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Record W2902921594 · doi:10.1155/2018/5919874

An Investigation of Direct Hydrocarbon (Propane) Fuel Cell Performance Using Mathematical Modeling

2018· article· en· W2902921594 on OpenAlexafffund
Bhavana Parackal, Hamidreza Khakdaman, Yves Bourgault, Marten Ternan

Bibliographic record

VenueInternational Journal of Electrochemistry · 2018
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Ottawa
FundersAustralian GovernmentTransport Canada
KeywordsLimiting currentPolarization (electrochemistry)ElectrolyteProton exchange membrane fuel cellCurrent densityExchange current densityCathodeHydrogenConcentration polarizationChemistryLimitingPropaneThermodynamicsHydrocarbonCurrent (fluid)Analytical Chemistry (journal)MechanicsMembraneElectrodeElectrochemistryChromatographyPhysicsPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

An improved mathematical model was used to extend polarization curves for direct propane fuel cells (DPFCs) to larger current densities than could be obtained with any of the previous models. DPFC performance was then evaluated using eleven different variables. The variables related to transport phenomena had little effect on DPFC polarization curves. The variables that had the greatest influence on DPFC polarization curves were all related to reaction rate phenomena. Reaction rate phenomena were dominant over the entire DPFC polarization curve up to 100 mA/cm 2 , which is a value that approaches the limiting current densities of DPFCs. Previously it was known that DPFCs are much different than hydrogen proton exchange membrane fuel cells (PEMFCs). This is the first work to show the reason for that difference. Reaction rate phenomena are dominant in DPFCs up to the limiting current density. In contrast the dominant phenomenon in hydrogen PEMFCs changes from reaction rate phenomena to proton migration through the electrolyte and to gas diffusion at the cathode as the current density increases up to the limiting current density.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.366
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.221
Teacher spread0.212 · 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 teacher head, 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

Citations2
Published2018
Admission routes2
Has abstractyes

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