Characterization of Pure Face-Shear Strain in Piezoelectric α-Tellurium Dioxide (α-TeO2)
Bibliographic record
Abstract
Paratellurite, also known as α-tellurium dioxide, is a ceramic that is primarily employed for its interesting optical properties. However, this material’s crystal structure belongs to the 422 symmetry class that allows a unique piezoelectric behavior to manifest itself: deformation in pure face-shear. This means that crystal symmetry necessitates the piezoelectric tensor to have only a single non-zero coefficient, d123 = d14: such unique behavior has the potential to enable novel gyroscopic sensors and high-precision torsional microelectromechanical systems (MEMS) actuators, as pure face-shear can be used to induce pure torsion. Although α-TeO2 is one of the few known materials belonging to this symmetry class, considerable uncertainty in its single piezoelectric coefficient exists, with the few reported literature values ranging from 6.13 to 14.58 pC/N; this large uncertainty results from the difficulty in using conventional piezoelectric characterization techniques on paratellurite, limiting measurements to indirect methods. The novel applications that would be enabled by the adoption of this extraordinary material are frustrated by this lack of confidence in the literature. We therefore leverage, for the first time, a first-principles analytical physical model with electrochemical impedance spectroscopy (EIS) to determine, directly, the lone piezoelectric coefficient d123 = d14 = 7.92 pC/N.
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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".