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Record W2092218389 · doi:10.1063/1.2872462

Influence of laser beam size on measurement sensitivity of thermophysical property gradients in layered structures using thermal-wave techniques

2008· article· en· W2092218389 on OpenAlexaff
Chinhua Wang, Andreas Mandelis, Hong Qu, Zhuying Chen

Bibliographic record

VenueJournal of Applied Physics · 2008
Typearticle
Languageen
FieldEngineering
TopicThermography and Photoacoustic Techniques
Canadian institutionsUniversity of Toronto
FundersGovernment of Jiangsu Province
KeywordsThermal diffusivityMaterials scienceThermal conductivityPhotothermal therapyLaser flash analysisLaserAmplitudeBeam (structure)OpticsThermalThermal conductivity measurementPhase (matter)Composite materialThermodynamicsChemistryNanotechnology

Abstract

fetched live from OpenAlex

The influence of the photothermal laser source beam size on the measurement sensitivity of layered systems using photothermal radiometry (PTR) is presented. Based on an appropriate theoretical model, widely different behaviors of the photothermal amplitude and phase in terms of combinations of thermophysical properties (i.e., thermal conductivity and thermal diffusivity) between a thin coating and the substrate are observed. The beam size effect on PTR measurement sensitivity is theoretically examined and experimentally demonstrated using a carbonitrided C1018 steel sample. The experimental results of using a variable size laser beam for the carbonitrided C1018 sample validate the theoretical prediction, in which an expanded beam exhibits a much larger magnitude change in both amplitude and phase as a function of frequency than measurements with a focused beam. The fitted thermal conductivity and thermal diffusivity based on the assumed industrially relevant range of effective hardness case depth gives the approximate range of the change in thermal conductivity and thermal diffusivity of C1018 steels after the carbonitriding process.

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)0.000
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 score0.282
Threshold uncertainty score0.534

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 head0.025
GPT teacher head0.214
Teacher spread0.189 · 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

Citations14
Published2008
Admission routes1
Has abstractyes

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