Aberration-corrected STEM and EELS of semiconducting nanostructures
Bibliographic record
Abstract
We review some applications of aberration–corrected electron microscopy for the detailed characterization of semiconducting nanostructures using a combination of high-angle annular dark-field scanning transmission electron microscopy and electron energy loss spectroscopy. The study of self-assembled quantum wires shows that it is possible to determine the composition of the nanostructures with better than 1 nm resolution down to the atomic level while the contrast in the high-angle annular dark-field images is used to determine the presence of wetting layers separating quantum wires and the strain field arising from the local compositional changes. The local measurements of energy loss spectra demonstrate the shift of plasmon peaks consistent with the changes in lattice parameters. High-angle annular dark-field images are also used to study the contrast in GaSb thin films deposited and study the presence of anti-phase domain boundaries. These examples show that aberration-corrected microscopy combined with electron energy loss spectroscopy provide not only enhanced resolution but also increased sensitivity to atomic site compositional changes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".