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Record W3038066986 · doi:10.1021/acs.iecr.0c02281

First-Principles Determination of Transference Numbers in Cryolitic Melts

2020· article· en· W3038066986 on OpenAlexafffund
Aïmen E. Gheribi, Mathieu Salanne, Didier Zanghi, Kelly Machado, Catherine Bessada, Patrice Chartrand

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2020
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaAlcoa
KeywordsComputer scienceChemistry

Abstract

fetched live from OpenAlex

The charge and electron-transport properties of molten ionic systems are among the most relevant properties to consider in the control of several electrochemical processes. First-principles-based equilibrium molecular dynamics (EMD) can provide reliable predictions of both total and partial charge-transport properties. In this work, we calculate the charge-transport properties of the electrolytic bath (Na3AlF6–AlF3–Al2O3) of the Hall–Héroult electrolysis cells. We predict both individual and collective charge-transport properties (total and partial conductivities and self-diffusion coefficients) for 11 different compositions typical of industrial conditions via a series of EMD simulations. The predicted total and partial ionic conductivities and their composition dependence are compared to available experimental data. A good agreement is obtained for all studied compositions. From a more fundamental point of view, the microscopic aspect of the charge-transport properties of cryolitic melts is discussed through its correlation with the local structure of different melts. Deviations between the calculated partial conductivities and those derived via the Nernst–Einstein approximation can be explained by the presence of strong short-range ordering within the melts.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.134
GPT teacher head0.299
Teacher spread0.165 · 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 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

Citations7
Published2020
Admission routes2
Has abstractyes

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