Lead (Pb2+) ion sensor development using optical fiber gratings and nanocomposite materials

被引:37
作者
Ghosh, Souvik [1 ,4 ]
Dissanayake, Kasun [2 ]
Asokan, S. [3 ]
Sun, T. [1 ]
Rahman, B. M. Azizur [1 ]
Grattan, Kenneth T. V. [1 ]
机构
[1] City Univ London, Inst Sensors & Instrumentat, London EC1V 0HB, England
[2] Sri Lanka Inst Nanotechnol Pvt Ltd, Homagama, Sri Lanka
[3] Indian Inst Sci, Dept Instrumentat & Appl Phys, Bengaluru 560012, India
[4] UCL, Dept Elect & Elect Engn, London WC1E 7JE, England
基金
英国自然环境研究理事会;
关键词
Long period grating; Fiber Bragg grating; Heavy metal ion sensor; Temperature sensor; Chemical sensor; Artificial Neural Network; Graphene oxide; Polymer; SURFACE-PLASMON-RESONANCE; HEAVY-METAL IONS; ZEHNDER INTERFEROMETER; TEMPERATURE SENSOR; MODE; POLYMER; MECHANISMS; BIOSENSOR; CHITOSAN; ANTIBODY;
D O I
10.1016/j.snb.2022.131818
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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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页数:12
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