A 3D Simulation Investigation of the Influence of Temperature Increases on the Accuracy of Propagation Rate Coefficients Determined by Pulsed-Laser Polymerization
Bibliographic record
Abstract
A numerical approach has been developed to capture the inhomogeneous and nonstationary processes of pulsed laser polymerization (PLP). The new simulation tool has been applied to systematically investigate how temperature gradients that develop in the sample cell influence the accuracy of propagation rate coefficients, k p, determined by the PLP-SEC method. Although the temperature in the center of the sample cell may increase more than 30 °C during pulsing of bulk vinyl acetate (VAc), this increase does not greatly influence the accuracy of k p determined from the cumulative polymer molar mass distribution (MMD), provided that overall conversion in the system is kept low. Simulations indicate that the dependence of k p on laser pulse repetition rate observed experimentally for VAc is not caused by nonisothermal gradients. However, the temperature increases cause a widening of the peaks in MMDs, a loss of the expected PLP structure, and a slight increase in k p values with the number of pulses applied. These observations are demonstrated experimentally for vinyl pivalate (VPi), with simulated MMDs in good agreement with those measured experimentally. The pulse repetition dependence of k p observed in the literature for VPi is verified, with the value determined at 30 °C, k p = 7270 L mol –1 s –1, also in good agreement with literature.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".