Carbon nanotube and graphene reinforced magnesium matrix composites: A state-of-the-art review
Bibliographic record
Abstract
Magnesium (Mg) composites reinforced with carbon-based nanomaterial (CBN) often exhibit low density, enhanced strength, good conductivity, improved wear resistance, and excellent biocompatibility when compared to current industry Mg alloys. This review aims to critically evaluate recent developments in Mg-CBN composites and is divided into five sections: First, a brief introduction to Mg-CBN composites is provided, followed by a discussion of different fabrication techniques for these composites, including powder metallurgy, casting, friction stir processing, and selective laser melting. A particular focus is on the current processing challenges, including dispersion strategies to create homogeneous Mg-CBN composites. The effect of processing on the quantifying disorder in CBNs and distinguishing different sp2 carbon materials is also highlighted. Then, the effect of CBN on various properties of Mg-CBN composites is thoroughly analyzed, and the strengthening efficiency of CNTs and graphene in the Mg matrix is examined. Finally, the potential applications of Mg-CBN composites in various industries are proposed, followed by a summary and suggestions for future research directions in the field of Mg-CBN 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".