Tailoring Plasmonic Fields with Shape-Controlled Single-Crystal Gold Metasurfaces
Bibliographic record
Abstract
Geometry and crystallinity play a critical role in the wavelength-dependent optical responses and plasmonic local near-field distributions of metallic nanostructures. Nevertheless, the ability to tailor the shape and position of crystalline metal surface nanostructures has remained a challenge that limits control of their enhanced local fields and represents a barrier to harnessing their individual and collective responses. Here, we describe a solution deposition method in the presence of anionic additives, which yields shape-controlled, single-crystal plasmonic gold nanostructures on Ag(100) and Au(100) substrates. Use of SO 4 2– ions yields smooth Au(111)-faceted square pyramids with large plasmonic Raman enhancements. Halide additives produce textured hillocks comprising edge- and screw-type dislocations (Cl – ), or platelets with large-area Au(100) terraces and (110) step edges (Br – ), while SO 4 2– and Br – additive combinations provide Au(110)-faceted square pyramids. With lithographic patterning, this chemistry yields metal deposition with precise geometry and location control to provide single-crystal, plasmonic gold metasurfaces with tailored optical response. The appropriately designed metasurfaces can then generate large Raman scattering enhancements, far greater than high density gold square pyramids with random surface disposition. Shape-controlled single-crystal plasmonic metasurfaces will thus offer opportunities to tune the characteristics of nanostructures, providing enhanced optical, photocatalytic, and sensory response.
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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 | 0.001 |
| 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".