Nanorattles with tailored electric field enhancement
Bibliographic record
Abstract
Nanorattles are metallic core-shell particles with core and shell separated by a dielectric spacer. These nanorattles have been identified as a promising class of nanoparticles, due to their extraordinary high electric-field enhancement inside the cavity. Limiting factors are reproducibility and loss of axial symmetry owing to the movable metal core; movement of the core results in fluctuation of the nanocavity dimensions and commensurate variations in enhancement factor. We present a novel synthetic approach for the robust fixation of the central gold rod within a well-defined box, which results in an axisymmetric nanorattle. We determine the structure of the resulting axisymmetric nanorattles by advanced transmission electron microscopy (TEM) and small-angle X-ray scattering (SAXS). Optical absorption and scattering cross-sections obtained from UV-vis-NIR spectroscopy quantitatively agree with finite-difference time-domain (FDTD) simulations based on the structural model derived from SAXS. The predictions of high and homogenous field enhancement are evidenced by scanning TEM electron energy loss spectroscopy (STEM-EELS) measurement on single-particle level. Thus, comprehensive understanding of structural and optical properties is achieved for this class of nanoparticles, paving the way for photonic applications where a defined and robust unit cell is crucial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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".