Vinyl-ester nanocomposites based on a binary-solvent system for well-exfoliated graphene and enhanced performance
Bibliographic record
Abstract
This paper introduces a green approach for preparing well-dispersed graphene nanoplates (GNPs) in vinyl-ester (VE) resin. The GNPs were exfoliated in a novel binary-solvent system comprised of a water and ethanol mixture (S nanocomposites). Water and ethanol were considered poor solvents for graphene, but a combination of them could be an ideal solvent mixture. TEM images of the S nanocomposites revealed well-exfoliated graphene sheets with a few wrinkles. Hence, the effective surface areas of the GNPs considerably increased, playing a vital role in enhancing the performance of the nanocomposites. With the incorporation of just 0.4 wt% of exfoliated GNPs (S2 nanocomposite) in the VE, the tensile strength, and toughness enhanced by 51%, and 63%, respectively. Fractography analysis indicated smooth fracture surfaces of pure resin changed to very rough surface structures for the S2 nanocomposite. The viscoelastic properties of the specimens indicated that directly loading GNPs into the resin decreased the glass transition temperature (Tg) of the VE resin. In contrast, the samples prepared by liquid-phase exfoliation enhanced the Tg of the VE. Lastly, the barrier properties of the S2 nanocomposite revealed that its water absorption decreased by 26% compared to the pure resin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".