Enhanced Performance of 200-kHz PMN-PT Crystal Transducers Through Medium-Temperature AC Poling and Electrically Parallel Stacking
Bibliographic record
Abstract
Despite their excellent piezoelectric properties, relaxor-based ferroelectric crystals have not been widely used in medium- to low-frequency ultrasound transducers because of the low sensitivity arising from a low capacitance and the low signal intensity due to a weak coercive field of the piezoelectric materials. In this study, a new type of transducer has been designed and fabricated by stacking two PMN-0.27PT crystals of opposite polarizations poled under optimized poling conditions, which exhibits an enhanced element capacitance and improved piezoelectric performance, leading to a better sensitivity and a broader bandwidth. It is found that using the optimized condition of low-voltage alternative current poling (ACP) (square wave 205 Vrms/mm, three cycles at 0.1 Hz) at a medium temperature of $65~^{\circ }$ C [medium-temperature low-voltage ACP MT-LV ACP)], the relative permittivity of the crystal is increased by 16%, the electromechancial coupling factor increased by 6%, and the piezoelectric coefficient increased by 27%, compared with the conventional direct current poling at room temperature (RT DCP). The 200-kHz single-element transducer fabricated from the MT-LV ACP single-layer PMN-0.27PT crystal exhibits a -6-dB bandwidth that is increased by 6.4% and a receiver free field voltage response that is increased by 23.6%, respectively, compared with a similar transducer made from RT DCP single-layer PMN-0.27PT. In addition, the transducer fabricated from two stacked PMN-0.27PT platelets of identical thicknesses but opposite poling directions not only produce similar center frequency and bandwidth as the transducer made from single layer crystal of the same height but also produce quadrupled element capacitance, which leads to a much better electrical impedance match, resulting in a sensitivity increase up to 224%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".