High temperature characterization of piezoelectric lithium niobate using electrochemical impedance spectroscopy resonance method
Bibliographic record
Abstract
Piezoelectric materials reversibly deform when exposed to an electric field. This property is indispensable to modern engineering devices, enabling a wide range of sensors and actuators. However, unfortunately conventional piezoelectric materials are limited to operating temperatures of below approximately 200 °C. Lithium niobate is a promising candidate for high temperature applications (above 500 °C), as it has a high Curie temperature (1200 °C) and good piezoelectric properties. Nevertheless, degradation mechanisms occurring at elevated temperatures are not fully understood, although they are known to interfere with the piezoelectric behavior. In addition, the material properties of this technologically promising ceramic have not been adequately characterized at high temperatures, particularly when excited at high frequencies, due to the difficulty of performing such measurements. We therefore employ an electrochemical impedance spectroscopy resonance method using a novel analytical model to determine the material properties of single crystal lithium niobate over the wide frequency range of 100 kHz to 7 MHz for temperatures up to 750 °C. We find that lithium niobate retains its good piezoelectric properties over this entire frequency and temperature range and rules out suspected degradation mechanisms involving ionic conductivity or vacancy diffusion.
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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 | 0.001 | 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".