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Record W2052159567 · doi:10.1063/1.4902987

Effects of configurational changes on electrical resistivity during glass-liquid transition of two bulk metal-alloy glasses

2014· article· en· W2052159567 on OpenAlexafffund
Daisman P. B. Aji, G. P. Johari

Bibliographic record

VenueThe Journal of Chemical Physics · 2014
Typearticle
Languageen
FieldEngineering
TopicMetallic Glasses and Amorphous Alloys
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrical resistivity and conductivityAlloyMaterials scienceAmorphous metalGlass transitionCondensed matter physicsTransition metalMetallurgyComposite materialChemistryPhysicsPolymer

Abstract

fetched live from OpenAlex

Consequences of increase in structural fluctuations on heating Pd40Ni10Cu30P20 and Zr46.75Ti8.25Cu7.5Ni10Be27.5 through their glass to liquid transition range were investigated by measuring the electrical resistivity, ρ, an electron scattering property. The temperature coefficient of resistivity (TCR = (1/ρ) dρ/dT) of the liquid and glassy states is negative. The plots of their ρ against T in the Tg (glass to liquid transition) range show a gradual change in the slope similar to the change observed generally for the plots of the density, elastic modulus, and refractive index. As fluctuations in the melt structure involve fewer configurations on cooling, ρ increases. In the energy landscape description, the melt's structure explores fewer minima with decrease in T, vibrational frequencies increase, and electron scattering and ρ increase. Plots of (-dρ/dT) against T resemble the plot of the specific heat of other glasses and show a sub-Tg feature and a rapid rise at T near Tg. Analysis shows that the magnitude of negative TCR is dominated by change in the phonon characteristics, and configurational fluctuations make it more negative. The TCR of the liquid and glassy states seems qualitatively consistent with the variation in the structure factor in Ziman's model for pure liquid metals as extended by Nagel to metal alloys and used to explain the negative TCR of a two-component metal glass.

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 score0.005
Threshold uncertainty score0.411

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 head0.007
GPT teacher head0.212
Teacher spread0.205 · 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

Citations4
Published2014
Admission routes2
Has abstractyes

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