Applications of a Novel Tunable Piezoelectric Vibration Energy Harvester
Bibliographic record
Abstract
Conversion of ambient energy to usable electrical energy is attracting attention from researchers since providing a maintenance-free power source for the sensors is critical in any IoT (Internet of Things)-based system and in SHM (structural health monitoring). Continuous health monitoring of structures is advantageous since the damage can be identified at inception and the necessary action taken. Sensor technology has advanced significantly, and MEMS (microelectromechanical systems)-based low-power sensors are available for incorporating into large structures. Relevant signal conditioning and transmission modules have also evolved, making them power-efficient and miniaturized. Various micro wireless sensor nodes (WSN) have also been developed in recent years that require very little power. This paper describes the applications of a novel tunable piezoelectric vibration energy harvester (PVEH) for providing autonomous power to low-power MEMS sensors for use in IoT and remote SHM. The novel device uses piezoelectric material and an ionic polymer-metal composite (IPMC) and enables electrical tuning of the resonant frequency using a small portion of the power generated.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| 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 teacher head, 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".