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Record W1974973924 · doi:10.1002/er.1141

Multi-component mathematical model of solid oxide fuel cell anode

2005· article· en· W1974973924 on OpenAlexafffund
Mohammed Hussain, X. Li, İbrahim Dinçer

Bibliographic record

VenueInternational Journal of Energy Research · 2005
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsUniversity of Ontario Institute of TechnologyUniversity of Waterloo
FundersAUTO21 Network of Centres of Excellence
KeywordsAnodeSolid oxide fuel cellKnudsen diffusionCathodeElectrochemistryCarbon monoxideHydrogenMaterials scienceChemical engineeringElectrodeDiffusionChemistryPorosityThermodynamicsComposite materialCatalysisPhysical chemistryOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

A mathematical model describing the multi-component species transport inside the porous solid oxide fuel cell (SOFC) anode has been developed. The model includes the water–gas shift reaction in the anode electrode (backing) layer and the spatially resolved electrochemical reaction in the reaction zone layer. The modified Stefan–Maxwell equations incorporating Knudsen diffusion were used to model multi-component diffusion inside the porous electrode (backing) and reaction zone layers. Moreover, the general Butler–Volmer equation was used to model the electrochemical reaction in the reaction zone layer. The model can predict the distribution of species within the SOFC anode for any reformate gas composition involving carbon dioxide, carbon monoxide, hydrogen and water vapour. The chemical and electrochemical reactions as well as transport processes in the SOFC anode can be simulated, yielding the anode performance under various operating and design conditions. This anode model can be coupled with a similarly developed model for the cathode to form an overall model for a single SOFC model. Copyright © 2005 John Wiley & Sons, Ltd.

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 score0.088
Threshold uncertainty score0.813

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
Research integrity0.0000.000
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 head0.089
GPT teacher head0.405
Teacher spread0.316 · 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

Citations55
Published2005
Admission routes2
Has abstractyes

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