Enhanced light–matter interactions in dielectric nanostructures via machine-learning approach
- Submitting institution
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Nottingham Trent University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 34 - 1354984
- Type
- D - Journal article
- DOI
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10.1117/1.ap.2.2.026003
- Title of journal
- Advanced Photonics
- Article number
- 026003
- First page
- -
- Volume
- 2
- Issue
- 2
- ISSN
- 2577-5421
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2020
- URL
-
-
- Supplementary information
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- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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11
- Research group(s)
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A - Imaging, Materials and Engineering Centre
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The modelling approach in this paper can significantly improve the performance of cutting-edge miniaturised optical components, e.g. optical filters, switches and sensors (see https://www.sciencedaily.com/releases/2020/04/200430150226.htm). Newly designed nanoscale optical components, will lead to developing a new generation high-tech devices e.g. detectors and cameras. Mohsen Rahmani was awarded funding by the Royal Society Wolfson Fellowship (RSWF\FT\191022) to further develop this innovation and the paper is among the Advanced Photonics 2020 top three downloads (http://www.clp.ac.cn/EN/NewsDetails/bec5f198-8427-4c19-9636-10f9c75481a2).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -