Insights into the polymerization kinetics of thermoresponsive polytrimethylene carbonate bearing a methoxyethoxy side group
Bibliographic record
Abstract
Abstract The ring‐opening polymerization kinetics of 5‐[2‐(2‐methoxyethoxy)‐ethoxymethyl]‐5‐methyl‐1,3‐dioxa‐2‐one (TMOE‐2) and 5‐[2‐{2‐(2‐methoxyethoxy)ethyoxy}‐ethoxymethyl]‐5‐methyl‐1,3‐dioxa‐2‐one (TMOE‐3) was investigated using different catalysts with the aim to improve control over molecular weight. The possibility of monomer impurities driving the variability in molecular weight that has been seen in different reports, was assessed and evidence of catalysis via an imidazole impurity was found. The catalysts 1,5,7‐triazobicyclo(4.4.0)dec‐5‐ene (TBD), hydrogen chloride in diethyl ether (HCl·Et2O), stannous 2‐ethylhexanoate (SnOct2), and catalyst free thermal polymerizations were conducted to understand the mechanisms influencing the molecular weight. TBD and HCl·Et2O consistently achieved high conversion of the monomer; however, molecular weights greater than 7,000 Da could not be achieved due to competing side reactions. SnOct2 catalyzed and catalyst free thermal polymerizations were highly influenced by monomer purity and achieved lower conversion than TBD and HCl·Et2O. Understanding these mechanisms will guide future synthesis of poly(TMOE‐2) and poly(TMOE‐3) for biomedical 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 | 0.000 |
| 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) | 0.001 | 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".