High Performance Compatibilized Polyamide Composites Containing Graphene Nanoplatelets and Recycled Glass Fibers
Bibliographic record
Abstract
ABSTRACT Polyamide‐based composites reinforced with graphene nanoplatelets (GNPs) have exceptional potential as high‐performance functional materials. Non‐covalent modification with a trimellitic anhydride (TMA) coating agent improved the compatibility of the GNPs with polyamide 6,12 (PA), owing to the interactions between the anhydride moiety and the amide groups. The resulting well‐dispersed TMA‐GNPs promote higher crystallinity, with flexural modulus and impact strength improvements of up to 196% and 119%, reaching maximum values of 6.1 GPa and 80 J/m respectively. Maximum thermal and electrical conductivities of 3.3 W/m·K and 0.23 S/m, respectively, with a relative permittivity of 12.5 and maximum loss tangent value of 0.15 recorded at 30 GHz were achieved, rendering these materials suitable for microwave applications. Composites containing recycled glass fibers (rGFs) and a hybrid composite containing 10 wt.% rGF and 10 wt.% TMA‐GNP were also prepared and compared to the PA/TMA‐GNP composites. The GNPs coated the rGF, thus altering the interface with the PA matrix. The hybrid composite demonstrated comparable mechanical properties to the 20 wt.% TMA‐GNP composites, suggesting that this approach may provide an effective means to reduce the cost, while including recycled materials.
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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) | 0.001 | 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".