Influence of graphene oxide and graphene nanosheet on the properties of polyvinylidene fluoride nanocomposites
Bibliographic record
Abstract
In this study, graphene oxide (GO) and graphene nanosheets (GN) were prepared using natural graphite as a reinforcing agent, and different filler contents (1, 2, 3, and 4 wt%) were used to produce nanocomposites based on polyvinylidene fluoride (PVDF). In particular, a melt‐blending method was used as a 10 wt% masterbatch was prepared and then diluted to get the final samples via compression molding. A complete characterization in terms of X‐ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM) confirmed that GO and GN were of high structural quality. Then, the nanocomposites were characterized in terms of thermal, rheological, electrical, and mechanical performances. The thermal stability of neat PVDF (400°C) was found to increase with both fillers addition reaching at 3 wt% 445 and 463°C for GO and GN, respectively. For the same concentration, the PVDF Young's modulus was found to increase by 32% for GN, while only a 7% gain was observed for GO. Similarly, the rheological and electrical resistivity results showed that GN was more effective than GO in improving the performances of these nanocomposites, with an optimum ∼3 wt% for the conditions tested. POLYM. COMPOS., 39:2932–2941, 2018. © 2017 Society of Plastics Engineers
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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".