Size effects on electromechanical coupling fields of a bending piezoelectric nanoplate due to surface effects and flexoelectricity
Bibliographic record
Abstract
Due to large surface to volume ratio and manifest strain gradients typically present in nanostructures, it is essential to incorporate both surface effects and flexoelectricity in studying the size-dependent electromechanical coupling behaviors of piezoelectric materials at the nano-scale. In the current work, a modified Kirchhoff plate model with the consideration of residual surface stress, surface elasticity, surface piezoelectricity, and flexoelectricity is developed to investigate the electroelastic responses and vibrational behaviors of a bending piezoelectric nanoplate (PNP). The governing equations and the corresponding boundary conditions accounting for both the surface effects and the flexoelectricity are derived by the variational principle. Ritz approximate solutions of the static bending and the free vibration indicate that these nano-scale features are more prominent for thinner plates with smaller thickness. The simulation results also reveal that the influence of the flexoelectricity and the surface effects upon the bending behaviors of the PNP depends on the applied electrical loading and the plate dimensions. Moreover, it is also observed that the frequency tuning of PNP-based nanoresonators by adjusting applied electrical load can be modified by both the flexoelectricity and the surface effects. The current work is expected to provide increased understanding on the theoretical basis for the design and applications of PNP-based nanodevices.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".