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Record W3137748545 · doi:10.1021/acsomega.0c05297

Reaction–Diffusion Model for Gasification of a Shrinking Single Carbon-Anode Particle

2021· article· en· W3137748545 on OpenAlexafffund
Mohammad Kavand, Roozbeh Mollaabbasi, Donald Ziegler, Faı̈çal Larachi, Donald Picard, Houshang Alamdari

Bibliographic record

VenueACS Omega · 2021
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsUniversité Laval
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaAlcoa
KeywordsCarbon fibersParticle (ecology)Materials scienceAnodeCarbon monoxideDiffusionReaction rateParticle sizeChemistryThermodynamicsChemical engineeringPhysical chemistryComposite materialPhysicsOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

at 1233 K. The experiments were performed in a thermogravimetric analyzer (TGA). Good agreement between the model results and the experimental data showed that this approach could quantify with success the gasification kinetics and the gas distribution within the anode particle. In addition, the Langmuir-Hinshelwood (L-H) model is used in order to capture the inhibition effect of carbon monoxide on the gasification reaction. The effectiveness factor and Thiele modulus simulated for various particle sizes helped assess the evolution of the relative dominance of diffusion and chemical reactions during the gasification process.

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 score0.128
Threshold uncertainty score0.316

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 head0.023
GPT teacher head0.222
Teacher spread0.199 · 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

Citations14
Published2021
Admission routes2
Has abstractyes

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