Transparent Dual-Frequency CMUT Arrays for Photoacoustic Imaging
Bibliographic record
Abstract
The opaque ultrasound transducers used in conventional photoacoustic imaging systems necessitate oblique light delivery, which gives rise to some disadvantages such as inefficient target illumination and bulky system size. This work proposes a transparent capacitive micromachined ultrasound transducer (CMUT) linear array with dual-band operation for through-illumination photoacoustic imaging. Fabricated using an adhesive wafer bonding method, the array consists of optically transparent conductors [indium tin oxide (ITO)] as both top and bottom electrodes, a transparent polymer [bisbenzocyclobutene (BCB)] as the sidewall and adhesive material, and largely transparent silicon nitride as the membrane. The fabricated device had a maximum optical transparency of 76.8% in the visible range. Furthermore, to simultaneously maintain higher spatial resolution and deeper imaging depth, this dual-frequency array consists of low- and high-frequency channels with 4.2- and 9.3-MHz center frequencies, respectively, which are configured in an interlaced architecture to minimize the grating lobes in the receive point spread function (PSF). With a wider bandwidth compared to the single-frequency case, the fabricated transparent dual-frequency CMUT array was used in through-illumination photoacoustic imaging of wire targets demonstrating an improved spatial resolution and imaging depth.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".