Mathematical modeling of the full molecular weight distribution in ATRP techniques
Bibliographic record
Abstract
In this work the molecular weight distribution (MWD) of several atom transfer radical polymerization (ATRP) techniques has been derived and solved using the Reduced Stiffness by Quasi Steady State Approximation (RSQSSA) methodology. The Quasi Steady State Approximation has been validated on the living radicals for normal, Simultaneous Reversible and Normal Initiation and Activators Regenerated by Electron Transfer (ARGET), and it is shown that the information lost due to its application is negligible. According to these results, RSQSSA shows the best performance in terms of wall‐clock time and required memory in comparison to implicit techniques and Predici. In the case of the ARGET technique, the model predictions show good agreement with experimental data. Finally, an analysis on the impact of the slow and fast activation of the initiator on the MWD using ARGET has been carried out, indicating that the optimal initiator to control the MWD should exhibit activation‐deactivation rates very similar to those of the polymeric equilibrium. © 2016 American Institute of Chemical Engineers AIChE J , 62: 2762–2777, 2016
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".