Detection of PLP Structure for Accurate Determination of Propagation Rate Coefficients over an Enhanced Range of PLP-SEC Conditions
Bibliographic record
Abstract
The factors influencing the periodic structure in molar mass distributions (MMDs) generated in pulsed-laser polymerization (PLP) experiments are investigated to extend the range of operating conditions under which radical polymerization propagation rate coefficients ( k p ) can be reliably estimated. Specifically, it is shown how k p may be determined well into conditions corresponding to the so-called low and high termination rate limits. A new parameter x is introduced to provide a convenient measure of when PLP pseudostationary conditions approach the low ( x ≤ 0.2) and high ( x ≥ 5.0) termination rate limits. In addition, a simple transformation is proposed to detect the PLP structure obscured by the background of the distribution under limiting experimental conditions, with simulations confirming that the methodology provides an estimate of k p with reasonable accuracy. The usefulness of the technique is then demonstrated through application to several experimental distributions. The influences of chain transfer and of chain-length-dependent kinetic parameters on the PLP structure are also systematically investigated via simulation, revealing that the principal limitation for detecting PLP structure using the methodology is size-exclusion chromatography (SEC) noise at the low and high termination rate limits.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".