Fabrication of nanostar arrays by nanoimprint lithography
Bibliographic record
Abstract
Using a low-cost and high-throughput process, this work demonstrates the fabrication of nanostar arrays over a large surface area, which would be an efficient substrate for surface enhanced Raman scattering applications. In the method, the nanostar is defined by the gap between four nanoholes “touching” each other. The two-dimensional periodic hole array was fabricated by nanoimprint lithography, and then the array pattern was transferred into a polymer layer sandwiched between two hard mask layers. Next, the holes in the polymer layer were enlarged by oxygen reactive ion etching (RIE) until its diameter was equal to the array period. The nanostar array was formed in the bottom hard layer after RIE, or it can be further transferred into a noble metal layer by lift-off steps. The authors fabricated a nanostar array with 200 nm tip-tip distance (equal to array period) and down to sub-10-nm apex and gap between adjacent stars. Numerical simulation confirmed the great enhancement of electromagnetic field near the star apexes.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".