Fabrication of large-area metal nanoparticle arrays by nanosphere lithography for localized surface plasmon resonance biosensors
Bibliographic record
Abstract
The localized surface plasmon resonance (LSPR) phenomenon that is characteristic of gold and silver nanoparticles has applications in areas such as portable and remote 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 to produce inexpensive nanoparticle arrays, through the use of monolayers of self-assembled microspheres as a deposition mask. However, lack of control over the location and size of the arrays, as well as poor uniformity over large areas, limits its use to research purposes. Here, we present large-area fabrication of nanoparticle arrays through both convective self-assembly NSL (CSANSL) and our new method, geometrically confined NSL (GCNSL). In GCNSL, microsphere assembly is confined to geometric patterns defined in photoresist. We show that 400nm polystyrene microspheres can be assembled inside of large arrays of photoresist trenches from 4-20μm in width and 500μm in length, with high uniformity, repeatability, and quality. Compared to CSANSL, GCNSL allows precise patterning of nanoparticle arrays for use in practical LSPR sensing devices, while still remaining inexpensive.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".