Tuning the Liquid–Vapour Interface of VLS Epitaxy for Creating Novel Semiconductor Nanostructures
Bibliographic record
Abstract
Controlling the morphology and composition of semiconductor nano- and micro-structures is crucial for fundamental studies and applications. Here, Si-Ge semiconductor nanostructures were fabricated using photolithographically defined micro-crucibles on Si substrates. Interestingly, the nanostructure morphology and composition of these structures are strongly dependent on the size of the liquid–vapour interface (i.e., the opening of the micro-crucible) in the CVD deposition step of Ge. In particular, Ge crystallites nucleate in micro-crucibles with larger opening sizes (3.74–4.73 μm2), while no such crystallites are found in micro-crucibles with smaller openings of 1.15 μm2. This interface area tuning also results in the formation of unique semiconductor nanostructures: lateral nano-trees (for smaller openings) and nano-rods (for larger openings). Further TEM imaging reveals that these nanostructures have an epitaxial relationship with the underlying Si substrate. This geometrical dependence on the micro-scale vapour–liquid–solid (VLS) nucleation and growth is explained within a dedicated model, where the incubation time for the VLS Ge nucleation is inversely proportional to the opening size. The geometric effect on the VLS nucleation can be used for the fine tuning of the morphology and composition of different lateral nano- and micro-structures by simply changing the area of the liquid–vapour interface.
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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".