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Record W2916056676 · doi:10.1103/physrevb.99.054308

Nondiffusive electron transport in metals: A two-temperature Boltzmann transport equation analysis of thermoreflectance experiments

2019· article· en· W2916056676 on OpenAlexfundno aff
Justin P. Freedman, R. F. Davis, Jonathan A. Malen

Bibliographic record

VenuePhysical review. B./Physical review. B · 2019
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsnot available
FundersDivision of Chemical, Bioengineering, Environmental, and Transport SystemsNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsThermal conductivityBoltzmann equationThermal conductionElectronAtmospheric temperature rangePhononBoltzmann constantDiffusionConductivityCondensed matter physicsElectron temperatureMaterials scienceNon-equilibrium thermodynamicsHeat equationThermodynamicsChemistryPhysicsPhysical chemistry

Abstract

fetched live from OpenAlex

We develop a theoretical framework based on the electron-phonon coupled Boltzmann transport equations (BTEs) for the interpretation of nondiffusive thermal conductivity measurements in metals made via frequency domain thermoreflectance (FDTR). The thermal conductivity of a bulk gold crystal was measured over a temperature range of 23--304 K as a function of FDTR's laser spot size. Our interpretation of these measurements by a two-temperature heat diffusion model finds that the thermal conductivity is suppressed when the laser spot size is comparable to electron mean free paths. Using a simplified spherical geometry that enables analytical solutions, we compare the two-temperature diffusion model with the coupled BTEs to identify a thermal conductivity suppression function. We also conclude that over the timescales of our experiment electron-phonon nonequilibrium is negligible, but that the length scale of heat deposition by hot electrons critically influences our interpretation.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.363
Teacher spread0.342 · 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; both teacher heads agree on what is shown here.

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

Citations4
Published2019
Admission routes1
Has abstractyes

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