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Optics miniaturization strategy for demanding Raman spectroscopy applications

Author

Listed:
  • Oleksii Ilchenko

    (Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics
    Lightnovo ApS)

  • Yurii Pilhun

    (Lightnovo ApS
    Taras Shevchenko National University of Kyiv)

  • Andrii Kutsyk

    (Lightnovo ApS
    Taras Shevchenko National University of Kyiv
    Department of Energy Conversion and Storage)

  • Denys Slobodianiuk

    (Taras Shevchenko National University of Kyiv
    Institute of Magnetism)

  • Yaman Goksel

    (Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics)

  • Elodie Dumont

    (Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics)

  • Lukas Vaut

    (Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics)

  • Chiara Mazzoni

    (Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics)

  • Lidia Morelli

    (Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics)

  • Sofus Boisen

    (Lightnovo ApS)

  • Konstantinos Stergiou

    (Lightnovo ApS)

  • Yaroslav Aulin

    (Lightnovo ApS)

  • Tomas Rindzevicius

    (Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics)

  • Thomas Emil Andersen

    (Odense University Hospital and Research Unit of Clinical Microbiology, University of Southern Denmark)

  • Mikael Lassen

    (Danish Fundamental Metrology)

  • Hemanshu Mundhada

    (Cysbio ApS)

  • Christian Bille Jendresen

    (Cysbio ApS)

  • Peter Alshede Philipsen

    (Copenhagen University Hospital)

  • Merete Hædersdal

    (Copenhagen University Hospital
    Copenhagen University)

  • Anja Boisen

    (Center for Intelligent Drug Delivery and Sensing Using Microcontainers and Nanomechanics)

Abstract

Raman spectroscopy provides non-destructive, label-free quantitative studies of chemical compositions at the microscale as used on NASA’s Perseverance rover on Mars. Such capabilities come at the cost of high requirements for instrumentation. Here we present a centimeter-scale miniaturization of a Raman spectrometer using cheap non-stabilized laser diodes, densely packed optics, and non-cooled small sensors. The performance is comparable with expensive bulky research-grade Raman systems. It has excellent sensitivity, low power consumption, perfect wavenumber, intensity calibration, and 7 cm−1 resolution within the 400–4000 cm−1 range using a built-in reference. High performance and versatility are demonstrated in use cases including quantification of methanol in beverages, in-vivo Raman measurements of human skin, fermentation monitoring, chemical Raman mapping at sub-micrometer resolution, quantitative SERS mapping of the anti-cancer drug methotrexate and in-vitro bacteria identification. We foresee that the miniaturization will allow realization of super-compact Raman spectrometers for integration in smartphones and medical devices, democratizing Raman technology.

Suggested Citation

  • Oleksii Ilchenko & Yurii Pilhun & Andrii Kutsyk & Denys Slobodianiuk & Yaman Goksel & Elodie Dumont & Lukas Vaut & Chiara Mazzoni & Lidia Morelli & Sofus Boisen & Konstantinos Stergiou & Yaroslav Auli, 2024. "Optics miniaturization strategy for demanding Raman spectroscopy applications," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47044-7
    DOI: 10.1038/s41467-024-47044-7
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    References listed on IDEAS

    as
    1. Derek M. Kita & Brando Miranda & David Favela & David Bono & Jérôme Michon & Hongtao Lin & Tian Gu & Juejun Hu, 2018. "High-performance and scalable on-chip digital Fourier transform spectroscopy," Nature Communications, Nature, vol. 9(1), pages 1-7, December.
    2. Chi-Sing Ho & Neal Jean & Catherine A. Hogan & Lena Blackmon & Stefanie S. Jeffrey & Mark Holodniy & Niaz Banaei & Amr A. E. Saleh & Stefano Ermon & Jennifer Dionne, 2019. "Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
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