Hybrid Piezoelectric–Magnetic Self‐Sensing Actuator using Novel Dual‐Alignment Magnetic/Mechanical Processing for Vibration Control of Whole‐Body Vibrations
Bibliographic record
Abstract
Multi‐stimuli‐responsive/‐functional polymeric materials can respond to numerous stimuli and execute multiple tasks, overcoming barriers faced by single‐stimuli materials. Herein, the development of hybrid piezoelectric–magnetic self‐sensing actuator (HPMSA) that can both sense and actuate is proposed. This iron oxide/functionalized carbon nanotube/polyvinylidene fluoride film optimizes both piezoelectric and magnetic properties through dual‐alignment fabrication, utilizing strong element bonds for simultaneous alignment. Magnetic nanoparticles are advantageous over nanorods due to latter's randomized shape anisotropy decreasing magnetization. The dual magnetic and mechanical processing increases polar β‐crystal content to 88%, where magnetic alignment alone increases degree of crystallinity to 66%. As a vibration damper, HPMSA operates within 40–600 Hz frequency, with a sensing sensitivity of 2.5 mV g−1 and 0.72 m s−2 weighted acceleration damping, lowering passenger health risks. Piezoelectric and magnetic relationship shows 0.19 V increase with 125 mT applied. The flexible HPMSA can integrate onto a curved surface and sense/dampen vibrations of an air motor, propeller drone, and simulated tremors. The HPMSA provides tremendous potential and understanding into multi‐stimuli‐responsive/functional materials, simultaneous alignment, and vibration control in the next generation of transportation vehicles for human safety.
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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 | 0.000 |
| 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".