High performance, single crystal gold bowtie nanoantennas fabricated via epitaxial electroless deposition
Bibliographic record
Abstract
Material quality plays a critical role in the performance of nanometer-scale plasmonic structures and represents a significant hurdle to large-scale device integration. Progress has been hindered by the challenges of realizing scalable, high quality, ultrasmooth metal deposition strategies, and by the poor pattern transfer and device fabrication yields characteristic of most metal deposition approaches which yield polycrystalline metal structure. Here we highlight a novel and scalable electrochemical method to deposit ultrasmooth, single-crystal (100) gold and to fabricate a series of bowtie nanoantennas through subtractive nanopatterning. We investigate some of the less well-explored design and performance characteristics of these single-crystal nanoantennas in relation to their polycrystalline counterparts, including pattern transfer and device yield, polarization response, gap-field magnitude, and the ability to model accurately the antenna local field response. Our results underscore the performance advantages of single-crystal nanoscale plasmonic materials and provide insight into their use for large-scale manufacturing of plasmon-based devices. 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 | 0.001 | 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".