Evaluation of Piezoelectric Ceramics for use in Miniature Histotripsy Transducers
Bibliographic record
Abstract
Five piezoelectric ceramics were evaluated for use in miniature broadband histotripsy transducers. The materials tested were PZT-5A, PZT-5H (CTS 3203HD), Pz39, Pz54 and PMN-38. 5 MHz, 1–3 dice and fill piezo/epoxy composites were fabricated for all except for Pz39, which is a low acoustic impedance porous ceramic. Four air-backed transducers for each material were built by bonding the composite to an 8 mm machined aluminum lens with a 7 mm focus and depositing a Parylene-C matching layer. The performance of each material was evaluated by measuring the transducers' transmitting sensitivity, cavitation threshold voltage in water and surface pressure versus drive voltage, and then testing them to failure. Measured sensitivities were compared to FEM model results, and HITU Simulator V2.0 was used to non-linearly extrapolate the results. Transducers were tested using 8-cycle, 1 kHz PRF pulses. As expected, the measured sensitivity for transducers of each material increased with increasing dielectric constant, while cavitation threshold voltages in water decreased. All Pz39 transducers failed when driven at or above 264 Vpp. All PMN38 transducers failed due to high instantaneous current. The most common failure mode was Parylene delamination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".