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Record W2461219621 · doi:10.1002/fuce.201500189

Improvement of the Ohmic Loss Process Model of the Proton Exchange Membrane Fuel Cell

2016· article· en· W2461219621 on OpenAlexaff
Seyed Mohammad Rezaei Niya, Ryan K. Phillips, Mina Hoorfar

Bibliographic record

VenueFuel Cells · 2016
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsProton exchange membrane fuel cellOhmic contactFuel cellsMaterials scienceProcess (computing)MembraneChemical engineeringNuclear engineeringChemistryNanotechnologyComputer scienceEngineering

Abstract

fetched live from OpenAlex

Abstract The impedance characteristics of the ohmic overpotential of the proton exchange membrane (PEM) fuel cells are studied analytically using the process modeling approach. The water transport in the membrane, the cathode catalyst layer, and gas diffusion layer are analyzed. The analytical relation of the impedance of the ohmic loss is determined and is converted to an equivalent circuit. Then, the impedance of a PEM fuel cell is measured experimentally in different current densities, operating temperatures and the anode and cathode relative humidities. The measured impedances are compared with the predicted ones from the analytical model. It is shown that the predicted impedance characteristics are in great agreement with the measured ones in all different operating conditions.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.007
GPT teacher head0.180
Teacher spread0.173 · 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 designSimulation or modeling
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

Citations12
Published2016
Admission routes1
Has abstractyes

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