Influence of graphene‐based compounds on the mechanical toughness and thermal stability of polypropylene
Bibliographic record
Abstract
ABSTRACT A study of the improvement of the mechanical and thermal properties of nanocomposites prepared with polypropylene (PP) and different graphene samples [graphene oxide (GO), reduced GO (RGO), and commercial graphene (G)] is presented. X‐ray diffraction (XRD) and X‐ray photoelectron spectroscopy characterization were applied to the graphene samples. The nanocomposites were characterized by thermogravimetric analysis, XRD, differential scanning calorimetry, transmission electron microscopy (TEM), tensile, and impact resistance tests. PP/RGO nanocomposites showed significant improvement in mechanical and thermal properties. Sample PP/RGO‐0.75 resulted in an increment in Young's modulus (51%), tensile strength (24%), and elongation at break (15%). This is attributed to a good dispersion state, a higher crystallinity percentage, and a good interfacial adhesion between PP and RGO. Sample PP/RGO‐0.50 exhibited an increase of 197 °C in the temperature at which a loss in weight of 5% occurred, compared to that for pure PP. The height of stacked layers calculated by XRD measurements was similar to the value observed by TEM. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020, 137, 48258.
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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".