Catalyst-Assisted Pulsed Laser Deposition of One-Dimensional Single-Crystalline Nanostructures of Tin(IV) Oxide: Interplay of VS and VLS Growth Mechanisms at Low Temperature
Bibliographic record
Abstract
Single-crystalline nanostructures of tin(IV) oxide (TO) are grown with the aid of size-controllable gold nanoisland (GNI) catalysts supported on a Si substrate by using the pulsed laser deposition (PLD) method. The use of gold nanocatalysts with average size between 15 and 50 nm enables the deposition driven by the vapor–liquid–solid growth mechanism to occur at a relatively low temperature (500–700 °C). By controlling the gas atmosphere (O 2 or Ar) and the GNI support (oxidized or H-terminated Si), we are able to produce a variety of TO nanostructures, including one-dimensional nanowires, nanobelts, and nanobricks, as well as cuboid nanoparticles by the PLD method. Scanning electron microscopy and helium ion microscopy show faceted morphology of these nanostructures and reveal the underlying vapor–liquid–solid and vapor–solid growth mechanisms dominant for nanowires and nanobelts and for nanobricks and nanoparticles, respectively. The nanowires exhibit a square cross section (with side length varying from 70 to 90 nm at the base to 10–50 nm near the tip), while the nanobelts have a rectangular cross section (with a width-to-thickness ratio of 2–9) with a remarkably small thickness (5–30 nm). These unique micrometer-long TO nanostructures have not only the largest surface-to-volume ratio but also low-index surfaces, which can be modified with different deposition parameters. X-ray diffraction study further shows the expected tetragonal crystalline phase of these TO nanostructures but each with its own preferred growth orientation(s): (101) for the nanoparticles and nanobricks, (200) for the nanowires, and (200) and (101) for nanobelts. For nanowires and nanobelts, transmission electron microscopy confirms the single-crystalline nature of these one-dimensional nanostructures, with their different growth orientations that lead to the preferred growth directions as collaborated by the corresponding X-ray diffraction data. These results demonstrate the versatility of the catalyst-assisted PLD method in fabricating novel one-dimensional TO nanostructures, with good control on not only their shapes and cross-sectional dimensions and their aerial densities but also their growth orientations and side surface planes. The catalyst-assisted PLD method can easily accommodate doping and other fabrication steps to incorporate desirable semiconducting, gas-sensing, optoelectronic, and magnetic properties in these one-dimensional TO nanostructures for emerging applications.
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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".