Optical resonances of hollow nanocubes controlled with sub-particle structural morphologies
Bibliographic record
Abstract
The structural details of nanoparticles at the sub-particle level are critical for our understanding of their functionalities and the basic mechanisms involved in their formation. In particular, the geometries of such features determine the particle's overall optical response. Hollow metallic nanoparticles (hollow-MNPs) that have cubic geometries, with varying morphologies on their walls and voids in their body, offer a platform to study the effects of such structural features on the properties of single nanoparticles and their ensemble. Here, we report the control over sub-particle pinholes and voids by modifying the dynamics of the galvanic reaction, and we connect these structures to the optical response of the hollow nanocubes. We observe that symmetry breakage in individual particles, caused by pinholes and voids, has a drastic effect on the plasmon-resonance peak positions in their UV-Vis-NIR spectra. Via electron microscopy imaging, statistical analyses, and electromagnetic simulations, we observe that enlargement in a pinhole's diameter and an increase in their number produce a redshift in the resonance absorption peak of the ensemble. Our results outline nanoparticle design avenues via sub-particle morphologies for several applications, including those operating in the biological window and those carrying chemical payloads in organisms.
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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 | 0.001 |
| 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".