Optical Properties and Applications of Plasmonic‐Metal Nanoparticles
Bibliographic record
Abstract
Abstract Noble metal nanoparticles due to their unique optical properties arising from their interactions with an incident light have been intensively employed in a broad range of applications. This review comprehensively describes fundamentals behind plasmonics, used to develop applications in the fields of biomedical, energy, and information technologies. Basic concepts (electromagnetic interaction and permittivity of metals) are discussed through Mie theory presented as the main model for interpreting phenomena of optical absorption and scattering. The effects of near‐field enhancement, shape, composition, and surrounding medium of nanoparticles on optical properties are described in detail. The review explores and identifies the potential of plasmonic nanoparticles based on their optical properties (e.g., light absorption, scattering, and field enhancement) for developing different applications (biomedical, energy and information technologies). Due to a significant impact of plasmonic nanoparticles on medicine and healthcare products and technologies, the review initially focuses on biomedical applications extensively benefited from optical features of these nanoparticles. Advantages of the optical properties outstandingly implemented are also briefly discussed in other applications, including energy and information technologies. This review concisely summarizes the explored areas based on plasmonic properties, compares advantages of plasmonic nanoparticles over other types of nanomaterials and highlights challenges.
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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".