Bias-Sensitive 128x128 hand-held TOBE ultrasound probe based on electrostrictive PMN-PT for photoacoustic applications (Withdrawal Notice)
Bibliographic record
Abstract
Although fully-wired 2D ultrasound arrays can provide idealistic Ultrasound (US) image quality, commercial piezo-based 2D arrays still remain opaque for through-illumination photoacoustic (PA) applications. Also, fabricating a fully-wired N×N 2D array would become impractical for large arrays. Alternatively, Top-Orthogonal-to-Bottom Electrode (TOBE) arrays, also known as Row-Column Arrays (RCA), significantly reduce the number of active channels from N×N channels down to 2×N with some applications in volumetric imaging. This makes the fabrication of large-area TOBE arrays possible for a more excellent spatial resolution compared to the state-of-the-art Matrix probes. However, transparent TOBE arrays would be desirable for PA applications facilitating through-illumination light delivery. This could enable improved SNR compared to opaque ultrasound arrays with oblique illumination and also lead to compact US/PA probe design. Electrostrictive lead magnesium niobate (PMN) with low lead titanate (PT) doping can be a good candidate for these PA applications. Ultrasound transducer arrays made of PMN-PT have shown promise for transparent arrays. These electrostrictive materials do not exhibit a piezoelectric effect without an applied bias voltage and have acceptable optical transparency when polished on both sides. TOBE arrays made of these electrostrictive PMN-PT can be used for 3D aperture-coded PA imaging.
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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) | 0.009 | 0.003 |
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".