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review article

Metasurface-Based Molecular Biosensing Aided by Artificial Intelligence

Tittl, Andreas  
•
John-Herpin, Aurelian  
•
Leitis, Aleksandrs  
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August 8, 2019
Angewandte Chemie International Edition

Molecular spectroscopy provides unique information on the internal structure of biological materials by detecting the characteristic vibrational signatures of their constituent chemical bonds at infrared frequencies. Nanophotonic antennas and metasurfaces have driven this concept towards few-molecule sensitivity by confining incident light into intense hot spots of the electromagnetic fields, providing strongly enhanced light-matter interaction. In this Minireview, recently developed molecular biosensing approaches based on the combination of dielectric metasurfaces and imaging detection are highlighted in comparison to traditional plasmonic geometries, and the unique potential of artificial intelligence techniques for nanophotonic sensor design and data analysis is emphasized. Because of their spectrometer-less operation principle, such imaging-based approaches hold great promise for miniaturized biosensors in practical point-of-care or field-deployable applications.

  • Details
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Type
review article
DOI
10.1002/anie.201901443
Web of Science ID

WOS:000480980000001

Author(s)
Tittl, Andreas  
John-Herpin, Aurelian  
Leitis, Aleksandrs  
Arvelo, Eduardo R.
Altug, Hatice  
Date Issued

2019-08-08

Publisher

Wiley-VCH Verlag GmbH

Published in
Angewandte Chemie International Edition
Volume

58

Issue

42

Start page

14810

End page

14822

Subjects

Chemistry, Multidisciplinary

•

Chemistry

•

biosensors

•

deep learning

•

molecular spectroscopy

•

nanophotonics

•

plasmonics

•

enhanced infrared-absorption

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plasmonic perfect absorber

•

nanoantenna arrays

•

fano resonances

•

spectroscopy

•

nanostructures

•

nanocavities

•

aggregation

•

monolayers

•

antennas

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
BIOS  
Available on Infoscience
August 29, 2019
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/160675
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