Indium tin oxide nanowhisker morphology control by vapour–liquid–solid glancing angle deposition
Bibliographic record
Abstract
A new growth technique for indium tin oxide nanowhiskers with increased control over feature size and spacing is reported. The technique is based on a unique combination of self-catalysed vapour-liquid-solid (VLS) growth and glancing angle deposition (GLAD). This VLS-GLAD technique provides enhanced control over nanowhisker morphology as the effect of typical VLS growth parameters (e.g. flux rate, temperature) is amplified at large deposition angles characteristic of GLAD. Spatial modulation of the collimated growth flux controls trunk width, number and orientation of branches, and overall nanowhisker density. Here we report the influence of growth conditions (including deposition angle, flux rate, nominal pitch and substrate temperature) on nanowhisker morphology, with specific focus on the effect of large deposition angles. Sheet resistance and transmission of the films were measured to characterize their performance as transparent conductive oxides. Hybrid nanostructured films grown in this study include high surface area nanowhiskers protruding from a conductive film, ideal for transparent conductive electrode applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 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".