Analysis of nonlinear bending behavior of nano-switches considering surface effects
Bibliographic record
Abstract
Nano-switch structures are important control elements in nanoelectromechanical systems and have potential applications in future nanodevices. This paper analyzes the effects of surface effects, geometric nonlinearity, electrostatic forces, and intermolecular forces on the nonlinear bending behavior and adhesion stability of nano-switches. Based on the Von Karman geometric nonlinearity theory, four types of boundary conditions for the nano-switch structure were specifically calculated. The results show that surface effects have a significant impact on the nonlinear bending and adhesion stability of nano-switches. Surface effects increase the adhesion voltage of the nano-switch and decrease its adhesion displacement, and as the size of the nano-switch structure increases, the impact of surface effects decreases. A comparative analysis of the linear theory and the nonlinear theory results shows that the adhesion voltage predicted by the linear theory is smaller than that predicted by the nonlinear theory. The effect of geometric nonlinearity increases as the size of the nano-switch structure increases, as the distance between the electrodes increases, and as the aspect ratio of the nano-switch structure increases. These findings provide theoretical support and reference for the design and use of future nanodevices and nanoelectromechanical systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".