Steering single-element lead zirconate titanate ultrasound transducers using biaxial driving
Bibliographic record
Abstract
Previous work has shown that biaxial driving using two phase-offset orthogonal electric fields (propagation and lateral) improves the efficiency of ferroelectric materials by reducing coercivity and, hence, energy dissipation. In the current investigation, we demonstrated the capability of the biaxial method to steer ultrasound waves in single-element piezoceramic transducers made of prismatic lead zirconate titanate (PZT). We conducted finite element analysis simulations for 133 kHz (model 1) and 470 kHz biaxial (model 2) transducers models. We performed experimental validation with biaxially driven single-element transducers (n = 3) operating at an average frequency of 131 kHz with the same characteristics as model 1. For both models, we found non-symmetric steering that was a function of both the phase and power of the second electric field. At a constant electrical power (1 W) on the propagation electrodes, simulations for the 133 kHz model predicted maximal steering of 10.3°, 22.6°, and 30.9° for lateral electrode powers of 0.1 W, 0.5 W, and 1.0 W, respectively. Experimentally, for model 1, the maximal steering was 11.7° ± 1.9°, 23.5° ± 3.5°, and 30.2° ± 4.4° for the lateral electrode powers of 0.1 W, 0.5 W, and 1.0 W, respectively. Simulations for the 470 kHz model predicted maximal steering of 8.8°, 16.1°, and 27° for lateral electrode powers of 0.1 W, 0.5 W, and 1.0 W, respectively. Simulations showed that the cause of the steering asymmetry was a non-uniform shear deformation associated with the slightly off-resonance lateral electric field driving frequency. This is the first demonstration of ultrasound steering using a single-element transducer, which can have important applications for ultrasound focusing with phased arrays.
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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".