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Record W2144441584 · doi:10.1149/05901.0145ecst

Contact Resistance at PEM-PEM Interfaces: Charged Fluid Flow between Nanochannels

2014· article· en· W2144441584 on OpenAlexafffund
Sven‐Joachim Kimmerle, Kehinde O. Ladipo, Arian Novruzi, P.M. van den Berg

Bibliographic record

VenueECS Transactions · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSurface and Thin Film Phenomena
Canadian institutionsUniversity of OttawaHumber Polytechnic
FundersUniversity of Ottawa
KeywordsElectrolyteMembraneIonomerOhmic contactContact resistanceMaterials sciencePolymerProton exchange membrane fuel cellFlow (mathematics)Fluid dynamicsChemical engineeringComposite materialChemical physicsMechanicsChemistryLayer (electronics)ElectrodePhysicsEngineeringPhysical chemistry

Abstract

fetched live from OpenAlex

For hydrogen fuel cells running at low temperature, a single polymer electrolyte membrane could be fabricated from several layers of ionomer, each with specific properties so as to optimize the functionality of the overall membrane. However, an ohmic resistance between interfaces of two joint polymer electrolyte membranes is observed for which a theoretical explanation is missing. In this contribution, a new model for the charged fluid flow in and between ionomer nanochannels is established and investigated numerically. Employing a statistical analysis over an ensemble of nanochannel connections, the model provides a possible explanation for this interfacial resistance.

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)0.002
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
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 head0.012
GPT teacher head0.222
Teacher spread0.209 · 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

Citations0
Published2014
Admission routes2
Has abstractyes

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