Hexoctahedron in a Nanoframe: Tailoring Stationary and Open Nanogaps for Single-Particle Surface-Enhanced Raman Scattering
Bibliographic record
Abstract
Plasmonic nanostructures with sharp features and nanogaps have been of immense interest in surface-enhanced Raman scattering (SERS) applications due to their strong hot spots generated at such morphological features. Here, we report a synthetic route to produce Au hexoctahedron-in-a-nanoframe structures (Au HIAFs), in which the high-curvature vertices of the Au hexoctahedral nanocrystal core fit tightly into the frame corners without the aid of an anchoring linker, realizing stationary, open nanogaps at sharp features within a single nanostructure. The prepared Au HIAFs can effectively exploit both sharp feature- and gap-enhanced electromagnetic fields, thereby exhibiting prominent SERS performance at the single-particle level. Due to the finely controlled, linker-free nanogaps of the Au HIAFs, the SERS detection of a series of analyte molecules could also be possible with an identical single Au HIAF through the reversible adsorption and desorption of analytes, highlighting their potential as a practical, reusable substrate for SERS-related 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) | 0.000 | 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".