MétaCan
Menu
Back to cohort
Record W2031145760 · doi:10.1063/1.4812734

Influence of particle-matrix interface, temperature, and agglomeration on heat conduction in dispersions

2013· article· en· W2031145760 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Applied Physics · 2013
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThermal conductionEconomies of agglomerationThermal conductivityMaterials scienceParticle (ecology)Matrix (chemical analysis)Monte Carlo methodScatteringNanoparticleConductivityThermodynamicsCondensed matter physicsChemical physicsChemistryNanotechnologyComposite materialPhysicsOpticsChemical engineeringPhysical chemistry

Abstract

fetched live from OpenAlex

A combination of the effective medium and the phonon approaches is used to investigate heat conduction in heterogeneous media composed of a homogeneous matrix in which spherical particles of micro and nanosizes are dispersed. In particular, we explore the effect of different types of scattering on the particle-matrix interface, temperature dependence of the effective heat conduction coefficient, and the effect of various degrees of agglomeration of the particles. Predictions calculated explicitly for Si nanoparticles dispersed in Ge matrix agree with available Monte Carlo simulations. Our predictions show that the higher is the temperature the lower is the heat conductivity and the smaller is the influence of the details of the particle-matrix interactions. As for the influence of the agglomeration, we predict both decrease and increase of the heat conduction depending on the degree of the agglomeration.

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 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 score0.094
Threshold uncertainty score0.271

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 head0.012
GPT teacher head0.246
Teacher spread0.235 · 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