Micro-Electromagnetic Vibration Energy Harvesters: Analysis and Comparative Assessment
Bibliographic record
Abstract
The development of Micro-electro-magnetic Vibration Energy Harvesters (MEMVEHs) plays a crucial role in advancing self-powered nanophotonic, nanoelectronic, and nanosensor systems. As energy autonomy becomes critical for miniaturized devices, MEMVEHs offer a sustainable power source for low-power nanodevices operating in wireless sensor networks, wearable electronics, and biomedical implants. This study provides a comparative assessment of MEMVEH technologies and evaluates their integration potential within next-generation nanoscale systems, enabling enhanced performance, longevity, and energy efficiency of emerging nanotechnologies. Electromagnetic vibration energy harvesters (EMEHs) based on microelectromechanical system (MEMS) technology are promising solutions for powering small-scale, autonomous electronic devices. In this study, two electromagnetic vibration energy harvesters based on microelectromechanical (MEMS) technology are presented. Two models with distinct vibration structures were designed and fabricated. A permanent magnet is connected to a silicon vibration structure (resonator) and a tiny wire-wound coil as part of the energy harvester. The coil has a total volume of roughly 0.8 cm3. Two energy harvesters with various resonators are tested and compared. Model A’s maximum load voltage is 163 mV, whereas Model B’s is 208 mV. A maximum load power of 59.52 μW was produced by Model A at 347 Hz across a 405 Ω load. At 311.4 Hz, Model B produced a maximum load power of 149.13 μW while accelerating by 0.4 g. Model B features a larger working bandwidth and a higher output voltage than Model A. Model B performs better than Model A in comparable experimental settings. Simple study revealed that Model B’s electromagnetic energy harvesting produced superior outcomes. Additionally, it indicates that a nonlinear spring may be able to raise the output voltage and widen the frequency bandwidth.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".