Load sharing inside multi-layered graphene nanosheets under bending and tension
Bibliographic record
Abstract
Graphene nanosheets show unique material properties and are highly anisotropic in stiffness and strength . These materials are non-continuum in micro-structures. The mechanisms of load transfer from outside into the inner layers depend on the shear stress in the interphase between layers. In this work, the stress distribution in the layers and the interphases are investigated by using a modified shear-lag method, and the finite element results are also employed for comparison purpose. The loads examined include bending and tension. The effect of layer number and the equivalent shear modulus of the interphase are studied. The simulation results show that the length for saturated stress is around 20 nm for the case of 10 layers and an interphase shear modulus of 4.2 GPa. The shear modulus is sensitive to the load sharing efficiency. This work also reveals that the saturation length increases with an increase in the number of sheets in graphene nanosheets . This length increases from 5 nm to 60 nm when the sheet number changes from 5 to 20. The stresses are drastically varied and the interlayer shear stresses are the highest near the edge where the load is applied.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".