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Record W2585845284 · doi:10.1021/acsenergylett.6b00644

Current Status and Future Development of Catalyst Materials and Catalyst Layers for Proton Exchange Membrane Fuel Cells: An Industrial Perspective

2017· article· en· W2585845284 on OpenAlexaff
Dustin Banham, Siyu Ye

Bibliographic record

VenueACS Energy Letters · 2017
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsBallard Power Systems (Canada)
Fundersnot available
KeywordsProton exchange membrane fuel cellTimelineCatalysisAutomotive industryDurabilityFuel cellsNanotechnologyEngineeringMaterials scienceWaste managementChemical engineeringChemistryComposite material

Abstract

fetched live from OpenAlex

Proton exchange membrane fuel cells (PEMFCs) have already penetrated many commercial markets (e.g., portable power, backup power, materials handling, and buses) and are poised to greatly expand in the automotive market with both Toyota and Hyundai recently commercializing small fleets. As this occurs, catalysts for PEMFCs will experience ever greater demands on cost, activity, and durability. This Perspective outlines the technology timeline and characteristics of the most promising catalysts currently being developed and discusses the remaining challenges for both platinum group metal and nonprecious metal catalysts. Finally, the importance of combined catalyst and catalyst layer design strategies is highlighted, and a brief discussion on the future outlook of this field is provided.

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)0.003
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 score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.005
Open science0.0010.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.021
GPT teacher head0.255
Teacher spread0.233 · 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

Citations578
Published2017
Admission routes1
Has abstractyes

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