Extending nanoscale patterning with multipolar surface plasmon resonances
Bibliographic record
Abstract
Plasmonic excitation of metallic nanoparticles can trigger chemical reactions at the nanoscale. Such optical effects can also be employed to selectively and locally graft photopolymer layers at the nanostructure surface, and, when combined with a surface functionalization agent, new pathways can be explored to modify the surface of a plasmonic nanoparticle. Among these approaches, diazonium salt chemistry is seen as an attractive strategy due to the high photoinduced reactivity of these salts. In this work, we demonstrate that it is possible to trigger the site-selective grafting of aryl films derived from diazonium salts on distinct nano-localized area of single gold nanotriangles, by taking advantage of their multipolar localized surface plasmon modes. It is shown the aryl film will preferentially graft in areas where the electric field enhancement is maximum, independently of the considered excited surface plasmon mode. These experimental findings are in very good qualitative agreement with the calculations of the local electric field, using the finite-difference time-domain (FDTD) method. We believe that this plasmonic-based approach will not only pave a new way for the spatially controlled surface functionalization of plasmonic nanoparticles, but also provide a general strategy to attach distinct molecules to hot spot regions on a single nanoparticle, opening promising prospects in sensing and multiplexing, and optically nano-scale patterning of functional groups.
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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) | 0.002 | 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".