Visualization of Bivariate Sequence Length–Chain Length Distribution in Free Radical Copolymerization
Bibliographic record
Abstract
Copolymer properties and processability depend on copolymer microstructure, i.e., copolymer composition and monomer unit arrangements along the copolymer chains. To predict the ultimate properties of copolymers, one needs complete information on the length and position of sequences of each monomer type in every chain. A versatile kinetic Monte Carlo code is developed and applied for the simulation of typical free radical copolymerizations. The code allows explicit monitoring of every growing chain during the course and at the end of polymerization, can account for comonomer systems of any arbitrary reactivity ratios (r1 and r2) over the full range of monomer composition. Meanwhile, it eliminates the need for solving arrays of differential equations arising from deterministic modeling approaches. Since the code virtually synthesizes billions of copolymer molecules and keeps in storage information on each and every copolymer chain in the system, it allows for detailed statistical analysis. The simulator visualizes the bivariate sequence length–chain length distribution for typical copolymerization systems and examples with: r1 < 1 and r2 < 1; r1 > 1 and r2 < 1; (r1 × r2) = 1; and r1 = r2 = 1, and is also applied successfully to an experimental scenario described in the literature.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".