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Photothermal radiometric determination of thermal diffusivity depth profiles in a dental resin

2010· article· en· W2019417005 on OpenAlexaff
P. Martínez-Torres, Andreas Mandelis, J. J. Alvarado‐Gil

Bibliographic record

VenueJournal of Physics Conference Series · 2010
Typearticle
Languageen
FieldEngineering
TopicThermography and Photoacoustic Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceThermal diffusivityPhotopolymerPhotothermal therapyOpacityOpticsPolymerizationThermalComposite materialPolymerNanotechnology

Abstract

fetched live from OpenAlex

The depth of curing due to photopolymerization in a commercial dental resin is studied using photothermal radiometry. The sample consists of a thick layer of resin on which a thin metallic layer is deposited guaranteeing full opacity of the sample. In this case, purely thermal-wave inverse problem techniques without the interference of optical profiles can be used. Thermal profiles are obtained by heating the coating with a modulated laser beam and performing a modulation frequency scan. Before each frequency scan, photopolymerization was induced using a high power blue LED. However due to the fact that dental resins are highly light dispersive materials, the polymerization process depends strongly on the optical absorption coefficient inducing a depth dependent thermal diffusion in the sample. It is shown that using a robust depth profilometric inverse method one can reconstruct the thermal diffusivity profile of the photopolymerized resin.

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)0.001
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
Metaresearch0.0000.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.226
Teacher spread0.215 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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