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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 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)0.001
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 score0.006
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0050.002

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 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

Citations55
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Energy ResearchSame topicAdvancements in Solid Oxide Fuel CellsFrench-language works237,207