Effect of Ligands in MMA AGET ATRP in 2L Stirred Tank Emulsion Reactor
Bibliographic record
Abstract
Atom Transfer Radical Polymerization (ATRP) in a 2L emulsion batch reactor system was initiated by using the Activator Generated by Electron Transfer (AGET) technique to produce Poly (Methyl Methacrylate) (PMMA).The reactants were composed of Ethyl-2-bromoisobutyrate (EBiB) as the initiator, polyoxyethylene (20) oleyl ether (Brij98) as nonionic surfactant and ascorbic acid as the reducing agent.In addition, the catalyst complex consists of copper bromide (CuBr2) and different ligands such as Triphenylphosphine (PPh3), 1, 10-Phenanthroline (Phenol), and Vitamin D. The effect of using PPh3, Phenol and Vitamin D as novel ligands was investigated to produce PMMA polymers having the features obtained through controlled polymerization.The reaction follows a two-step experimental procedure, during which a transition from microemulsion to emulsion takes place.The mixing process between the organic phase and the aqueous phase was carried out under sufficient amount of air for simplification purposes.However, the reaction is usually sensitive to air and therefore a particular precaution was taken when purging the system inside the reactor.Gravimetric method was used to measure the monomer conversion.Characterization of PMMA samples was done by means of GPC to measure the molecular weight and the polydispersity of the product.FTIR analysis was performed to characterize the polymer product.After 5h of reaction, high monomer conversion was obtained using Phenol and gradually increasing up to 93% with low number average molecular weight of 10,158 g/mol and a relatively narrow PDI of 1.58.A narrower PDi was obtained with Phenol compared with PPh3 and Vitamin D.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".