsustainable energy

Identifiers

PMID41236123
PMCPMC12713058

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.