A Straightforward Estimation of Activation and Deactivation Parameters for ATRP Systems from Actual Polymerization Rate and Molecular Weight Distribution Data
Bibliographic record
Abstract
The accuracy of a model prediction relies heavily on the quality of the parameters used. Some of the available methods to estimate the activation and deactivation rate constants in atom transfer radical polymerization (ATRP) are done in the absence of monomer, hence they may not be representative of the polymerization conditions. Others offer great accuracy but requires data that are not commonly measured in experiments. In this study, a simple method is proposed to estimate activation and deactivation rate constants through correlating the recently reported theoretical equations with experimental data of monomer conversion and polymer molecular weight dispersity. The kinetics and dispersity equations used are straightforward and explicit, thus can be easily calculated. Only two parameters (activation and deactivation rate constant) are involved in the correlation of two data sets without any other adjustable parameters. In addition, the experimental data required are some of the most often measured quantities in polymerization, thus no additional experiments or measurements are required. Since the conversion and dispersity are obtained from actual polymerization, the reaction rate constants estimated are representative of the real reaction conditions. The applicability of this method is demonstrated by estimating parameters using reported experimental data of ATRP of methyl methacrylate conducted at various conditions. image
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".