Design and Control of Copolymer Composition Distribution in Living Radical Polymerization Using Semi‐Batch Feeding Policies: A Model Simulation
Bibliographic record
Abstract
Abstract Summary: Although controlled/living radical copolymerization has been extensively studied, the control of copolymer composition distribution receives little attention. In this paper, taking RAFT copolymerization as an example, we develop a mathematical model and simulate copolymerization systems with various reactivity ratios. It is demonstrated that through semi‐batch operations with programmed profiles of slow monomer feeding rate, precise control over copolymer composition distribution (uniform and designed gradient distributions) along polymer chain can be achieved. It is also found that the semi‐batch operations have lower rates of polymerization than their batch counterparts. The reason for this difference is analyzed, and the magnitude depends on the reactivity ratios and targeted copolymer composition. The improvement of the semi‐batch rate by distributing a part of the initiator amount to the monomer feeding tank is found to be minor. Model‐based design and control over composition distribution of gradient copolymers implemented by semi‐batch operations. magnified image Model‐based design and control over composition distribution of gradient copolymers implemented by semi‐batch operations.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".