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Record W2100014971 · doi:10.1039/c3cs60053j

Alkaline polymer electrolyte membranes for fuel cell applications

2013· review· en· W2100014971 on OpenAlexaff
Yanjie Wang, Jinli Qiao, R. Baker, Jiujun Zhang

Bibliographic record

VenueChemical Society Reviews · 2013
Typereview
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsElectrolyteMembraneDurabilityCommercializationFuel cellsPolymerMaterials scienceAlkaline fuel cellProton exchange membrane fuel cellChemical engineeringNanotechnologyElectrodeChemistryEngineeringComposite materialBusiness

Abstract

fetched live from OpenAlex

In this review, we examine the most recent progress and research trends in the area of alkaline polymer electrolyte membrane (PEM) development in terms of material selection, synthesis, characterization, and theoretical approach, as well as their fabrication into alkaline PEM-based membrane electrode assemblies (MEAs) and the corresponding performance/durability in alkaline polymer electrolyte membrane fuel cells (PEMFCs). Respective advantages and challenges are also reviewed. To overcome challenges hindering alkaline PEM technology advancement and commercialization, several research directions are then proposed.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.005

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 head0.027
GPT teacher head0.272
Teacher spread0.245 · 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

Citations594
Published2013
Admission routes1
Has abstractyes

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