Plasmonic Chirality and Circular Dichroism in Bioassembled and Nonbiological Systems: Theoretical Background and Recent Progress
Bibliographic record
Abstract
Nature is chiral, thus chirality is a key concept required to understand a multitude of systems in physics, chemistry, and biology. The field of optics offers valuable tools to probe the chirality of nanosystems, including the measurement of circular dichroism, the differential interaction strength between matter and circularly polarized light with opposite helicity. Simultaneously, the use of plasmonic systems with giant light-interaction cross-sections opens new paths to investigate and manipulate systems on the nanoscale. Consequently, the interest in chiral plasmonic and hybrid systems has continually grown in recent years, due to their potential applications in biosensing, polarization-encoded optical communication, polarization-selective chemical reactions, and materials with polarization-dependent light-matter interaction. Experimentally, chiral properties of nanostructures can be either created artificially using modern fabrication techniques involving inorganic materials, or borrowed from nature using bioassembly or biomolecular templating. Herein, the recent progress in the field of plasmonic chirality is summarized, with a focus on both the theoretical background and the experimental advances in the study of chirality in various systems, including molecular-plasmonic assemblies, chiral plasmonic nanostructures, chiral assemblies of interacting plasmonic nanoparticles, and chiral metal metasurfaces and metamaterials. The growth prospects of this field are also discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".