Excitação óptica tipo degrau nas técnicas fototérmicas : estudo de vidros ópticos e de materiais para produção de energia
Bibliographic record
Abstract
In this work, four photothermal techniques, the Thermal Lens Spectroscopy (TL), the Thermal Mirror Spectroscopy (TM), the Photothermal Deflection (PDS), and the Open Photoacoustic Cell (OPC) have been employed to characterize several materials under tophat laser excitation. The experiments were performed on optical materials used for energy production. The theoretical models for the tophat laser excitation were developed for the TL and TM methods. Measurements on transparent optical glasses were performed concurrently with the TL and the TM techniques. The results have showed the feasibility of using less expensive non-Gaussian profile lasers for these techniques. Indeed, it has been showed that the TM tophat excitation can be used to study opaque solid materials. The measurements were performed on crystalline and polycrystalline graphite plates, which are materials used in hydrogen fuel cells. The feasibility of the TL under tophat excitation for the study of liquids was tested on diesel fuels extracted from Canadian oil sands. The results under tophat and also under Gaussian excitations were in good agreement and the values obtained for the thermal diffusivity and the temperature coefficient of the refractive index (dn/dT) were correlated with the cetane number and the monocyclic aromatics contents of the fuels. In addition, the OPC and PDS were used to characterize catalyst layers. Catalyst layers are materials also used in hydrogen fuel cells. In summary, the photothermal techniques under tophat laser excitation presented in this work have been proven to be applicable in studying several different materials, suggesting their applicability in less expensive setups compared to the Gaussian excitation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".