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Light Management with Patterned Micro‐ and Nanostructure Arrays for Photocatalysis, Photovoltaics, and Optoelectronic and Optical Devices
Abstract
The design and fabrication of patterned micro‐ and nanostructure arrays have been demonstrated to be a powerful strategy toward efficient light management, which is of vital importance to a variety of photon‐related applications such as photocatalysis, photovoltaics, optoelectronic devices, and optical devices. Tunable optical reflectance, scattering, transmittance, and absorption can be readily achieved by adjusting the characteristics of the primary units in the micro‐/nanoarrays and the spatial patterns of the aligned units, thus realizing controllable light–matter interactions. This review describes various light management strategies based on patterned micro‐/nanoarrays, such as scattering enhancement, antireflection, resonances, photonic crystals, and plasmonic structures. Furthermore, recent advances in the applications of patterned micro‐/nanoarrays in photoelectrochemical water splitting, solar cells, photodetectors, light emitting diodes, lasers, color display, microlens arrays, and photonic crystal sensors are summarized, with particular attention paid to the light management mechanisms and the relationship between the structure and device performance. Lastly, the prospects and existing challenges facing the development of the photon‐related applications based on patterned micro‐/nanoarrays are discussed.
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Metadata
- Authors
- Wang, W., & Qi, L.
- Venue
- Advanced Functional Materials
- Publisher
- Wiley
- Published
- 2019-04-03
- Volume
- 29
- Issue
- 25
- ISSN
- 1616-301X (ISSN-L)
- 1616-3028
- OpenAlex
- W2930194321
- Cited by
- 256
- References
- 176
- FWCI
- 9.11
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
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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.
