Bibliographic record
Abstract
Mechanical metamaterials have emerged as a new class of functional materials with unusual material properties. An important example of mechanical metamaterials is auxetic material with tunable negative Poisson's ratio. Such materials are attracting considerable interests due to their promising applications in various engineering sectors. Although enormous studies have been performed to present negative Poisson's ratio properties in various materials, research work on the auxetic behavior of graphene reinforced metal nanocomposites is still at its early stage. The present work develops a graphene origami structure to tune the negative Poisson's ratio in graphene reinforced metal nanocomposites. Various graphene origami structures, including Miura pattern, Waterbomb pattern, and Yoshimura pattern, are formed with the assistance of surface hydrogen functionalization in graphene sheet. The shape of the graphene origami can be tuned and controlled by changing the content and width of H adatoms in the creases. The designed graphene origami is embedded into a metal matrix to achieve the tunability of negative Poisson's ratio. Our results demonstrate that a higher content of graphene origami in composites can lead to a larger negative Poisson's ratio. In addition, the ambient conditions have considerable effects on the auxetic behaviors of metal/graphene metamaterials. To facilitate structural analysis and engineering design, a modified micromechanics model capable of accurately predicting the mechanical properties of the nanocomposites is also developed based on physics embedded machine learning technique.
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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".