Viral‐based nanomaterials for plasmonic and photonic materials and devices
Bibliographic record
Abstract
Over the last decade, viruses have established themselves as a powerful tool in nanotechnology. Their proteinaceous capsids benefit from biocompatibility, chemical addressability, and a variety of sizes and geometries, while their ability to encapsulate, scaffold, and self-assemble enables their use for a wide array of purposes. Moreover, the scaling up of viral-based nanotechnologies is facilitated by high capsid production yield and speed, which is particularly advantageous when compared with slower and costlier lithographic techniques. These features enable the bottom-up fabrication of photonic and plasmonic materials, which relies on the precise arrangement of photoactive material at the nanoscale to control phenomena such as electromagnetic wave propagation and energy transfer. The interdisciplinary approach required for the fabrication of such materials combines techniques from the life sciences and device engineering, thus promoting innovative research. Materials with applications spanning the fields of sensing (biological, chemical, and physical sensors), nanomedicine (cellular imaging, drug delivery, phototherapy), energy transfer and conversion (solar cells, light harvesting, photocatalysis), metamaterials (negative refraction, artificial magnetism, near-field amplification), and nanoparticle synthesis are considered with exclusive emphasis on viral capsids and protein cages. This article is categorized under: Biology-Inspired Nanomaterials > Protein and Virus-Based Structures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.005 | 0.004 |
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".