Recycling gold nanohole arrays
Bibliographic record
Abstract
The authors report the impact of common cleaning methods on the stability of gold nanohole arrays used as extraordinary optical transmission surface plasmon resonance sensors. Their optical sensitivity, physical structure, and surface contamination levels were measured before and after multiple cycles of monolayer adsorption and removal with various wet chemicals (sulfochromic acid, piranha, or ammonium hydroxide: hydrogen peroxide) and dry oxygen plasma etchants. While these oxidative chemical and plasma etches remove organic monolayers and other contaminants, the oxidation and associated heating also damages the gold nanostructures to varying degrees. The authors observed decreases in the arrays' optical sensitivities via changes in the shapes and positions of their surface plasmon resonance peaks. The optimum recycling process was a room temperature, aqueous ammonium hydroxide: hydrogen peroxide treatment (15 min) commonly referred to as Radio Corporation of America Clean 1, followed by immersion in dilute nitric acid (0.1M, 30 min). This method was effective at removing an alkanethiol self-assembled monolayer of 11-mercaptoundecanoic acid; after six recycles, no loss in optical sensitivity was detected with minimal changes in the gold film thickness (−10%), hole area (−10%), and hole circularity (+6%).
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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 | 0.001 |
| 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 | 0.001 |
| 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".