Eco-Friendly Highly Sensitive Transducers Based on a New KNN–NTK–FM Lead-Free Piezoelectric Ceramic for High-Frequency Biomedical Ultrasonic Imaging Applications
Bibliographic record
Abstract
High-frequency ultrasonic imaging with improved spatial resolution has gained increasing attention in the field of biomedical imaging. Sensitivity of transducers plays a pivotal role in determining ultrasonic image quality. Conventional ultrasonic transducers are mostly made from lead-based piezoelectric materials that may be harmful to the human body and the environment. In this study, a new (K,Na)NbO3-KTiNbO5-BaZrO3-Fe2O3-MgO (KNN-NTK-FM) lead-free piezoelectric ceramic was utilized in developing eco-friendly transducers for high-frequency biomedical ultrasonic imaging applications. A needle transducer with a small active aperture size of 0.45 × 0.55 mm2was designed and evaluated. The fabricated transducer exhibits great performance with a high center frequency (52.6 MHz), a good electromechanical coupling (keff~ 0.45), a large bandwidth (64.4% at -6 dB), and a very low two-way insertion loss (10.1 dB). Such high sensitivity is superior to those transducers based on other lead-free piezoelectric materials and can even be comparable to the lead-based ones. Imaging performance of the KNN-NTK-FM needle transducer was analyzed by imaging a wire phantom and an agar tissue-mimicking phantom. Imaging capabilities of the transducer were further demonstrated by ex vivo imaging studies on a porcine eyeball and a rabbit aorta. The results suggest that the KNN-NTK-FM piezoceramic has many attractive properties over other lead-free piezoelectric materials in developing eco-friendly highly sensitive transducers for high-frequency biomedical ultrasonic imaging applications.
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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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".