4.7 Article

Smartphone-powered portable chemiresistive sensing system for label free detection of lead ions in water

Journal

MICROCHEMICAL JOURNAL
Volume 194, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.microc.2023.109239

Keywords

Lead; Chemiresistive sensors; Android interface; Smart readout circuit; Environmental monitoring

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This paper presents a portable, label-free chemiresistive sensor for the selective detection of lead (Pb) ions in water. The sensor exhibits high selectivity and can be analyzed quickly using a smartphone interface. The results show that the sensor can successfully detect the presence of lead ions even at a concentration as low as 100 pico Molar (pM). In addition, a machine learning algorithm is used to enhance data analysis and assist in classification.
Lead (Pb) is a toxic heavy metal that can contaminate water sources and cause serious health problems. Detecting Pb in water is essential for protecting public health and the environment. However, the current state of technology heavily depends upon traditional bench-top equipment for detecting toxic heavy metal ions dissolved in water. In this paper we report a portable, label-free chemiresistive sensor for the selective detection of Pb ions dissolved in water. Lithography patterned micro-ineterdigitated electrodes (& mu;IDEs) modified with Graphene oxide (GO) is used as the transducing agent to capture the sensor's electrical response on a portable readout platform with a smartphone interface. Graphene oxide functionalized with environment friendly oligosaccharide & beta;- Cyclodextrin acts as the sensing material. The sensor's electrical properties undergo variation in the presence of Pb ions, hence serving as a parameter for successfully detecting the targeted heavy metal ion. The designed sensor is highly selective towards Pb ions, even in the presence of other heavy metal ions, with the lowest detection concentration being 100 pico Molar (pM). The smartphone-powered portable readout circuit, along with the sensor, makes the entire system energy-efficient and cost-effective, providing a quick analysis of the sensor output. Further, an ML algorithm is used to enhance the data analysis and assist the classification based on sensor response.

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