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Nanomaterials
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Optical Resonators: Advanced Platform for Sensing Applications
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Optical Resonators: Advanced Platform for Sensing Applications

A special issue of Nanomaterials (ISSN 2079-4991). This special issue belongs to the section "Nanophotonics Materials and Devices".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 3397

Special Issue Editor

Dr. Hiromasa Shimizu

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, Department of Applied Physics and Chemical Engineering, Tokyo University of Agriculture and Technology, Tokyo University of Agriculture and Technology, Koganei, Tokyo, Japan
Interests: optical sensor; plasmonics; optical resonator; surface plasmon resonance sensor; optical isolator; optical integrated circuit
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Optical resonators are important platforms to realize compact sensing applications in a variety of fields, such as security, healthcare, environmental, and food industry. Much effort has been reported in terms of (1) type and size of transducers in order to detect the difference of refractive index or optical absorption; (2) materials, including inorganic materials (SiO2, SiN, Si, etc.) and organic materials (polymer); (3) adsorbents (polymer, receptor) for real time detection of target molecules (proteins, DNA, viruses, gas molecules). Fabrication and miniaturization of the transducer array combined with many kinds of adsorbents will lead to detection combined with machine learning. Submissions of recent works on advanced optical resonators towards sensing applications are welcome for exchanging the information among the researchers in a variety of fields.

Dr. Hiromasa Shimizu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Nanomaterials is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • optical resonator
  • photonic crystal
  • frequency comb
  • sensing
  • transducers
  • adsorbents
  • receptor
  • machine learning

Published Papers (1 paper)

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Review

25 pages, 5125 KiB  
Review
Instantaneous Property Prediction and Inverse Design of Plasmonic Nanostructures Using Machine Learning: Current Applications and Future Directions
by Xinkai Xu, Dipesh Aggarwal and Karthik Shankar
Nanomaterials 2022, 12(4), 633; https://0-doi-org.brum.beds.ac.uk/10.3390/nano12040633 - 14 Feb 2022
Cited by 10 | Viewed by 3112
Abstract
Advances in plasmonic materials and devices have given rise to a variety of applications in photocatalysis, microscopy, nanophotonics, and metastructures. With the advent of computing power and artificial neural networks, the characterization and design process of plasmonic nanostructures can be significantly accelerated using [...] Read more.
Advances in plasmonic materials and devices have given rise to a variety of applications in photocatalysis, microscopy, nanophotonics, and metastructures. With the advent of computing power and artificial neural networks, the characterization and design process of plasmonic nanostructures can be significantly accelerated using machine learning as opposed to conventional FDTD simulations. The machine learning (ML) based methods can not only perform with high accuracy and return optical spectra and optimal design parameters, but also maintain a stable high computing efficiency without being affected by the structural complexity. This work reviews the prominent ML methods involved in forward simulation and inverse design of plasmonic nanomaterials, such as Convolutional Neural Networks, Generative Adversarial Networks, Genetic Algorithms and Encoder–Decoder Networks. Moreover, we acknowledge the current limitations of ML methods in the context of plasmonics and provide perspectives on future research directions. Full article
(This article belongs to the Special Issue Optical Resonators: Advanced Platform for Sensing Applications)
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