Efficient Driving of Piezoelectric Transducers Using a Biaxial Driving Technique
Bibliographic record
Abstract
Efficient driving of piezoelectric materials is desirable when operating transducers for biomedical applications such as high intensity focused ultrasound (HIFU) or ultrasound imaging. More efficient operation reduces the electric power required to produce the desired bioeffect or contrast. Our preliminary work [Cole et al. Journal of Physics: Condensed Matter. 2014;26(13):135901.] suggested that driving transducers by applying orthogonal electric fields can significantly reduce the coercivity that opposes ferroelectric switching. We present here the experimental validation of this biaxial driving technique using piezoelectric ceramics typically used in HIFU. A set of narrow-band transducers was fabricated with two sets of electrodes placed in an orthogonal configuration (following the propagation and the lateral mode). The geometry of the ceramic was chosen to have a resonance frequency similar for the propagation and the lateral mode. The average (± s.d.) resonance frequency of the samples was 465.1 (± 1.5) kHz. Experiments were conducted in which each pair of electrodes was driven independently and measurements of effective acoustic power were obtained using the radiation force method. The efficiency (acoustic/electric power) of the biaxial driving method was compared to the results obtained when driving the ceramic using electrodes placed only in the pole direction. Our results indicate that the biaxial method increases efficiency from 50% to 125% relative to the using a single electric field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".