The spatial distribution of silicon NCs and erbium ion clusters by simultaneous high‐resolution energy filtered and Z‐contrast STEM and transmission electron tomography
Bibliographic record
Abstract
Abstract Imaging the spatial distribution of silicon nanocrystals (Si‐NCs) and erbium ion clusters can assist in the development of light‐emitting erbium‐doped silicon nanocomposites. We applied simultaneous energy‐filtered and high‐angle annular dark field imaging in the scanning transmission electron microscopy (STEM) mode; the former method images the Si NCs and the latter is sensitive to the high atomic number of the Er3+ ions. Both methods were performed simultaneously so as to avoid relative image drift. In Er‐doped Si nano‐composites, we find that the Er ions are not uniformly distributed in the matrix: instead, they form clusters that appear in projection images to decorate the boundaries of the Si‐NCs. This structure may develop during sample annealing, due to the low solubility of Er in the forming Si clusters. Although multi‐detector simultaneous imaging is a good way to avoid nanometer‐scale image drift that could otherwise conceal the nanoscale spatial relationship between Si‐NCs and Er clusters, an additional difficulty relates to the projection imaging methods. Essentially, it is hard to obtain spatial information parallel to the electron beam direction, so where there is apparent overlap we cannot ascertain whether an Er cluster (for example) lies above, below, or inside a given Si‐NC. Therefore, we also report on initial electron beam tomography investigations whose goal is ultimately to generate methods for obtaining the Er and Si cluster distribution in 3D. The results can lead to improved understanding of the sensitization mechanism and optimization for Er‐doped silicon nanocomposites; the clustered nature of the Er ions near the Si NCs is certainly undesirable and may explain the typically short Er3+ photoluminescence (PL) lifetimes (compared to Er‐doped silica) and the difficulty in reliably observing stimulated emission phenomena (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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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".