ARGET ATRP of Butyl Methacrylate: Utilizing Kinetic Modeling To Understand Experimental Trends
Bibliographic record
Abstract
A comprehensive kinetic Monte Carlo (kMC) model is used to interpret and better understand the results of a systematic experimental investigation of activators regenerated by electron transfer atom transfer radical polymerization (ARGET ATRP) of butyl methacrylate (BMA) using Sn(EH) 2 as reducing agent, ethyl 2-bromoisobutyrate (EBiB) as ATRP initiator, and CuBr 2 /TPMA (TPMA: tris[(2-pyridyl)methyl]amine) as deactivator. The model demonstrates the importance of slow initiation, with distinct activation and deactivation rate coefficients for the initiator and polymeric species required to match the experimental data. In addition, the model incorporates a second reduction step for the reducing agent and accounts for diffusional limitations on chain-length-dependent termination. The effect of temperature on the slow ATRP initiation is limited, and a sufficiently high initial reducing agent concentration is crucial to obtain a high conversion, although achieved at the expense of decreased end-group functionality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".