Static and Dynamic Analysis of a Clamp-Clamp Nano-Beam Under Electrostatic Actuation and Detection Considering Intermolecular Forces
Bibliographic record
Abstract
Micro/nano gyroscopes which can measure angular rate or angle are types of merging gyroscope technology with MEMS/NEMS technology. They have extensively used in many fields of engineering, such as automotive, aerospace, robotics and consumer electronics. There are many studies of a variety of gyroscopes with various drive and detect methods and different resonator structures in last years. In case of electrostatically actuated and detected beam micro/nano-gyroscopes, DC voltages are applied in driving and sensing directions and AC voltage is utilized in driving direction in order to excite drive oscillation. The intermolecular surface forces are especially significant when the gyroscopes are working in vacuum without the effect of capillary forces and the separations between movable components are in the sub-micrometer range. In this paper, a new model is used to study the static and dynamic behavior and pull-in instability of the vibratory clamped-clamped beam micro/nano gyroscopes. The system is operated by electrostatic mechanisms and subjected to base rotation. In this model, the effects of the intermolecular attractions and fringing electrostatic fields are accounted. Nonlinear differential equations governing the dynamic motion of the system are obtained using extended Hamilton principle. The static and dynamic problems are solved for various values of nondimensional design parameters.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".