One-Pot Controlled Synthesis of Homopolymers and Diblock Copolymers Grafted Graphene Oxide Using Couplable RAFT Agents
Bibliographic record
Abstract
An original strategy is presented to synthesize homopolymers and diblock copolymers grafted graphene oxide by simultaneous coupling reaction and RAFT process. Z-functionalized S -methoxycarbonylphenylmethyl S′ -3-(trimethoxysilyl)propyltrithiocarbonate (MPTT) and R-functionalized S -4-(trimethoxysilyl)benzyl S′ -propyltrithiocarbonate (TBPT) were used as couplable RAFT agents to prepare the target nanocomposites. Under similar conditions, MPTT-mediated grafting reaction was liable to afford grafted chains with shorter chain length, narrower molecular weight distribution and lower grafting density than TBPT-based reaction owing to increased shielding effect and different grafting process. The grafted polymers had nearly controlled molecular weight and polydispersity ranging between 1.11 and 1.38, and the apparent molar grafting ratio was estimated to be 73.6–220 μmol/g as the molecular weights of grafted polymers were in the range of 3980–12500 g/mol. The improved solubility and dispersibility of GO–polymer composites in various solvents comprising hexane and water confirmed their amphiphilicity. The grafting process offers an opportunity to alter GO morphologies, and surface morphologies involving nanosheets, nanoparticles, and nanorods were observed as the composites were dispersed in different solvents with the aid of sonication treatment. This tandem approach is promising for surface modification of solid substrates with hydroxyl surface due to its mild conditions, straightforward synthesis and good controllability.
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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 | 0.001 |
| 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".