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Record W2791742501 · doi:10.1080/08957959.2018.1493476

Decreasing electrical resistivity of gold along the melting boundary up to 5 GPa

2018· article· en· W2791742501 on OpenAlexafffund
Meryem Berrada, Richard A. Secco, Wenjun Yong

Bibliographic record

VenueHigh Pressure Research · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicHigh-pressure geophysics and materials
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsElectrical resistivity and conductivityMaterials sciencePhase boundaryThermodynamicsThermal conductivityMelting temperatureCondensed matter physicsPhase (matter)ChemistryComposite materialPhysics

Abstract

fetched live from OpenAlex

The electrical resistivity of gold was experimentally measured at high pressures from 2 to 5 GPa and temperatures ∼300 K above melting. The resistivity decreased as a function of pressure and increased as a function of temperature as expected. The temperature dependence of resistivity in the solid and liquid phases are comparable to 1 atm results. The observed melting temperatures at each pressure agree well with previous experimental and theoretical studies. The essential result of this study is that resistivity decreases along the pressure-dependent melting boundary, conflicting with a prediction of invariant behavior as reported in the literature. This result is discussed in terms of the interaction between s and d-bands as both pressure and temperature increase along the melting boundary. The thermal conductivity of gold was calculated from the measured electrical resistivity using the Wiedemann-Franz law. The temperature-induced effect on the thermal conductivity at high temperatures is as expected in both the solid and liquid phase while the pressure-effect shows some variability.

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)0.001
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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.319
Teacher spread0.276 · 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

Citations16
Published2018
Admission routes2
Has abstractyes

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