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Record W2044660986 · doi:10.1155/2015/748679

n-Hexadecane Fuel for a Phosphoric Acid Direct Hydrocarbon Fuel Cell

2015· article· en· W2044660986 on OpenAlexafffund
Yuanchen Zhu, Travis Robinson, Amani Al–Othman, André Y. Tremblay, Marten Ternan

Bibliographic record

VenueJournal of Fuels · 2015
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaTransport Canada
KeywordsDiesel fuelHexadecanePhosphoric acidAnodeProton exchange membrane fuel cellWaste managementHydrocarbonChemical engineeringHydrogenPolarization (electrochemistry)Cetane numberHydrogen fuelMaterials scienceEnvironmental scienceFuel cellsChemistryElectrodeCatalysisOrganic chemistryBiodieselEngineering

Abstract

fetched live from OpenAlex

The objective of this work was to examine fuel cells as a possible alternative to the diesel fuel engines currently used in railway locomotives, thereby decreasing air emissions from the railway transportation sector. We have investigated the performance of a phosphoric acid fuel cell (PAFC) reactor, with n-hexadecane, C 16 H 34 (a model compound for diesel fuel, cetane number = 100). This is the first extensive study reported in the literature in which n-hexadecane is used directly as the fuel. Measurements were made to obtain both polarization curves and time-on-stream results. Because deactivation was observed hydrogen polarization curves were measured before and after n-hexadecane experiments, to determine the extent of deactivation of the membrane electrode assembly (MEA). By feeding water-only (no fuel) to the fuel cell anode the deactivated MEAs could be regenerated. One set of fuel cell operating conditions that produced a steady-state was identified. Identification of steady-state conditions is significant because it demonstrates that stable fuel cell operation is technically feasible when operating a PAFC with n-hexadecane fuel.

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)0.002
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 score0.203
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.029
GPT teacher head0.280
Teacher spread0.251 · 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

Citations27
Published2015
Admission routes2
Has abstractyes

Explore more

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