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Record W2319089026 · doi:10.1098/rspa.2015.0144

An extended irreversible thermodynamic modelling of size-dependent thermal conductivity of spherical nanoparticles dispersed in homogeneous media

2015· article· en· W2319089026 on OpenAlexaff
G. Lebon, Hatim Machrafi, Miroslav Grmela

Bibliographic record

VenueProceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 2015
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsThermal conductivityNanoparticleMaterials scienceVolume fractionHomogeneousScatteringThermalParticle sizeNanocompositeDispersion (optics)ConductivityThermodynamicsComposite materialNanotechnologyOpticsChemistryPhysicsPhysical chemistry

Abstract

fetched live from OpenAlex

The effective thermal conductivity of nanocomposites constituted by nanoparticles and homogeneous host media is discussed from the point of view of extended irreversible thermodynamics. This formalism is particularly well adapted to the description of small length scales. As illustrations, dispersion of Si nanoparticles in Ge (respectively, SiO 2 in epoxy resin) homogeneous matrices is investigated, the nanoparticles are assumed to be spherical with a wide dispersion. Four specific problems are studied: the dependence of the effective thermal conductivity on the volume fraction of particles, the type of phonon scattering at the interface particle–matrix, the radius of the nanoparticles and the temperature.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.220
Teacher spread0.195 · 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 designSimulation or modeling
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

Citations10
Published2015
Admission routes1
Has abstractyes

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