Hybrid piezoelectric-magnetic, self-sensing actuator for vibration damping
Bibliographic record
Abstract
Stimuli-responsive soft actuators can actuate from an external stimulus. Compared to traditional actuators, these soft actuators offer advantages such as flexibility, conformability, better biomimicking ability, lower cost, and higher power to-weight ratio. These advantages are ideal for the vibration damping of transportation vehicles where there is a need for strong and lightweight designs whilst maintaining user comfort to encourage widespread public adoption. Piezoelectric materials are extensively used as sensors and actuators due to its piezoelectric property, and magnetic actuation is known for its accurate control. The current study investigates the development of a nanocomposite that can exhibit both piezoelectric and magnetic effects in terms of sensing and actuation, respectively. To achieve this, iron (III) oxide (Fe3O4) nanoparticles were attached to the surfaces of functionalized single walled carbon nanotubes (f-CNT), and the resulting nanofiller was embedded into a PVDF matrix. The Fe3O4 will provide magnetic actuation while the SWCNT will enhance the sensing performance of PVDF through its piezoelectric property. The proposed Fe3O4/f-CNT/PVDF showed effective vibration sensing and damping of excessive vibration on next generation of transportation vehicles for enhanced human safety and comfort. This research will assist in advancing multi stimuli-responsive materials and improve functionality and commerciality of soft smart material devices.
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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) | 0.002 | 0.001 |
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".