High-Performance, Single-Crystal Gold Bowtie Nano-Antennas via Epitaxial Electroless Deposition
Bibliographic record
Abstract
Material quality can play a critical role in the performance of nanometer-scale plasmonic structures. Here, we highlight a novel deposition strategy for single-crystal noble metal deposition and provide a direct and quantitative comparison between the fabrication yield, durability, and efficiency of bowtie nano-antennas fabricated from monocrystalline and polycrystalline gold films using subtractive nanofabrication. Focused ion beam milling of monocrystalline Au(100) films deposited through epitaxial electroless deposition to form bowtie nano-antennas produces devices that demonstrate key performance enhancements over devices patterned identically from polycrystalline Au films deposited via physical vapor deposition. Single-crystal bowties reveal significant improvements in pattern transfer fidelity and device yield, the ability to tailor and model local plasmonic field enhancements and marked improvement in their thermal and mechanical stability over those fabricated from polycrystalline Au films. This work underscores the performance advantages of single-crystal nanoscale plasmonic materials and describes a straightforward, solution-phase deposition pathway to achieve them. We anticipate that this approach will be broadly useful in applications where local near-fields can enhance light−matter interactions, including for the fabrication of optical sensors, photocatalytic structures, hot carrier-based devices, and nanostructured noble metal architectures targeting nano-attophysics.
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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 | 0.001 |
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".