Blocking germanium diffusion inside silicon dioxide using a co-implanted silicon barrier
Bibliographic record
Abstract
We investigate the effect of co-implanting a silicon sublayer on the thermal diffusion of germanium ions implanted into SiO2 and the growth of Ge nanocrystals (Ge-ncs). High-resolution imaging obtained by transmission electron microscopy and energy dispersive spectroscopy measurements supported by Monte-Carlo calculations shows that the Si-enriched region acts as a diffusion barrier for Ge atoms. This barrier prevents Ge outgassing during thermal annealing at 1100 °C. Both the localization and the reduced size of Ge-ncs formed within the sample region co-implanted with Si are observed, as well as the nucleation of mixed Ge/Si nanocrystals containing structural point defects and stacking faults. Although it was found that the Si co-implantation affects the crystallinity of the formed Ge-ncs, this technique can be implemented to produce size-selective and depth-ordered nanostructured systems by controlling the spatial distribution of diffusing Ge. We illustrate this feature for Ge-ncs embedded within a single SiO2 monolayer, whose diameters were gradually increased from 1 nm to 5 nm over a depth of 100 nm.
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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".