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Record W2340731708 · doi:10.1063/1.4945764

Thermal transport properties of multiphase sintered metals microstructures. The copper-tungsten system: Experiments and modeling

2016· article· en· W2340731708 on OpenAlexafffund
Aïmen E. Gheribi, Emmanuel Autissier, Jean-Laurent Gardarein, M. Richou

Bibliographic record

VenueJournal of Applied Physics · 2016
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaAgence Nationale de la Recherche
KeywordsMaterials scienceMicrostructureGrain sizeTungstenSpark plasma sinteringThermal diffusivityCopperGrain boundaryPorosityThermal conductivityMetallurgyLaser flash analysisGrain boundary diffusion coefficientSinteringComposite materialThermodynamics

Abstract

fetched live from OpenAlex

The thermal diffusivity of Cu-W sintered alloys microstructures is measured at room temperature at different compositions, using rear face flash experiments. The samples are synthesized with the Spark Plasma Sintering technique. The resulting microstructures are slightly porous and consist of angular nanoscale grains of tungsten with medium sphericity in a copper matrix. The tungsten particles are at the nanoscale with an average grain size of 250 nm in contrast to the copper matrix for which the average grain size lies in the range 20 μm–30 μm; this is large enough to avoid the grains boundary effect upon the thermal transport. The overall porosity of the microstructures lies within the range: 6%≤P≤12%. Along with the experimental work, a predictive model describing the effective thermal conductivity of multiphasic macrostructures is proposed in order to explain the obtained experimental results. The model was developed based only on physical considerations and contains no empirical parameters; it takes into account the type of microstructure and the microstructure parameters: porosity, grain shape, grain size, and grain size distribution. The agreement between the experiments and the model is found to be excellent.

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)0.001
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 score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.039
GPT teacher head0.240
Teacher spread0.201 · 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

Citations11
Published2016
Admission routes2
Has abstractyes

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