Metasurface-Enhanced Infrared Spectroscopy: An Abundance of Materials and Functionalities

Aurelian John-Herpin, Andreas Tittl, Lucca Kühner, Felix Richter, Steven H. Huang, Gennady Shvets, Sang Hyun Oh, Hatice Altug

Research output: Contribution to journalReview articlepeer-review

32 Scopus citations

Abstract

Infrared spectroscopy provides unique information on the composition and dynamics of biochemical systems by resolving the characteristic absorption fingerprints of their constituent molecules. Based on this inherent chemical specificity and the capability for label-free, noninvasive, and real-time detection, infrared spectroscopy approaches have unlocked a plethora of breakthrough applications for fields ranging from environmental monitoring and defense to chemical analysis and medical diagnostics. Nanophotonics has played a crucial role for pushing the sensitivity limits of traditional far-field spectroscopy by using resonant nanostructures to focus the incident light into nanoscale hot-spots of the electromagnetic field, greatly enhancing light–matter interaction. Metasurfaces composed of regular arrangements of such resonators further increase the design space for tailoring this nanoscale light control both spectrally and spatially, which has established them as an invaluable toolkit for surface-enhanced spectroscopy. Starting from the fundamental concepts of metasurface-enhanced infrared spectroscopy, a broad palette of resonator geometries, materials, and arrangements for realizing highly sensitive metadevices is showcased, with a special focus on emerging systems such as phononic and 2D van der Waals materials, and integration with waveguides for lab-on-a-chip devices. Furthermore, advanced sensor functionalities of metasurface-based infrared spectroscopy, including multiresonance, tunability, dielectrophoresis, live cell sensing, and machine-learning-aided analysis are highlighted.

Original languageEnglish (US)
Article number2110163
JournalAdvanced Materials
Volume35
Issue number34
DOIs
StatePublished - Aug 24 2023

Bibliographical note

Funding Information:
A.J.‐H. and A.T. contributed equally to this work. A.J.‐H., F.R., and H.A. acknowledge funding from the European Research Council (ERC) under Grant Agreement No. 682167 (VIBRANT‐BIO) and the European Union Horizon 2020 Framework Programme for Research and Innovation under Grant Agreement No. 777714 (NOCTURNO). A.T. and L.K. acknowledge funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under grant numbers EXC 2089/1 – 390776260 (Germany's Excellence Strategy) and TI 1063/1 (Emmy Noether Program), the Bavarian program Solar Energies Go Hybrid (SolTech), and the Center for NanoScience (CeNS). S.H.H. and G.S. acknowledge funding by the National Cancer Institute of the National Institutes of Health under award number R21 CA251052 and the National Institute of General Medical Sciences of the National Institutes of Health under award number R21 GM138947. S.‐H.O. acknowledges funding from the Samsung Global Research Outreach (GRO) program, the Sanford P. Bordeau Chair in Electrical Engineering at the University of Minnesota, and the Minnesota Environment and Natural Resources Trust Fund as recommended by the Legislative‐Citizen Commission on Minnesota Resources.

Publisher Copyright:
© 2022 The Authors. Advanced Materials published by Wiley-VCH GmbH.

Keywords

  • biosensors
  • machine learning
  • metasurfaces
  • nanophotonics
  • surface-enhanced spectroscopy

PubMed: MeSH publication types

  • Journal Article
  • Review

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