Lead (Pb2+) ion sensor development using optical fiber gratings and nanocomposite materials
Lead (Pb2+) ion sensor development using optical fiber gratings and nanocomposite materials
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DOI：
10.1016/j.snb.2022.131818
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发表时间：
2022-04-14
影响因子：
8.4
通讯作者：
Grattan, Kenneth T. V.
中科院分区：
文献类型：
--
作者：
Ghosh, Souvik;Dissanayake, Kasun;Grattan, Kenneth T. V.
由于世界范围内涉及重金属的水污染事件越来越多，研究用于水质监测的紧凑型、柔性光学传感器，特别是针对重金属离子监测，变得极其重要。基于光纤的传感器为在广泛领域创建新的传感解决方案提供了良好的基础，包括能源，医疗保健，结构监测，国防以及重要的环境监测。通过引入一种基于长周期光栅（LPG）和光纤布拉格光栅（FBG）的混合光纤光栅传感器系统，同时检测一种重要的、特定的重金属离子污染物（在本例中为铅（Pb2+）），提出了一种创新的、成本优化的传感器解决方案，以更好地检测重金属。该方法使用了一种化学合成的新型纳米复合材料的光纤光栅的功能化（连同温度传感，以允许应用这种校正）。该方法不仅显著提高了系统灵敏度（达到2.547 nm/ nm），检测限（0.5 nm），对Pb2+离子具有高选择性，而且减轻了许多此类传感器存在交叉灵敏度的缺点。此外，在这项工作中，结合了基于前向人工神经网络（ANN）的预测算法，创建了一个有效的、校准良好的系统，该系统具有智能、高灵敏度的特点，已在饮用水中Pb2+离子亚纳摩尔浓度的检测中得到证实。
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.