Issue 8, 2023

A parylene-mediated plasmonic–photonic hybrid fiber-optic sensor and its instrumentation for miniaturized and self-referenced biosensing

Abstract

With the development of advanced nanofabrication techniques over the past decades, different nanostructure-based plasmonic fiber-optic sensors have been developed and have presented a low limit of detection for various biomolecules. However, owing to both the dependence on complex equipment and the trade-off between the fabrication cost and sensing performance, nanostructured plasmonic fiber-optic sensors are rarely used outside laboratories. To facilitate wider application of the plasmonic fiber-optic sensors, a parylene-mediated hybrid plasmonic–photonic cavity-based sensor was developed. Compared with a similar plasmonic sensor which only works in the plasmonic mode, the proposed hybrid sensor shows a higher reproducibility (CV < 2.5%) due to its resistance to fabrication variations. Meanwhile, a self-referenced detection mechanism and a novel miniaturized system were developed to adapt to the hybrid resonance sensor. The entire system only has a weight of 263 g, and a size of 12 cm × 10 cm × 8 cm, and is especially suitable for outdoor applications in a handheld manner. In experiments, a high refractive index sensitivity of 3.148 RIU−1 and real-time biomolecule monitoring at nanomolar concentrations were achieved by the proposed system, further confirming the potential of the miniaturized system as a candidate for point-of-care health diagnostics outside laboratories.

Graphical abstract: A parylene-mediated plasmonic–photonic hybrid fiber-optic sensor and its instrumentation for miniaturized and self-referenced biosensing

Supplementary files

Article information

Article type
Paper
Submitted
06 Jan 2023
Accepted
07 Mar 2023
First published
08 Mar 2023

Analyst, 2023,148, 1672-1681

A parylene-mediated plasmonic–photonic hybrid fiber-optic sensor and its instrumentation for miniaturized and self-referenced biosensing

X. Li, N. Wang, F. Wang, J. Liu, Y. Shi, J. Jiang, H. Liu, M. Li, L. Zhang, W. Zhang, Y. Zhao, L. Zhang and C. Huang, Analyst, 2023, 148, 1672 DOI: 10.1039/D3AN00028A

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