Dynamic Monte Carlo Simulation of Atom‐Transfer Radical Polymerization
Bibliographic record
Abstract
Abstract Summary: A dynamic Monte Carlo model was developed to simulate atom‐transfer radical polymerization (ATRP). The algorithm used to describe the polymerization includes activation, deactivation, propagation, chain transfer, and termination by combination and disproportionation reactions. Model probabilities are calculated from polymerization kinetic parameters and reactor conditions. The model was used to predict monomer conversion, average molecular weight, polydispersity and the complete molecular weight distribution at any polymerization time or monomer conversion. The model was validated with experimental results for styrene polymerization and compared with simulation results from a mathematical model that uses population balances and the method of moments. The simulations agree well with experimental and theoretical results reported in the literature. We also investigated the control volume size and number of iterations to reduce computation time while keeping an acceptable noise level in the Monte Carlo results. Comparison of the chain length distribution of polystyrene made with ATRP and conventional free radical (CFR) polymerization at 50% conversion. The initiator to monomer ratios are 1:100 (ATRP left peak), 1:500 (ATRP right peak), and 1:1000 (CFR). magnified image Comparison of the chain length distribution of polystyrene made with ATRP and conventional free radical (CFR) polymerization at 50% conversion. The initiator to monomer ratios are 1:100 (ATRP left peak), 1:500 (ATRP right peak), and 1:1000 (CFR).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".