Stacking nuances modulate the mechanical properties of graphene/SnO <sub>2</sub> nanocomposites
Bibliographic record
Abstract
Abstract Due to its superior mechanical properties, graphene is widely used as reinforcement materials in nanocomposites. In this work, a series of indentation simulations was performed, using finite element method, to investigate the mechanical properties of graphene/TiO 2 and graphene/SnO 2 nanocomposite films. The force–displacement curves obtained from simulations were first compared to analytical results, which demonstrates that with increasing the thicknesses of metal oxide layers, the mechanical responses of nanocomposites exhibit a transition from non-linear behaviors to linear behaviors. Furthermore, consistent with literature works, increasing graphene volume fraction can enhance the Young’s modulus of the corresponding heterostructure. Interestingly, this enhancement can be modulated by nuances in stacking orders, i.e. layer arrangements, of nanocomposites. Through analyzing stress and strain distributions, the underlying mechanisms were proposed. Our results reported here provide comprehensive characterizations and understandings on the reinforcement effects of graphene on graphene/metal oxide nanocomposites.
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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".