Kinetically Favorable Vapor–Adsorbate–Solid Growth of Rutile Nanowires
Bibliographic record
Abstract
Abstract The vapor‐based synthesis of nanowires, particularly vapor–liquid–solid growth and its variants, is an undoubtedly promising method for fabricating high‐quality silicon‐based and group III–V semiconductors. However, the assembly of oxide nanowires has limited success while adopting these methods. Herein, a simple and scalable approach is developed to synthesize single‐crystal oxide nanowires with controllable morphology. Using titanium oxide as the model system for validation, this approach emphasizes the essential role of the surface characteristic of the seed for the space‐confined growth of nanowires. The shape and growth directions of nanowires can be additionally tailored by using bimetallic seeds, with compositional segregations and dissimilar surface characteristics. Since the source material has the same thermodynamic conditions as the produced nanowires within a closed vessel, the results suggest that the growth of nanowires can be dominated by the kinetic enrichment at seed surfaces or referred to as kinetically favorable vapor–adsorbate–solid growth. While identifying the key thermodynamic and kinetic parameters during growth, this approach is applicable for a wide range of materials.
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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 | 0.001 |
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".