Interface adjustment between poly(ethylene terephthalate) and graphene oxide in order to enhance mechanical and thermal properties of nanocomposites
Bibliographic record
Abstract
Abstract There is great interest in the use of graphene and derivatives in the production of polymer nanocomposites as it provides improvements in the properties of the materials to which they are associated. Such improvements depend heavily on filler dispersion and the interaction between the nanomaterials and the matrix. This work aimed to study the compatibility of graphene oxide (GO) with a poly(ethylene terephthalate) matrix. For this, graphite was modified using Hummers method, using reaction times of 3 and 6 h. The obtained GO was functionalized with amine, amide, and magnetite groups (FGO). The effects of the oxidation degree, functionalization and concentration of the nanofillers on the dispersion and consequently on the properties of the polymer nanocomposites were evaluated. The nanocomposites were synthesized by the solid–solid deposition method followed by the melt mixing technique. It was observed that lower concentrations of nanofiller associated with the lower degree of oxidation and functionalization improved the interaction of the nanofillers with the matrix, which resulted in better mechanical properties under tensile stresses for strain at break, maximum stress, Young's modulus and toughness. It was also observed that the glass transition and crystallization of nanocomposites increased due to a nucleating effect of the nanofillers.
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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".