Multifunctional Poly(Vinylidene Fluoride) and Styrene Butadiene Rubber Blend Magneto-responsive Nanocomposites Based on Hybrid Graphene Oxide and Fe3O4: Synthesis, Preparation and Characterization
Bibliographic record
Abstract
Abstract Functional nanocomposites based on a blend of styrene butadiene rubber (SBR) and polyvinylidene difluoride (PVDF) as the matrix reinforced by a hybrid nanofiller system of graphene oxide nanosheets (GOn) and ferromagnetic nanoparticles (Fe3O4) at different weight ratios (3.75:1.25; 2.5:2.5 and 1.25:3.75) were successfully prepared and characterized to determine the effect of nanofillers hybridization in improving the structural,mechanical, dielectric/electrical, magnetic and thermal properties of a polymer blend (PVDF-SBR) for electromagnetic interference (EMI) shielding applications. Due to the synergy between both nanofillers, it was found that the Young’s modulus of the nanocomposite containing 5 wt.% of the hybrid nanofiller was significantly improved (87%), while the strain at yield remained constant due to the low particle content and the rubbery effect of the SBR copolymer. In addition, the degradation temperature of the matrix was shifted from 464°C to 472 °C with the addition of 2.5:2.5 of GOn:Fe3O4. Finally, the hybrid reinforcement also had a positive effect on the electrical and magnetic properties of the nanocomposites with an improvement that exceeds 30%. By careful selection of synthetic techniques and understanding/exploiting the unique physics of the polymeric nanocomposites in such materials, novel functional polymer-inorganic nanocomposites can be designed and fabricated for new interesting in magneto applications such as superparamagnetism, electromagnetic wave absorption, and electromagnetic interference shielding.
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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".