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Record W4385767016 · doi:10.1007/s10008-023-05624-5

Modelling diffusion controlled electro-deoxidation of metal oxide to metal in molten salt

2023· article· en· W4385767016 on OpenAlexaff
Charles Osarinmwian, Edward P.L. Roberts

Bibliographic record

VenueJournal of Solid State Electrochemistry · 2023
Typearticle
Languageen
FieldChemical Engineering
TopicMolten salt chemistry and electrochemical processes
Canadian institutionsUniversity of Calgary
FundersEngineering and Physical Sciences Research Council
KeywordsMolten saltOxideDiffusionDrop (telecommunication)Exchange current densityThermodynamicsElectrolyteOverpotentialElectrochemistryCathodeMetalChemistryMaterials scienceAnalytical Chemistry (journal)ElectrodeInorganic chemistryPhysical chemistryMetallurgy

Abstract

fetched live from OpenAlex

Abstract Diffusion is a fundamental irreversible process intervening in the evolution of many out-of-equilibrium systems and is successfully described by Fick’s law obtained from non-equilibrium thermodynamics. Despite this, numerical simulations of solid state electro-deoxidation in the diffusion-controlled regime in molten salt remain elusive. Here, a new model for diffusion controlled three-phase interline (3PI) penetration in a porous cathode during electro-deoxidation is validated against experimental observation. This penetrating 3PI model is applied at high overpotential and benchmarked using the oxygen ionisation TiO 2 (s) + 4e − → Ti(s) + 2O 2− at the 3PI. The model couples slow diffusive transport and fast oxygen ionisation while assuming a negligible ohmic potential drop in bulk molten CaCl 2 electrolyte. The 14 nm s −1 penetration rate of the 3PI and the order of magnitude of 3PI currents (derived from an exchange current density and cathodic transfer coefficient of 0.32 A cm −2 and 0.01, respectively) in the chronoamperometric data for porous cathodes are in good agreement with experimental observation.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.253
Teacher spread0.243 · 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.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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