Air gap optimization for output power and band width in out-of-plane vibration energy harvesters employing electrets
Bibliographic record
Abstract
We investigated the dependence of output power, frequency band width, and resonance frequency on the initial air gap for electret-based out-of-plane vibration energy harvesters, both numerically and experimentally. In this investigation, the external acceleration and surface charge densities of the electret were held constant. The numerical investigation predicted the following results: (1) an optimum value exists in the initial air gap to maximize the output power; and (2) enhanced electrostatic forces with decreasing the initial air gap emphasize the soft spring effect, which widens the frequency band width and lowers the resonance frequency. The experimental results showed behaviour consistent with the numerical predictions. The maximum output power in experiment was 4.0 μ W at the optimum initial air gap of 0.43 mm when the external acceleration and the frequency were 4.9 m s −2 and 102 Hz, respectively. With reducing the initial air gap to 0.28 mm, the frequency band width increased to 17 Hz, a 2.6-fold increase over the optimum initial air gap. The peak output power at the initial air gap of 0.28 mm was 2.7 μ W, when the external acceleration and frequency were 4.9 m s −2 and 96 Hz, respectively.
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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 | 0.001 |
| 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) | 0.000 | 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".