Demonstration of Low Frequency and High-Power Density Aln-Based Piezoelectric Vibration Energy Harvesters Using High Density Tungsten Proof Masses
Bibliographic record
Abstract
In this study, we experimentally evaluate the performance of piezoelectric energy harvesters integrated with tungsten proof masses. A wafer level process was successfully developed to fabricate multiple devices. Tungsten proof masses were integrated in MEMS harvesters, which previously used silicon proof masses. This integration helped reduce the resonant frequency of the harvesters closer to ambient vibration and enhance device performance. The results demonstrate significant improvement in the Q factor for the tungsten-integrated devices, increasing from 250 to 2000 for one of the devices. Normalized power densities of $244.8 \mathrm{~mW} \cdot \mathrm{~g}^{-2} \cdot \mathrm{~cm}^{-3}$ at 50 Hz and $69.4 \mathrm{~mW} \cdot \mathrm{~g}^{-2} \cdot \mathrm{~cm}^{-3}$ at 553 Hz were achieved for different devices. These results demonstrated that we have achieved some of the highest performance levels among piezoelectric energy harvesters while operating at low frequencies typical of available ambient vibration in the environment of IoT devices, without the need for vacuum packaging.
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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 | 0.001 |
| 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".