University of Exeter
Browse

Single Virus Detection on Silicon Photonic Crystal Random Cavities

Download (455.89 kB)
journal contribution
posted on 2025-08-01, 14:00 authored by K Watanabe, H Wu, J Xavier, LT Joshi, F Vollmer
On-chip silicon microcavity sensors are advantageous for the detection of virus and biomolecules due to their compactness and the enhanced light–matter interaction with the analyte. While their theoretical sensitivity is at the single-molecule level, the fabrication of high quality (Q) factor silicon cavities and their integration with optical couplers remain as major hurdles in applications such as single virus detection. Here, label-free single virus detection using silicon photonic crystal random cavities is proposed and demonstrated. The sensor chips consist of free-standing silicon photonic crystal waveguides and do not require pre-fabricated defect cavities or optical couplers. Residual fabrication disorder results in Anderson-localized cavity modes which are excited by a free space beam. The Q ≈105 is sufficient for observing discrete step-changes in resonance wavelength for the binding of single adenoviruses (≈50 nm radius). The authors’ findings point to future applications of CMOS-compatible silicon sensor chips supporting Anderson-localized modes that have detection capabilities at the level of single nanoparticles and molecules.

Funding

EP/R031428/1

Engineering and Physical Sciences Research Council (EPSRC)

History

Related Materials

Rights

© 2022 Wiley-VCH GmbH

Notes

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.

Journal

Small

Pagination

2107597-2107597

Publisher

Wiley

Version

  • Version of Record

Language

en

FCD date

2022-02-28T14:23:45Z

FOA date

2023-02-26T00:00:00Z

Citation

Published online 26 February 2022

Department

  • Physics and Astronomy

Usage metrics

    University of Exeter

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC