Nonlinear Multi-Mode Wideband Piezoelectric MEMS Vibration Energy Harvester
Bibliographic record
Abstract
The conventional micro-electromechanical system (MEMS) energy harvesters can only generate voltage disadvantageously in a narrow bandwidth at higher frequencies. In this paper, we propose a piezoelectric MEMS harvester with the capability of vibrating in multi-degrees-of-freedom, whose operational bandwidth is enhanced by taking the advantage of both the multimodal and nonlinear mechanisms. The proposed harvester has a symmetric structure with a doubly-clamped configuration enclosing three proof masses in distinct locations. Thanks to the uniform mass distribution, the energy harvesting efficiency can be considerably enhanced. To determine the optimum geometry for the preferred nonlinear behavior, we propose an automated design and optimization methodology based on the genetic algorithm (GA). By using the micromachining process, our optimized harvester with a total volume of 4.1 mm3has been fabricated and measured. The prototype measurements demonstrate that our proposed piezoelectric MEMS harvester is able to generate voltage with a frequency bandwidth of 59 Hz, ranging from 227 to 286 Hz. When the device operates at its second-mode resonant frequency, the nonlinear behavior can be obtained with an extremely small magnitude of the base excitation (i.e., 0.2 m/s2). Its normalized power density (NPD) of 595.12 (μW·cm-3·m-2·s4) is found to be superior to any previously reported wideband aluminum nitride (AlN) piezoelectric MEMS harvesters in the literature.
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 | 0.000 |
| 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".