Optical and thermal depth profile reconstructions of inhomogeneous photopolymerization in dental resins using photothermal waves
Bibliographic record
Abstract
Photopolymerization is a process that depends, among other factors, on the optical properties of polymerized materials. In turn, this process affects longitudinal light transport in these materials, thereby altering their optical absorption coefficient which is thus expected to exhibit depth dependence. Furthermore, polymerization affects the thermal properties of these materials. A robust theoretical approach to the study of the depth-dependent optical absorption coefficient, β(x), and thermal diffusivity, α(x), in materials exhibiting depth profiles of these parameters has been developed through the photothermal inverse problem based on the concept of the thermal-harmonic oscillator. Using this concept in the frequency-domain nonhomogeneous photothermal-wave boundary-value problem, the simultaneous reconstruction of arbitrary simultaneous optical and thermal depth profiles was achieved using a multiparameter fitting method to the experimental amplitude and phase. As a first application of the theory to partially polymerized Alert Composite (shade A3) dental resin, with curing induced by a blue light-emitting diode, the β(x) and α(x) depth profiles were reconstructed from photothermal radiometric frequency-scanned data. A strong anticorrelation of these two depth profiles was observed and was interpreted in terms of photochemical processes occurring during the optical (photocuring) creation of long polymeric chains in the resin. The photothermally reconstructed depth profiles may have implications for the optimization of blue light curing methods using such resins in dental clinical practice.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".