Large‐Area Fabrication of Periodic Arrays of Nanoholes in Metal Films and Their Application in Biosensing and Plasmonic‐Enhanced Photovoltaics
Bibliographic record
Abstract
Abstract Plasmonics is a fast developing research area with a great potential for practical applications. However, the implementation of plasmonic devices requires low cost methodologies for the fabrication of organized metallic nanostructures that covers a relative large area (∼1 cm2). Here the patterning of periodic arrays of nanoholes (PANHs) in gold films by using a combination of interference lithography, metal deposition, and lift off is reported. The setup allows the fabrication of periodic nanostructures with hole diameters ranging from 110 to 1000 nm, for 450 and 1800 nm of periodicity, respectively. The large areas plasmonic substrates consist of 2 cm × 2 cm gold films homogeneously covered by nanoholes and gold films patterned with a regular microarray of 200 μm diameter circular patches of PANHs. The microarray format is used for surface plasmon resonance (SPR) imaging and its potential for applications in multiplex biosensing is demonstrated. The gold films homogeneously covered by nanoholes are useful as electrodes in a thin layer organic photovoltaic. This is first example of a large area plasmonic solar cell with organized nanostructures. The fabrication approach reported here is a good candidate for the industrial‐scale production of metallic substrates for plasmonic applications in photovoltaics and biosensing.
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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".