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Record W2518451839 · doi:10.1139/cjp-2016-0241

Thermal transport across a pair of thin silicon films with the presence of minute vacuum gap: effect of film thickness on thermal characteristics

2016· article· en· W2518451839 on OpenAlexvenueno aff
Haider Ali, Bekir Sami Yilbaş

Bibliographic record

VenueCanadian Journal of Physics · 2016
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsnot available
FundersKing Fahd University of Petroleum and Minerals
KeywordsPhononCondensed matter physicsSiliconBand gapMean free pathPhysicsThermal conductivityTemperature jumpBoltzmann equationBallistic conductionHeat transferThin filmOpticsMechanicsOptoelectronicsScatteringThermodynamicsElectron

Abstract

fetched live from OpenAlex

Energy transport across a pair of thin silicon films with the vacuum gap at the films interface is studied. The Boltzmann transport equation is incorporated in the analysis and the solution for the transient frequency-dependent phonon distribution across the films pair is presented. To assess the phonon characteristics, equivalent equilibrium temperature is introduced, which resembles the average energy of all phonons around a local point when they redistribute adiabatically to an equilibrium state. Because the gap size is comparable to the mean free path of silicon, a near-field radiation heat transfer is incorporated across the film edges at the interface. The frequency cutoff method is used at the interface of the films and the phonons jump across the gap resembling the ballistic phonon contribution to the energy transport is accommodated. The thermal conductivity data predicted are validated with the data obtained from the previous study. The effect of near-field radiation heat transfer on temperature increase at the edges of the film, across the gap interface, is not considerable as compared to that corresponding to phonons transmitted across the gap. Increasing the first film thickness increases temperature difference across the gap, which is more pronounced for large gap sizes.

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 score0.026
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.014
GPT teacher head0.217
Teacher spread0.203 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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