Epitaxially aligned submillimeter-scale silver nanoplates grown by simple vapor transport
Bibliographic record
Abstract
Epitaxially aligned large silver (Ag) nanoplate arrays with ultraclean surfaces are very attractive for novel plasmonic applications. Although solution-phase methods have been extensively employed to synthesize Ag nanoplates, these cannot be used to grow epitaxial large Ag nanoplates on substrates. Here we report a vapor-phase synthetic strategy to epitaxially grow submillimeter-scale Ag nanoplates on a variety of substrates. By simply transporting Ag vapor to the substrates at an optimal temperature (820 °C), we synthesize ∼100 μm-sized Ag nanoplates with atomically clean surfaces, which are three-dimensionally aligned on the substrates. We demonstrate that both the type of supported seed and their interfacial lattice matching with the substrates determine the epitaxial growth habit of the nanoplates, directing their crystallinity, shape, and orientation. (i) On r-cut sapphire substrates, twinned pentagonal nanoplates grow vertically from twinned triangular seeds through a seed → nanoplate process. (ii) On m-cut sapphire substrates, twinned trapezoidal Ag nanoplates grow slantingly from twinned decahedral seeds through a seed → NW → nanoplate process. (iii) Interestingly, twin-free single-crystalline trapezoidal Ag nanoplates grow from twin-free square pyramidal seeds on STO (001) substrates through a seed → NW → nanoplate process. The epitaxially aligned Ag nanoplate arrays could serve as a new platform for two-dimensional (2D) guiding of surface plasmons as well as for hierarchical 3D plasmonic nanoarchitecturing.
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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 | 0.000 |
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".