Bibliographic record
Abstract
Our 21st century faces to a “sustainable society”, which enhances (a) usage of non-toxic materials, (b) disposal technology for existing hazardous materials, (c) reduction of contamination gas, (d) environmental monitoring system, (e) new energy source creation, and (f) energy-efficient device development in the piezoelectric area. With reducing their size, the electromagnetic components reduce their efficiency drastically. Thus, piezoelectric transducers with much less losses are highly sought recently. Piezoelectric devices seem to be all-around contributors and a key component to the above mentioned five R&D areas. Some of the efforts include: (a) Since the most popular piezoelectric lead zirconate titante ceramics will be regulated in European and Asian societies due to their toxicity (Pb2+ ion), lead-free piezoelectrics have been developed. (b) Since hazardous organic substances can easily be dissolved by the ultrasonic irradiation in water, a new safe disposal technology using piezoelectric transducers has been developed. (c) We demonstrated an energy recovery system on a hybrid car from its engine’s mechanical vibration to the rechargeable battery. (d) Micro ultrasonic motors based on piezoelectrics demonstrated 1/20 reduction in the volume and a 20-time increase in efficiency of the conventional electromagnetic motors. This paper introduces leading piezoelectric materials, devices, and drive/control methods, relating with the above “sustainability” technologies, aiming at further research expansion in this area.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".