Investigating the Microstructure and Mechanical Properties of Aluminum-Matrix Reinforced-Graphene Nanosheet Composites Fabricated by Mechanical Milling and Equal-Channel Angular Pressing
Bibliographic record
Abstract
Layered-graphene reinforced-metal matrix nanocomposites with excellent mechanical properties and low density are a new class of advanced materials for a broad range of applications. A facile three-step approach based on ultra-sonication for dispersion of graphene nanosheets (GNSs), ball milling for Al-powder mixing with different weight percentages of GNSs, and equal-channel angular pressing for powders' consolidation at 200 °C was applied for nanocomposite fabrication. The Raman analysis revealed that the GNSs in the sample with 0.25 wt.% GNSs were exfoliated by the creation of some defects and disordering. X-ray diffraction and microstructural analysis confirmed that the interaction of the GNSs and the matrix was almost mechanical, interfacial bonding. The density test demonstrated that all samples except the 1 wt.% GNSs were fully densified due to the formation of microvoids, which were observed in the scanning electron microscope analysis. Investigation of the mechanical properties showed that by using Al powders with commercial purity, the 0.25 wt.% GNS sample possessed the maximum hardness, ultimate shear strength, and uniform normal displacement in comparison with the other samples. The highest mechanical properties were observed in the 0.25 wt.% GNSs composite, resulting from the embedding of exfoliated GNSs between Al powders, excellent mechanical bonding, and grain refinement. In contrast, agglomerated GNSs and the existence of microvoids caused deterioration of the mechanical properties in the 1 wt.% GNSs sample.
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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".