Elevated‐temperature mechanical performance of <scp>GFRP</scp> composite with functionalized hybrid nanofiller
Bibliographic record
Abstract
Abstract In this article, alteration in the mechanical performance of glass fiber/epoxy (GE) composite due to individual and simultaneous incorporation of multi‐walled carbon nanotubes (CNT) and multi‐layered graphene sheets (MLG) in both their pristine and oxidized forms are discussed. Further, the effect of two different nanofiller concentrations (0.1 and 0.3 wt%) were also studied to optimize the performance. Flexural testing of these composites was performed at room temperature (RT), 70 and 110°C in‐situ temperatures to understand their temperature dependence behavior. From all considered composites, GE composite with 0.1 wt% of oxidized CNT and MLG mixture (O‐(CNT‐MLG)) (1:1) showed best flexural performance at all the in‐situ temperatures. The presence of oxidized CNTs and MLGs in the GE composite provided a synergetic strengthening effect like CNT pull‐outs and crack bridging confirmed through SEM imaging. Besides, oxidation helped in the dispersion of CNT and MLG in the composite. Glass transition temperature (Tg) of all the considered composites was evaluated using Differential Scanning Calorimetry (DSC). Fourier Transformed Infra‐red Spectroscopy (FTIR) was also conducted to confirm the functionalization of CNT and MLG after oxidation. During the fractography study, these composites showed variation in fiber/matrix interfacial bonding, matrix deformation, dispersion of nanofillers and fiber imprints, which helped to understand different failure modes responsible for the gross failure of the composites.
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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".