Comparison between the dual-exponential and autocatalytic models to examine rapid photopolymerization kinetics of dental resins
Bibliographic record
Abstract
Objectives 1) To investigate two empirical models used to characterize the polymerization kinetics of six resin-based composites (RBCs) and 2) the impact of the sampling rate on the time-varying degree of conversion (DC). Methods The DC of three sculptable and three flowable RBCs was recorded using attenuated total internal reflectance Fourier transform infrared spectroscopy at a collection rate of 13 DC/s. A multiple-diode light-curing unit delivered either an irradiance of 1.2 or 3 W/cm 2 . The RBC specimens were either 0.2 mm or 4 mm thick and were photocured at 32 ºC. Sampling rates as low as 0.2 DC/s were simulated by numerically interpolating the measured DC( t ). The DC( t ) obtained at different sampling rates was fitted to the dual-exponential and autocatalytic models. Results For all six RBCs, the fit of the autocatalytic model to the data resulted in the smallest mean squared errors. The lower simulated sampling rates did not represent the highly time-resolved DC collected at an irradiance of 3 W/cm 2 and specimen thickness of 0.2 mm. For the DC simulated at a sampling rate of 0.2 DC/s using PowerFill and analyzed with the autocatalytic model, the maximum DC rate was 3.7 %/s, occurring at a time of 5 s after the start of photocuring. However, using a sampling rate of 13 DC/s, they were 64.5 %/s and 116 ms, respectively. Significance The autocatalytic model was found to better characterize the kinetics of RBC photopolymerization than the dual-exponential model. The data collection rate has a strong influence on the results.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".