Optimizing Activators Regenerated by Electron Transfer for Atom Transfer Radical Polymerization of Methyl Methacrylate Initiated by Ethyl 2-bromopropionate
Bibliographic record
Abstract
In this study, we used ethyl 2-bromopropionate (EBrP) as an initiator of activators regenerated by electron transfer for atom transfer radical polymerization (ARGET ATRP) of methyl methacrylate (MMA). We investigated in detail the effect on polymerization of different kinds of reducing agents and ligands, the amounts of the reducing agent and catalyst, and reaction temperature. We determined the molecular weight and dispersity of the polymers by gel permeation chromatography (GPC). The results reveal glucose to be the best reducing agent for this system. The monomer conversion increased with increases in the reaction temperature and in the feeding amounts of the reducing agent and catalyst. The optimum amount of the reducing agent and minimal amount of catalyst required depend on the particular system. For example, we polymerized MMA with 200 ppm of catalyst and 15-fold of glucose/CuCl2 resulting in a PMMA with high Mn (Mn,GPC = 48 700, Mn,theo = 48 500) and low dispersity (1.27). The first-order kinetics show that the molecular weights increased linearly with the monomer conversion and are consistent with the theoretical values, the chain extension reaction and end group analysis results also demonstrate that the characteristics of polymerization process belong to a typical “living”/controlled radical polymerization. Moreover, 1H-NMR analysis results indicate the stereoregularity of the polymer is given priority over syndiotactic architecture and the effect of the type of ligand on the stereoregularity is very slight.
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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".