Shape-Controlled, Single-Crystal Gold Surface Nanostructures
Bibliographic record
Abstract
Size, shape and crystallinity play a critical role in the wavelength-dependent optical responses and plasmonic local near-field distributions of metallic nanostructures. While their enhanced local fields can drive new and useful chemical and physical processes, the ability to fabricate shape-controlled single-crystal metal nanostructures and position them precisely on substrates for device applications represents a significant barrier to harnessing their greater potential. Here, we describe a novel electroless deposition process in the presence of anionic additives that yields additive-specific, shape-controlled, single-crystal plasmonic Au nanostructures on Ag(100) and Au(100) substrates. Deposition of Au in the presence of SO42- ions results in the formation of smooth Au(111)-faceted square pyramids that show large surface enhanced Raman responses. The use of halide additives such as Cl- and Br- that interact strongly with (100) facets produces highly textured hillock-type structures characterized by edge and screw-type dislocations (Cl-), or flat platelet-like features characterized by large area Au(100) terraces with (110) step edges (Br-). Use of additive combinations provides structures that comprise characteristics derived from each additive including new square pyramidal structures with dominant Au(110) facets (SO42-and Br-). Finally we demonstrate that this bottom-up electroless deposition process, when combined with top-down lithographic patterning methods, can be used to position shape-controlled, single-crystal Au nanostructures with precise location and orientation on surfaces. We anticipate that this approach will be employed as a powerful new tool to tune the plasmonic characteristics of nanostructures and facilitate their broader integration into device applications.
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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".