Bibliographic record
Abstract
The highly-localized and intense electromagnetic hotspots afforded by plasmonic resonances in nano-scaled metallic objects have led to many exciting biomedical applications. The equivalence between nanoplasmonic hotspots, and those due to morphology-dependent resonances in high-index dielectrics is a promising avenue of nanophotonic research. In the microwave frequency regime water is such a material (n~9), and thus cm-sized aqueous dielectric objects can become resonant to few-GHz light from microwaves, WiFi, and other communication-band sources. We are using experimental, analytical, and computational approaches for studying hotspots in aqueous dimers. Experimentally, we use a household microwave oven, grape-sized hydrogel beads, and thermal imaging to demonstrate a transition from dipole-like resonance in isolated spheres to intense hotspots at the nexus of dimers. We computationally identify a host of fundamental resonances in spherical monomers that hybridize to yield either/both internal and point-of-contact dimer modes. We demonstrate that an intuitive vector-field addition approach intuitively identifies which resonances are most likely to combine to form an axial hotspot in the dimer. The usefulness of this approach is confirmed with 3D FEM simulations.
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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) | 0.002 | 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".