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Record W2765114707 · doi:10.1038/s41598-017-14242-x

Dynamic measurement of near-field radiative heat transfer

2017· article· en· W2765114707 on OpenAlexafffund
Slawa Lang, G.P. Sharma, Sean Molesky, Patrick Udo Kränzien, T. Jalas, Zubin Jacob, Alexander Yu. Petrov, M. Eich

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldEngineering
TopicThermal Radiation and Cooling Technologies
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaDeutsche ForschungsgemeinschaftHelmholtz-Alberta InitiativeAlberta InnovatesAlberta Innovates - Technology Futures
KeywordsRadiative transferThermal conductionBlack-body radiationHeat transferThermal radiationTransient (computer programming)MechanicsMaterials scienceRadiant heatField (mathematics)ThermalComputational physicsPhysicsThermodynamicsOpticsComputer scienceRadiationComposite material

Abstract

fetched live from OpenAlex

Super-Planckian near-field radiative heat transfer allows effective heat transfer between a hot and a cold body to increase beyond the limits long known for black bodies. Until present, experimental techniques to measure the radiative heat flow relied on steady-state systems. Here, we present a dynamic measurement approach based on the transient plane source technique, which extracts thermal properties from a temperature transient caused by a step input power function. Using this versatile method, that requires only single sided contact, we measure enhanced radiative conduction up to 16 times higher than the blackbody limit on centimeter sized glass samples without any specialized sample preparation or nanofabrication.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.233
Teacher spread0.216 · 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 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

Citations39
Published2017
Admission routes2
Has abstractyes

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