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Record W2007508502 · doi:10.1063/1.1631438

Temperature modeling of laser-irradiated azo-polymer thin films

2003· article· en· W2007508502 on OpenAlexafffund
Kevin G. Yager, Christopher J. Barrett

Bibliographic record

VenueThe Journal of Chemical Physics · 2003
Typearticle
Languageen
FieldMaterials Science
TopicLiquid Crystal Research Advancements
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIrradiationMaterials scienceThin filmPolymerGratingLaserThermal conductivityThermalPolarization (electrochemistry)Temperature gradientOptoelectronicsOpticsComposite materialChemistryNanotechnologyThermodynamics

Abstract

fetched live from OpenAlex

Azobenzene polymer thin films exhibit reversible surface mass transport when irradiated with a light intensity and/or polarization gradient, although the exact mechanism remains unknown. In order to address the role of thermal effects in the surface relief grating formation process peculiar to azo polymers, a cellular automaton simulation was developed to model heat flow in thin films undergoing laser irradiation. Typical irradiation intensities of 50 mW/cm2 resulted in film temperature rises on the order of 5 K, confirmed experimentally. The temperature gradient between the light maxima and minima was found, however, to stabilize at only 10(-4) K within 2 micros. These results indicate that thermal effects play a negligible role during inscription, for films of any thickness. Experiments monitoring surface relief grating formation on substrates of different thermal conductivity confirm that inscription is insensitive to film temperature. Further simulations suggest that high-intensity pulsed irradiation leads to destructive temperatures and sample ablation, not to reversible optical mass transport.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.279
Teacher spread0.257 · 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 designSimulation or modeling
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

Citations89
Published2003
Admission routes2
Has abstractyes

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