Position-controlled hydrothermal growth of ZnO nanorods on arbitrary substrates with a patterned seed layer via ultraviolet-assisted nanoimprint lithography
Bibliographic record
Abstract
A novel technique for the position-controlled growth of ZnO nanorods is established, by combining ultraviolet-assisted nanoimprint lithography (UV-NIL) and hydrothermal growth. Various ZnO nanorod arrays were obtained on silicon substrates, by UV-NIL of ZnO seed patterns with lines of 200 nm wide at a pitch of 1000 nm from a photosensitive ZnO precursor, followed by a position-controlled hydrothermal growth step with varied growth times. It was found that the aspect ratio of ZnO nanorods increased from 2.7 to 11.8 as the growth time was increased from 2 to 6 h. Selected area electron diffraction (SAED) analysis indicates that the root of studied ZnO nanorods consists of both amorphous and polycrystalline phases whereas the stem shows a single-crystalline nature with a preferred (002) growth. ZnO nanorod arrays were also routinely obtained on transparent glass and flexible polyethylene terephthalate (PET). In all cases, ZnO nanorods were observed on both the sidewalls and top surfaces of the ZnO seed patterns with a nanoflower-like structure regardless of substrate substances. This technique offers an alternative method for integrating ZnO nanorods at low temperatures and free of high vacuum, potentially useful in applications such as nanophotonics, photovoltaics and flexible nanoelectronics.
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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) | 0.000 | 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".