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Record W2888732878 · doi:10.1063/1.5037117

High-frequency measurements of thermophysical properties of thin films using a modified broad-band frequency domain thermoreflectance approach

2018· article· en· W2888732878 on OpenAlexafffund
Mohammadreza Shahzadeh, Mohammed M. Rahman, Olav Hellwig, Simone Pisana

Bibliographic record

VenueReview of Scientific Instruments · 2018
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaYork University
KeywordsFrequency domainMaterials scienceFrequency bandPhase (matter)ThermalOpticsThin filmOptoelectronicsPhysicsNanotechnologyComputer scienceTelecommunicationsThermodynamics

Abstract

fetched live from OpenAlex

In this work, we present the implementation of a new method to perform high-frequency thermoreflectance measurements on thin films. The so-called differential broad-band frequency domain thermoreflectance method follows broad-band frequency domain thermoreflectance developed previously [Regner et al., Rev. Sci. Instrum. 84(6), 064901 (2013)], without the use of expensive electro-optic modulators. Two techniques are introduced to recover the thermal phase of interest and to separate it from the unwanted instrumental contributions to the recorded phase. Measuring a differential thermal phase by either varying the spot size or offsetting the pump and probe beams, the thermophysical properties of materials can be extracted. This approach enables the study of nanoscale heat transport where non-equilibrium phenomena are dominating.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.081
GPT teacher head0.278
Teacher spread0.197 · 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

Citations6
Published2018
Admission routes2
Has abstractyes

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