Rheological and physical properties of a nanocomposite of graphene oxide nanoribbons with polyvinyl alcohol
Bibliographic record
Abstract
In this work, the physicochemical and rheological properties of a nanocomposite of graphene oxide (GO) and thermoplastic polyvinyl alcohol (PVA) are reported. After adding deionized water to PVA, the sample was heated and mixed. The polymer solution was divided in two portions, each of them containing PVA in water. GO nanoribbons were added to one portion of the PVA solution in water to obtain the nanocomposite suspension (1% w/w). The similarity of the Fourier-transform infrared spectra of PVA and its related nanocomposite was assessed (both having similar peaks, an important observation as it indicated the water absorption on the samples). The scanning electron microscopy images showed that PVA lost its spherical shape in composites due to the GO sheets coating. The characteristic peaks O − ( m/ e 16) and OH − ( m/ e 17) were observed in the time-of-flight secondary ion mass spectrometry negative mode spectra of PVA and nanocomposite of GO nanoribbons with PVA. The analysis of the OH − , O − , C 2 H − , and C 2 − peaks of PVA and the nanocomposite of GO nanoribbons revealed that a reduction occurred for GO nanoribbons when coated with PVA. The rheological behaviors of samples in solution were studied and their shear rate versus shear stress were assessed. A non-Newtonian rheological behavior of the nanocomposite of GO nanoribbons with PVA was observed. The dielectric response of the nanocomposite of GO-PVA featured a simultaneous increase of both real and imaginary part of the complex permittivity toward low frequency due to the contribution of charge carriers. These results provide more understanding about the physical, mechanical, and dielectric properties of GO-PVA nanocomposite to make it appropriate in the construction of embedded capacitors. The novelty of our work lies in the rheological and physical properties of this nanocomposite in comparison with those of PVA that have not been previously reported.
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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".