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Mass production of thin-walled hollow optical fibers enables disposable optofluidic laser immunosensors

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Abstract

Disposable biosensors are of great importance in disease diagnosis due to their inherent merits of no cross-contamination and ease of use. Optofluidic laser (OFL) sensors are a new category of sensitive biosensors; however, it is challenging to cost-effectively mass-produce them to achieve disposability. Here, we report a disposable optofluidic laser immunosensor based on thin-walled hollow optical fibers (HOFs). Using a fiber draw tower, the fabrication parameters, including drawing speed and gas flow rate, are explored, and the HOF geometry is precisely controlled, which allows identical laser microring resonators to be distributed along the fibers. The disposable OFL immunosensor detects the protein concentration in the HOF through a wash-free immunoassay. Enabled by the disposable sensors, the statistical characteristics of 80 tests for each concentration greatly reduces the bioassay uncertainty. A low coefficient of variation (CV) of 3.3% confirms the high reproducibility of the disposable HOF-OFL sensors, and the mean of the normal distribution of the logarithmic OFL intensity serves as the sensing output. A limit of detection of 11 nM within a short assay time of 15 min is achieved. These disposable immunosensors possess the advantages of low cost, high reproducibility, fast assay, and low-volume consumption of sample and reagents. We believe that this work will inspire disposable optofluidics through the mass production of multifunctional microstructured optical fibers.

Graphical abstract: Mass production of thin-walled hollow optical fibers enables disposable optofluidic laser immunosensors

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Supplementary files

Article information


Submitted
11 Dec 2019
Accepted
31 Jan 2020
First published
01 Feb 2020

Lab Chip, 2020, Advance Article
Article type
Paper

Mass production of thin-walled hollow optical fibers enables disposable optofluidic laser immunosensors

X. Yang, Y. Luo, Y. Liu, C. Gong, Y. Wang, Y. Rao, G. Peng and Y. Gong, Lab Chip, 2020, Advance Article , DOI: 10.1039/C9LC01216H

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