Synthesis of polydopamine‐coated graphene–polymer nanocomposites via RAFT polymerization
Bibliographic record
Abstract
ABSTRACT Graphene‐polymer nanocomposites have significant potential in many applications such as photovoltaic devices, fuel cells, and sensors. Functionalization of graphene is an essential step in the synthesis of uniformly distributed graphene‐polymer nanocomposites, but often results in structural defects in the graphitic sp 2 carbon framework. To address this issue, we synthesized graphene oxide (GO) by oxidative exfoliation of graphite and then reduced it into graphene via self‐polymerization of dopamine (DA). The simultaneous reduction of GO into graphene, and polymerization and coating of polydopamine (PDA) on the reduced graphene oxide (RGO) surface were confirmed with XRD, UV–Vis, XPS, Raman, TGA, and FTIR. The degree of reduction of GO increased with increasing DA/GO ratio from 1/4 to 4/1 and/or with increasing temperature from room temperature to 60 °C. A RAFT agent, 2‐(dodecylthiocarbonothioylthio)−2‐methylpropionic acid, was linked onto the surface of the PDA/RGO, with a higher equivalence of RAFT agent in the reaction leading to a higher concentration of RAFT sites on the surface. Graphene‐poly(methyl methacrylate), graphene‐poly( tert ‐butyl acrylate), and graphene‐poly( N ‐isopropylacrylamide) nanocomposites were synthesized via RAFT polymerization, showing their characteristic solubility in several different solvents. This novel synthetic route was found facile and can be readily used for the rational design of graphene‐polymer nanocomposites, promoting their applications. © 2013 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2013 , 51 , 3941–3949
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".