Investigation of terbium-doped silicon oxide thin films: comparison of TEM images prepared by FIB and mechanical methods
Bibliographic record
Abstract
Abstract This study characterizes the optical and structural properties of terbium-doped oxygen-rich silicon oxide (ORSO:Tb) thin films and investigates focused ion beam (FIB)-induced damage on transmission electron microscopy (TEM) lamellae prepared from these films. While there are significant advantages to the FIB technique, there is a potential that energetic ions used during the FIB process can damage the lamellae. A comparative analysis of TEM images obtained using FIB and conventional mechanical preparation methods was performed. The results indicate that TEM images of FIB-prepared lamellae exhibit higher resolution, allowing for a more detailed examination of nanocrystal structures and quantum dots. In contrast, the lack of sufficient clarity of the mechanically prepared TEM images reduces the number of nanocrystals visible in the field of view, resulting in a less effective and detailed study of the thinned films. We found no evidence of Ga implantation or mixing into the thinned film, and no observable FIB-induced damage such as recrystallization, or amorphization. Photoluminescence spectra exhibited red and blue shifts with increasing annealing temperature at blue and green emissions, respectively. X-ray diffraction patterns verify that the formation of crystalline nanostructures begins at 1100 °C, and at least at 1200 °C, two phases of Tb4Si3(SiO4)O10 and Tb2O3 in the sample are recognized.
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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) | 0.002 | 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".