Flexible PolyCMUTs: Fabrication and Characterization of a Flexible Polymer‐Based Capacitive Micromachined Ultrasonic Array for Conformal Ultrasonography
Bibliographic record
Abstract
Abstract Conventional ultrasound transducers are short and rigid, which limits their applications, especially in the area of musculoskeletal imaging where many of the structures to be imaged lie within long curved anatomical structures, such as limbs. In such cases, conformal ultrasound imaging can be advantageous. This paper presents a process for fabricating 1D and 2D flexible polymer‐based capacitive micromachined ultrasound transducers (flexible CMUTs). As a proof of concept, all elements of a 32‐element linear flexible array are shown to be functional (100% yield) and uniform in fundamental resonant frequency (SD = 1.8%). One‐way and two‐way acoustic responses during immersion tests in flat, convex, and concave array configurations (radii of curvature = 3 cm) show an average fractional bandwidth of 83% and 75%, respectively, across these bending conditions. The flexible array shows no signal drop after over 100 bending cycles and only 6% variation in pulse amplitude after over 14 h of continuous operation. Finally, the resulting transducers are shown to operate at up to 15 MHz. The findings demonstrate robust operation of flexible CMUT arrays and justify further development targeted at key imaging applications, particularly in the area of diagnosing musculoskeletal conditions.
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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 | 0.001 |
| 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) | 0.001 | 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".