Surface Bending Resistance in Architected Nanoporous Metallic Materials
Bibliographic record
Abstract
Abstract Finite element method (FEM) is considered as a powerful tool for predicting the mechanical behavior of complex structures. However, the commercially available numerical packages based on FEM are mainly limited to the evaluation of multiphysical properties at the continuum scale and are unable to accurately evaluate the response of nanomaterials since the dominant surface effects in nanoscale analysis are overlooked. In this study, our introduced numerical methodology not only incorporates the effects of surface residual stress and surface tensile stiffness based on the Gurtin–Murdoch surface elasticity but also takes into account the bending stiffness of nanosurfaces in the numerical analysis. The computational results reveal that the stress concentration in nanoporous metallic materials is affected by the void geometry and is enhanced by the surface bending stiffness. In addition, the effect of void geometrical parameters on the elastic properties of nanoporous metallic metamaterials with negative Poisson's ratio is studied and the mechanism of surface tensile/bending stiffness is revealed in detail. The results show that the surface bending stiffness increases the effective Young's modulus of nanoarchitected metallic materials with negative Poisson's ratio and randomly distributed nanopores.
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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".