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Record W4383750310 · doi:10.1039/d3cp01940c

Investigating the Al/Si mixed site occupancy in the β-AlFeSi phase

2023· article· en· W4383750310 on OpenAlexafffund
Paul Lafaye, Minh Duc Vo, Javier Jofré, Jean‐Philippe Harvey

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2023
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCompute Canada
KeywordsThermodynamicsEnthalpyChemistryEnthalpy of mixingSolid solutionEntropy of mixingGibbs free energyConfiguration entropyHeat capacityDebye modelPhysics

Abstract

fetched live from OpenAlex

of the β-AlFeSi structure. Finally, the configurational entropy of mixing from the Compound Energy Formalism (CEF) for the configurational entropy of mixing was subsequently used to calculate the occupation fractions of the Si sites on the Al sites of the β-AlFeSi structure, at 300 K and 938 K, the latter being the thermal decomposition temperature of this compound. These original site occupancy data were used to quantify the chemical ordering of the solid solution and to compare different sublattice (SL) models. We thus highlight that the SL model of the β-AlFeSi solution most commonly accepted in the literature generates considerable errors in its thermodynamic description, contrary to the model proposed in this paper, which is both simple and particularly accurate, consisting in merging the sites Al(1)-Al(6), the sites Al(2)-Al(3) as well as the sites Al(4)-Al(5).

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.018
GPT teacher head0.251
Teacher spread0.233 · 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 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

Citations7
Published2023
Admission routes2
Has abstractyes

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