Copper (0) Mediated Single Electron Transfer-Living Radical Polymerization of Methyl Methacrylate: Functionalized Graphene as a Convenient Tool for Radical Initiator
Bibliographic record
Abstract
Polymer nanocomposites have been synthesized by the covalent addition of bromide-functionalized graphene (Graphene-Br) through the single electron transfer-living radical polymerization technique (SET-LRP). Graphite functionalized with bromide for the first time via an efficient route using mild reagents has been designed to develop a graphene based radical initiator. The efficiency of sacrificial initiator (ethyl α-bromoisobutyrate) has also been compared with a graphene based initiator towards monitoring their Cu(0) mediated controlled molecular weight and morphological structures through mass spectroscopy (MOLDI-TOF) and field emission scanning electron microscopy (FE-SEM) analysis, respectively. The enhancement in thermal stability is observed for graphene-grafted-poly(methyl methacrylate) (G-g-PMMA) at 392 °C, which may be due to the influence ofthe covalent addition of graphene, whereas the sacrificial initiator used to synthesize G-graft-PMMA (S) has low thermal stability as analyzed by TGA. A significant difference is noticed on their glass transition and melting temperatures by DSC. The controlled formation and structural features of the polymer-functionalized-graphene is characterized by Raman, FT-IR, UV-Vis spectroscopy, NMR, and zeta potential measurements. The wettability measurements of the novel G-graft-PMMA on leather surface were found to be better in hydrophobic nature with a water contact angle of 109 ± 1°.
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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) | 0.000 | 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".