A Dual-Frequency Lens-Focused Endoscopic Histotripsy Transducer
Bibliographic record
Abstract
A forward-looking miniature histotripsy transducer has been developed that incorporates an acoustic lens and dual-frequency stacked transducers. An acoustic lens is used to increase the peak negative pressure through focal gain and the dual-frequency transducers are designed to increase peak negative pressure by summing the pressure generated by each transducer individually. Four lens designs, each with an f -number of approximately 1, were evaluated in a PZT5A composite transducer. The finite-element model (FEM) predicted axial beamwidths of 1.61, 2.40, 2.84, and 2.36 mm for the resin conventional, resin Fresnel, silicone conventional, and silicone Fresnel lenses, respectively; the measured axial beamwidths were 1.30, 2.28, 2.71, and 2.11 mm, respectively. Radial beamwidths from the model were between 0.32 and 0.35 mm, while measurements agreed to within 0.2 mm. The measured peak negative was 0.150, 0.124, 0.160, and 0.160 MPa/V for the resin conventional, resin Fresnel, silicone conventional, and silicone Fresnel lenses, respectively. For the dual-frequency device, the 5-MHz (therapy) transducer had a measured peak negative pressure of 0.136 MPa/V for the PZT5A composite and 0.163 MPa/V for the PMN-PT composite. The 1.2-MHz (pump) transducer had a measured peak negative pressure of 0.028 MPa/V. The pump transducer significantly lowered the cavitation threshold of the therapy transducer. The dual-frequency device was tested on an ex vivo rat brain, ablating tissue at up to 4-mm depth, with lesion sizes as small as [Formula: see text].
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".