High-temperature electrical conductivity in piezoelectric lithium niobate
Bibliographic record
Abstract
Lithium niobate is a promising candidate for use in high-temperature piezoelectric devices due to its high Curie temperature (≈1483 K) and strong piezoelectric properties. However, the piezoelectric behavior has, in practice, been found to degrade at various temperatures as low as 573 K, with no satisfactory explanation available in the literature. We, therefore, studied the electrical conductivity of congruent lithium niobate single crystals in the temperature range of 293–1273 K with an 500 mV excitation at frequencies between 20 Hz and 20 MHz. An analytical model that generalizes the universal dielectric relaxation law with the Arrhenius equation was found to describe the experimental temperature and frequency dependence and helped discriminate between conduction mechanisms. Electronic conduction was found to dominate at low temperatures, leading to low overall electrical conductivity. However, at high temperatures, the overall electrical conductivity increases significantly due to ionic conduction, primarily with lithium ions (Li+) as charge carriers. This increase in electrical conductivity can, therefore, cause an internal short in the lithium niobate crystal, thereby reducing observable piezoelectricity. Interestingly, the temperature above which ionic conductivity dominates depends greatly on the excitation frequency: at a sufficiently high frequency, lithium niobate does not exhibit appreciable ionic conductivity at high temperature, helping explain the conflicting observations reported in the literature. These findings enable an appropriate implementation of lithium niobate to realize previously elusive high-temperature piezoelectric 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 | 0.000 |
| 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".