Investigation of rheological properties of graphene oxide and its nanocomposite with polyvinyl alcohol
Bibliographic record
Abstract
This paper focuses on the rheological properties of graphene oxide (GO) and its nanocomposite with polyvinyl alcohol (PVA). The purpose of this paper is to compare the mechanical properties of these materials. GO is a nanomaterial that has been widely studied in engineering. The comparison of its rheological properties with those of its nanocomposite with PVA has been required. Rheology is an appropriate method for the comparative investigation of the mechancial behavior of these materials. The challenge has been to compare the rheological parameters of these materials regarding their mechanical properties. This investigation shows that GO and GO-PVA do not exhibit the same viscosity change at low shear rates. While GO shows a significant decrease of viscosity, GO-PVA shows a steady state behavior at these shear rates. However, both GO and GO-PVA show a constant viscosity at high shear rates. These materials show the same behavior concerning the change of their viscosity versus shear strain or time. The changes of torque versus shear strain and time for GO and GO-PVA are representes as increasing curves and lines, respectively. The increase of shear stress versus shear rate or shear strain coincide for both materials. However, as expected, the first change represents an increasing line and the second one a curve. This original investigation shows the difference between the mechanical behaviour of GO and GO-PVA concerning the change of their viscosity at low shear rates and their similarity at high shear rates. Moreover, this study gives new results concernig the change of other parameters as described above. The unique physicochemical properties of GO have made it an important candidate in engineering and materials science. This investigation can lead to a better understanding of the rheological properties of GO and GO-PVA for the improvement of these applications.
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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".