Addressing the plasmonic hotspot region by site-specific functionalization of nanostructures
Bibliographic record
Abstract
Strong electromagnetic fields emerge around resonant plasmonic nanostructures, focusing the light in tiny volumes, usually referred to as hotspots. These hotspots are the key regions governing plasmonic applications since they strongly enhance properties, signals or energies arising from the interaction with light. For a maximum efficiency, target molecules or objects would be exclusively placed within hotspot regions. Here, we propose a reliable, universal and high-throughput method for the site-specific functionalization of hotspot regions over macroscopic areas. We demonstrate the feasibility of the approach using crescent-shaped nanostructures, which can be fabricated using colloidal lithography. These structures feature polarization-dependent resonances and near-field enhancement at their tips, which we use as target regions for the site-selective functionalization. We modify the fabrication process and introduce a defined passivation layer covering the central parts of the gold nanocrescent, which, in turn, selectively uncovers the tips and thus enables a localized functionalization with thiol molecules. We demonstrate and visualize a successful targeting of the hotspot regions by binding small gold nanoparticles and show a targeting efficiency of 90%. Finally, we demonstrate the versatility of the method exemplarily by translating the principle to different nanostructure geometries and architectures.
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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 | 0.000 |
| Research integrity | 0.001 | 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".