Advancements in fractal plasmonics: structures, optical properties, and applications
Bibliographic record
Abstract
The structural characteristics of plasmonic nanostructures directly influence their plasmonic properties, and therefore, their potential role in applications ranging from sensing and catalysis to light- and energy-harvesting. For a structure to be compatible with a selected application, it is critical to accurately tune the plasmonic properties over a specific spectral range. Fabricating structures that meet these stringent requirements remains a significant challenge as plasmon resonances are generally narrow with respect to the considered selected spectral range. Adapted from their well-established role in GHz applications, plasmonic fractal structures have emerged as architectures of interest due to their ability to support multiple tunable resonances over broad spectral domains. Here, we review the advancements that have been made in the growing field of fractal plasmonics. Iterative and space-filling geometries that can be prepared by advanced nanofabrication techniques, notably electron-beam lithography, are presented along with the optical properties of such structures and metasurfaces. The distributions of electromagnetic enhancement for some of these fractals is shown, along with how the resonances can be mapped experimentally. This review also explores how fractal structures can be used for applications in solar cell and plasmon-based sensing applications. Finally, the future areas of physical and analytical science that could benefit from fractal plasmonics are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".