journal article·2022-12

Near-unity Raman β-factor of surface-enhanced Raman scattering in a waveguide

Ming Fu, Mónica P. dS. P. Mota, Xiaofei Xiao, Andrea Jacassi, Nicholas A. Güsken, Yuxin Chen, Huaifeng Xiao, Yi Li, Ahad Riaz, Stefan A. Maier, Rupert F. Oulton

Nature Nanotechnology· Volume 17· Issue 12
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Abstract

The Raman scattering of light by molecular vibrations is a powerful technique to fingerprint molecules through their internal bonds and symmetries. Since Raman scattering is weak1, methods to enhance, direct and harness it are highly desirable, and this has been achieved using optical cavities2, waveguides3-6 and surface-enhanced Raman scattering (SERS)7-9. Although SERS offers dramatic enhancements2,6,10,11 by localizing light within vanishingly small hot-spots in metallic nanostructures, these tiny interaction volumes are only sensitive to a few molecules, yielding weak signals12. Here we show that SERS from 4-aminothiophenol molecules bonded to a plasmonic gap waveguide is directed into a single mode with >99% efficiency. Although sacrificing a confinement dimension, we find a SERS enhancement of ~103 times across a broad spectral range enabled by the waveguide's larger sensing volume and non-resonant waveguide mode. Remarkably, this waveguide SERS is bright enough to image Raman transport across the waveguides, highlighting the role of nanofocusing13-15 and the Purcell effect16. By analogy to the β-factor from laser physics10,17-20, the near-unity Raman β-factor we observe exposes the SERS technique to alternative routes for controlling Raman scattering. The ability of waveguide SERS to direct Raman scattering is relevant to Raman sensors based on integrated photonics7-9 with applications in gas sensing and biosensing.
Source: europepmc · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Gold and Silver Nanoparticles Synthesis and Applications · Plasmonic and Surface Plasmon Research · Photonic and Optical Devices

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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