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Publication Additional Information Download
Publication Type
Journal Article
Authorship
Bo Xiong, Eric Mahoney, Joe F. Lo, Qiyin Fang
Title
A Frequency-domain optofluidic dissolved oxygen sensor with total internal reflection design for in situ monitoring
Year
2021
Publication Outlet
IEEE Selected Topics in Quantum Electronics, 27(4):1-7
DOI
https://doi.org/10.1109/JSTQE.2020.2997810
Citation
Bo Xiong, Eric Mahoney, Joe F. Lo, Qiyin Fang, A Frequency-domain optofluidic dissolved oxygen sensor with total internal reflection design for in situ monitoring, IEEE Selected Topics in Quantum Electronics, 27(4):1-7, 2021
Abstract
Continuous measurement of dissolved oxygen (DO) variation is important in water monitoring and biomedical applications, which require low-cost and low-maintenance sensors capable of automated operation. A frequency-domain optofluidic DO sensor with total internal reflection (TIR) design has been developed based on fluorescence quenching of Ruthenium complex (Ru(dpp) 3 Cl 2 ). To minimize artifacts causing drift in fluorescence measurements such as background autofluorescence, photobleaching, optical alignment variation, a low-cost frequency-domain approach is implemented in an optofluidic platform to measure the phase shift between the excitation and emission light. We show that the frequency domain optofluidic DO sensor provides absolute DO concentrations in repeated measurements. TIR design can enhance fluorescence signal in the integrated device and minimize background autofluorescence in the sample, which can subsequently improve overall sensitivity. Furthermore, photobleaching in the samples would be mitigated as the incident light does not enter the microfluidic channel. Our results demonstrate a measurement resolution of 0.2 ppm and response times of less than one minute. In accelerated photobleaching conditions, the long-term drift is shown to be less than ±0.4 ppm. These results suggest the potential of this optofluidic DO sensor as an in situ platform for water monitoring and biomedical applications.
Program Affiliations
GWF: Global Water Futures
Project Affiliations
GWF-Artificial Intelligence for Rapid and Reliable Detection of Cryptosporidium oocysts and Giardia cysts
Publication Stage
Published
Additional Information
Cryptosporidium-oocysts-Giardia-cysts, Refereed Publications
Download Links
https://doi.org/10.1109/JSTQE.2020.2997810
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