Measurements of ion‐induced ferroelectric emission and surface charge dynamics on LiTaO <sub>3</sub> (0001) by time‐of‐flight scattering and recoiling spectrometry (TOF‐SARS)
Bibliographic record
Abstract
Abstract We have derived a special surface analysis method for studying surface charge dynamics on ferroelectrics from time‐of‐flight ion scattering and recoiling spectrometry (TOF‐SARS). In this method, shifts of the TOF‐SARS peaks of a ferroelectric crystal are measured as a function of sample temperature, and these spectral data are used to deduce changes in electrical potential on the sample surface. These changes are then converted to information on surface charge dynamics. In addition, the method is also applicable in extracting data regarding ion‐induced ferroelectric electron emission (FEE) from the TOF‐SARS spectra. In this work, we have tested the method with LiTaO 3 (0001) crystals having the nominal stoichiometry and single domain properties. Our results show that for temperature changes from 25 to ∼100 °C, the excess amount of surface charge induced by pyroelectricity, ion irradiation, and ion‐induced electron emission is drained mainly by surface conductivity. For temperature changes above 100 °C, the bulk ionic conductivity becomes an important charge drainage channel. Our measurements give an activation energy of 0.75 eV for the thermally activated ionic conductivity, and the result agrees well with the literature value previously obtained by d.c. conductivity measurements. Copyright © 2006 John Wiley & Sons, Ltd.
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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".