Surface Stress Effect on Nonlinear Instability of Imperfect Piezoelectric Nanoshells under Combination of Hydrostatic Pressure and Lateral Electric Field
Bibliographic record
Abstract
In this paper, the nonlinear instability of piezoelectric cylindrical nanoshells under the combined radial compression and electrical load including the effects of surface free energy is studied. To consider the surface effects, the Gurtin-Murdoch elasticity theory is utilized along with the classical shell theory to develop an efficient size-dependent shell model. To satisfy the balance conditions on the surfaces of nanoshells, a linear variation of normal stress is assumed through the thickness of the bulk. Electrical field is also exerted along the transverse direction. Based on the virtual work principle, the size-dependent nonlinear governing differential equations are derived in which transverse displacement and Airy stress function are considered as independent variables. After that, a boundary layer theory is used incorporating the surface free energy effects in conjunction with the nonlinear prebuckling deformation, the large deflections in the postbuckling regime, and the initial geometrical imperfection. Finally, a two-stepped singular perturbation technique is employed to obtain the size-dependent critical buckling pressure and the associated postbuckling equilibrium path for alternative electrical loadings. It is revealed that the electrical load increases or decreases the critical buckling pressure and critical end-shortening of nanoshell which depends on the sign of applied voltage. Moreover, it is found that by taking surface free energy effects into account, the influence of electrical load on the postbuckling behavior of nanoshell increases.
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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".