Recent Advances in Piezoelectric Materials for Electromechanical Transducer Applications
Bibliographic record
Abstract
Ferroelectricity has made a huge impact on science and technology since Joseph Valasek (then a Ph.D. student at the University of Minnesota) discovered it in 1920, a little longer than 100 years ago. Whereas Dr. Valasek’s original research was motivated by the need to develop seismic sensors, at present ferroelectric materials have been extensively studied for applications in high-energy capacitors, energy harvesting systems, night vision sensors, and electrocaloric solid cooling, and, of particular significance, the ferroelectrics are the material-of-choice for numerous electromechanical devices, including underwater acoustic transducers, medical diagnostic and therapeutic transducers, piezoelectric actuators, and ultrasonic motors, to name a few. The progress over the past 100 years has been enormous and it is ongoing: for example, the piezoelectric coefficient d of ferroelectrics has increased from a few pico-Coulomb per Newton to several thousand pico-Coulomb per Newton, which benefits all piezoelectric sensing and actuation devices.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".