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Record W4412866371 · doi:10.1088/2515-7655/adf6e3

Ionomer-free electrodes in PEM water electrolyzers: a critical review

2025· review· en· W4412866371 on OpenAlexfundno aff
Mengyan Chen, ChungHyuk Lee, Jason Keonhag Lee

Bibliographic record

VenueJournal of Physics Energy · 2025
Typereview
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsnot available
FundersNational Research Foundation of KoreaNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsIonomerElectrodeProton exchange membrane fuel cellMaterials scienceEngineeringFuel cellsComposite materialChemistryChemical engineering

Abstract

fetched live from OpenAlex

Abstract Achieving large-scale production of clean hydrogen, which emits zero local-carbon emissions when powered by renewables, is a prerequisite to advance the hydrogen economy and to delay the escalating global temperatures. While proton-exchange-membrane water electrolyzers (PEMWEs) are projected to play a vital role for the market, the technology still encounters challenges associated with cost and scale-up. One viable approach is to reduce the amount of platinum-group-metal usage in the PEMWE. Recent studies have introduced novel electrode designs that eliminate ionomer layers (polymeric layers that conduct protons) while maintaining high performance at low iridium loadings. These ionomer-free electrode designs not only feature high performance but also enable facile fabrication processes and reuse of iridium after long-term operation, significantly contributing to cost reduction. This paper provides a comprehensive review of the ionomer-free electrodes for PEMWE, exploring its benefits, operation principles, and designs that have been studied in the literature to enhance catalytic activity and prolong durability.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.264
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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