Wrinkling of flexoelectric nano-film/substrate systems
Bibliographic record
Abstract
Abstract The study of wrinkling mechanisms essentially helps to establish stable and controllable performance in electronic products. To gain some basic understanding of the wrinkling process in flexoelectric dielectrics, this paper models the wrinkling of nano-film/substrate systems, typically seen in stretchable electronics, subjected to substrate prestrain and voltage loading on electrodes. Flexoelectricity is considered through the constitutive equations proposed by Shen and Hu, and Euler–Bernoulli beam theory is applied to formulate the expressions of wrinkling wavelength and amplitude through the Ritz method. The effects of flexoelectricity, surface parameters, prestrain, applied voltage, structural scale etc on wrinkling behaviors, including wrinkling deformation and the wrinkling critical condition, are discussed. Results reveal that the action of both flexoelectric and surface effects is significant over only a small scale range, with film thickness less than 10 nm. Alongside these issues, the fundamental difference between flexoelectric and piezoelectric effects on wrinkling behaviors is highlighted. Piezoelectricity may act as a promoter or suppressor of wrinkling initiation and amplitude, depending on the applied voltage, while flexoelectricity not only reduces the critical prestrain or voltage required for wrinkling, but also decreases the wrinkling wavelength and amplitude.
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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".