journal article·2025-08-14Open access

Deep-ultraviolet plasmon resonances on hybrid Si nanostructures for photoluminescence enhancement

Tao Liu, Haowen Chen, Wenxuan Liu, Huazhen Wu, Xing Zhen, Shasha Li, Lei Shao, Shao-Zhi Deng

Discover Nano· Volume 20· Issue 1

Abstract

Deep ultraviolet (DUV) nanophotonic technologies are of vital importance for applications in biomedical sensing, advanced lithography, light sources, and optoelectronic devices. Plasmonic nanostructures with DUV resonance properties can generate highly confined optical fields. They therefore have great potential in amplifying spectral signals from molecules with intense vibronic transitions in the DUV region and improving the sensitivity of solar-blind detection. However, practical applications of DUV plasmonic structures are hindered by challenges such as oxidation, photo-induced damage, high material loss, and costly fabrication. Herein, we employ hybrid Si Fabry-Pérot nanoresonators constructed from random Si nanodisk arrays and a Si mirror to improve the DUV plasmonic properties of individual Si nanostructures. The hybrid nanoresonators exhibit strong resonance modes that are tunable in the DUV regime, resulting from the coupling between nanodisk plasmon resonances and Fabry-Pérot cavity modes. In addition, we fabricate centimeter-scale nanoresonator arrays that support distinct DUV plasmon resonances using a low-cost hole-mask colloidal lithography method. We further demonstrate that the hybrid Si nanoresonator substrate can enhance the molecular ultraviolet photoluminescence by a factor of up to 2.7. By combining the advantages of Si nanodisks' DUV localized surface plasmon and Fabry-Pérot cavity resonances, our design offers a promising platform for molecular detection, solar-blind photodetection, and biosensing.
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Research context

OpenAlex topics: Plasmonic and Surface Plasmon Research · Gold and Silver Nanoparticles Synthesis and Applications · Optical Coatings and Gratings

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

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