Journal
SENSORS AND ACTUATORS B-CHEMICAL
Volume 364, Issue -, Pages -Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.snb.2022.131818
Keywords
Long period grating; Fiber Bragg grating; Heavy metal ion sensor; Temperature sensor; Chemical sensor; Artificial Neural Network; Graphene oxide; Polymer
Funding
- Natural Environment Research Council (NERC), UK [NE/R003289]
- City, University of London, UK
- Royal Academy of Engineering, UK
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Research on compact, flexible optical sensors for water quality monitoring, specifically targeting heavy metal ion monitoring, has become extremely important. An innovative sensor solution based on optical fiber grating sensors has been proposed, which shows high sensitivity and selectivity in detecting sub-nanomolar concentration of lead ions.
Research on compact, flexible optical sensors for water quality monitoring, specifically targeting heavy metal ion monitoring, has become extremely important due to the increasing number of water pollution incidents seen worldwide where such heavy metals are involved. Optical fiber-based sensors provide an excellent basis for creating new sensing solutions across a wide area, including for energy, healthcare, structural monitoring, defense and importantly here for environmental monitoring. An innovative, cost-optimized sensor solution to better heavy metal detection is proposed, by introducing a hybrid optical fiber grating sensor system based on concatenating a Long Period Grating (LPG) and Fiber Bragg Grating (FBG) for the concurrent detection of an important, specific heavy metal ion pollutant (in this case lead (Pb2+)). The approach uses the functionalization of an optical fiber grating with a chemically synthesized novel nanocomposite material (together with temperature sensing to allow such corrections to be applied). Such a method not only significantly enhances the system sensitivity (achieving 2.547 nm/nM), with a detection limit (0.5 nM), and high selectivity to the Pb2+ ions, but also mitigates the shortcomings of cross-sensitivity seen with many such sensors. Furthermore, in this work, the incorporation of a forward Artificial Neural Network (ANN)-based predictive algorithm has been incorporated to create an effective, well-calibrated system whose characteristics as an intelligent, highly sensitive system has been demonstrated in the detection of the sub-nanomolar concentration of Pb2+ ion in drinking water.
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