Computational approach for copolymerization of lactide with lactone ( <scp>5HDON</scp> ) inimer
Bibliographic record
Abstract
Abstract Polylactide, a biodegradable polymer, has garnered significant attention due to its environmental sustainability and application versatility. The present study introduces a comprehensive mathematical model for the sustainable production of branched polylactide through ring‐opening polymerization (ROP) of L‐lactide with 5HDON (5‐hydroxymethyl‐1,4‐dioxane‐2‐on), using Sn(Oct) 2 . The model incorporates a derived reaction mechanism, mass and population balance equations, and the method of moments to predict average molecular properties. Kinetic parameters are estimated through optimization techniques such as particle swarm optimization (PSO), simulated annealing (SA), and genetic algorithm (GA). The model effectively predicts the behaviour of different lactide/5HDON ratios (PLLH80, PLLH94, PLLH97, and PLLH99), revealing a positive correlation between monomer concentration and average molecular weight and an inverse correlation with degree of branching. Notably, PLLH80 exhibits superior branching compared to PLLH99, demonstrating the potential for tailoring polymer properties via controlled lactide/5HDON ratios. Moreover, this work offers a robust tool for optimizing copolymer synthesis for diverse industrial applications.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".