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Record W2129324241 · doi:10.3390/e15093810

Entropies in Alloy Design for High-Entropy and Bulk Glassy Alloys

2013· article· en· W2129324241 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEntropy · 2013
Typearticle
Languageen
FieldEngineering
TopicHigh Entropy Alloys Studies
Canadian institutionsHatch (Canada)
FundersJapan Society for the Promotion of Science
KeywordsAmorphous metalThermodynamicsEntropy of mixingTernary operationMaterials scienceEnthalpyAlloyHigh entropy alloysConfiguration entropyAtomic radiusAmorphous solidEntropy (arrow of time)Enthalpy of mixingMetallurgyCrystallographyPhysicsChemistry

Abstract

fetched live from OpenAlex

High-entropy (H-E) alloys, bulk metallic glasses (BMGs) and high-entropy BMGs (HE-BMGs) were statistically analyzed with the help of a database of ternary amorphous alloys. Thermodynamic quantities corresponding to heat of mixing and atomic size differences were calculated as a function of composition of the multicomponent alloys. Actual calculations were performed for configurational entropy (Sconfig.) in defining the H-E alloys and mismatch entropy (Ss) normalized with Boltzmann constant (kB), together with mixing enthalpy (DHmix) based on Miedema’s empirical model and Delta parameter (d) as a corresponding parameter to Ss/kB. The comparison between DHmix–d and DHmix– diagrams for the ternary amorphous alloys revealed Ss/kB ~ (d /22)2. The zones S, S′ and B’s where H-E alloys with disordered solid solutions, ordered alloys and BMGs are plotted in the DHmix–d diagram are correlated with the areas in the DHmix – Ss /kB diagram. The results provide mutual understandings among H-E alloys, BMGs and HE-BMGs.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.368
Threshold uncertainty score1.000

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 head0.011
GPT teacher head0.204
Teacher spread0.194 · 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