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Nanophotonic Structural Colors
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Abstract Structural colors traditionally refer to colors arising from the interaction of light with structures with periodicities on the order of the wavelength. Recently, the definition has been broadened to include colors arising from individual resonators that can be subwavelength in dimension, for example, plasmonic and dielectric nanoantennas. For instance, diverse metallic and dielectric nanostructure designs have been utilized to generate structural colors based on various physical phenomena, such as localized surface plasmon resonances (LSPRs), Mie resonances, thin-film Fabry–Pérot interference, and Rayleigh–Wood diffraction anomalies from 2D periodic lattices and photonic crystals. Here, we provide our perspective of the key application areas where structural colors really shine and other areas where more work is needed. We review major classes of materials and structures employed to generate structural coloration and highlight the main physical resonances involved. We discuss mechanisms to tune structural colors and review recent advances in dynamic structural colors. In the end, we propose the concept of a universal pixel that could be crucial in realizing next-generation displays based on nanophotonic structural colors.
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Metadata
- Authors
- Daqiqeh Rezaei, S., Dong, Z., You En Chan, J., Trisno, J., Ng, R. J. H., Ruan, Q., Qiu, C.-W., Mortensen, N. A., & Yang, J. K. W.
- Venue
- ACS Photonics
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-07-28
- Volume
- 8
- Issue
- 1
- Pages
- 18-33
- ISSN
- 2330-4022 (ISSN-L)
- OpenAlex
- W3045998919
- Cited by
- 348
- References
- 225
- FWCI
- 19.06
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
