Nanoparticle fabrication by geometrically confined nanosphere lithography
Bibliographic record
Abstract
Arrays of metal nanoparticles, typically gold or silver, exhibit localized surface plasmon resonance, a phenomenon that has many applications, such as chemical and biological sensing. However, fabrication of metal nanoparticle arrays with high uniformity and repeatability, at a reasonable cost, is difficult. Nanosphere lithography (NSL) has been used before to produce inexpensive nanoparticle arrays through the use of monolayers of self-assembled microspheres as a deposition mask. However, control over the size and location of the arrays, as well as uniformity over large areas is poor, thus limiting its use to research purposes. In this paper, a new NSL method, called here geometrically confined NSL (GCNSL), is presented. In GCNSL, microsphere assembly is confined to geometric patterns defined in photoresist, allowing high-precision and large-scale nanoparticle patterning while still remaining low cost. Using this new method, it is demonstrated that 400 nm polystyrene microspheres can be assembled inside of large arrays of photoresist patterns. Results show that optimal microsphere assembly is achieved with long and narrow rectangular photoresist patterns. The combination of microsphere monolayers and photoresist patterns is then used as a deposition mask to produce silver nanoparticles at precise locations on the substrate with high uniformity, repeatability, and quality.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".