Structural and Optical Properties of Self-Assembled Chains of Plasmonic Nanocubes
Bibliographic record
Abstract
Solution-based linear self-assembly of metal nanoparticles offers a powerful strategy for creating plasmonic polymers, which, so far, have been formed from spherical nanoparticles and cylindrical nanorods. Here we report linear solution-based self-assembly of metal nanocubes (NCs), examine the structural characteristics of the NC chains, and demonstrate their advanced optical characteristics. In comparison with chains of nanospheres with similar dimensions, composition, and surface chemistry, predominant face-to-face assembly of large NCs coated with short polymer ligands led to a larger volume of hot spots in the chains, a nearly uniform E-field enhancement in the gaps between colinear NCs, and a new coupling mode for NC chains due to the formation of a Fabry-Perot resonator structure formed by face-to-face bonded NCs. The NC chains exhibited stronger surface-enhanced Raman scattering in comparison with linear assemblies of nanospheres. The experimental results were in agreement with finite difference time domain 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) | 0.001 | 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".