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Record W1975066296 · doi:10.1149/05801.0797ecst

Characterization and Performance of Catalyst Layers Prepared by Inkjet Printing Technology

2013· article· en· W1975066296 on OpenAlexafffund
Madhu Sudan Saha, Mickey Tam, Viatcheslav Berejnov, Darija Susac, Scott McDermid, Adam P. Hitchcock, Jürgen Stumper

Bibliographic record

VenueECS Transactions · 2013
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsMcMaster UniversityAutomotive Fuel Cell Cooperation (Canada)
FundersBasic Energy SciencesNatural Sciences and Engineering Research Council of CanadaLawrence Berkeley National LaboratoryCanadian Light SourceU.S. Department of Energy
KeywordsIonomerMaterials scienceNafionCoatingChemical engineeringCharacterization (materials science)CatalysisMembraneCathodeBar (unit)Composite materialFuel cellsInkjet printingNanotechnologyInkwellElectrodePolymerChemistryOrganic chemistryElectrochemistryEngineering

Abstract

fetched live from OpenAlex

The relationship between the structure of cathode catalyst layers (CCLs) prepared by different coating techniques and fuel cell performance was investigated. Inkjet printing technique was employed for producing CCLs directly on Nafion membranes, while Mayar bar was used to prepare conventional CCL coating that was decaled-transferred on to the membrane. Scanning transmission X-ray microscopy was used to determine the ionomer and carbon support distributions within the interior of both CCL structures. Distinctly different ionomer distributions were observed impacting Pt- utilization.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.003
GPT teacher head0.159
Teacher spread0.156 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations18
Published2013
Admission routes2
Has abstractyes

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