Modeling the influence of surface effect and molecular force on pull-in voltage of rotational nano–micro mirror using 2-DOF model
Bibliographic record
Abstract
Herein, the torsion–bending coupled pull-in instability of rotational electromechanical nano–micro mirror is investigated using a two-degree-of-freedom (2-DOF) model. Two nano-scale phenomena (i.e., surface effect and molecular van der Waals attraction) are incorporated in the model. None of the previous 2-DOF models have taken these nano-scale effects into account. Results reveal that the influences of surface effects and intermolecular force on the coupled pull-in voltage of the nano–micro mirror highly depend on the geometrical characteristics of the system. It is found that if the mirror dimensions are of the order of the material length scale parameters, the pull-in characteristics computed via the present 2-DOF model will highly differ from those predicted by previous one-degree-of-freedom (1-DOF) models. Interestingly, the influence of surface effects on pull-in voltage of the system highly depends on the bending/torsion coupling ratio. Moreover, results show that the van der Waals force can reduce the pull-in voltage of the mirror. This deteriorating effect is more highlighted in the torsional mode than the bending mode.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".