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Real-time, Economical Identification of Microplastics Using Impedance-based Interdigital Array Microelectrodes and k-Nearest Neighbor Model
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Researchers developed a low-cost, real-time sensor using impedance spectroscopy to identify and distinguish two common types of microplastics — polyethylene and polystyrene — with up to 90% accuracy. Affordable detection tools like this could make routine monitoring of microplastic contamination in water far more practical.
Microplastic, being a direct carrier of many pollutants, has caused grave concern and become a public issue. This gives rise to the need of a quick method for quantifying and identifying microplastics in the environment. This study uses impedance spectroscopy, particularly the imaginary part of impedance, for detection and identification of sample microplastics. Two type of common microplastic contaminants, Polyethylene and Polystyrene, diameter 20 µm and 150 µm, were chosen for this study. The results confirm accurate identification of microplastic material in question, by using self-normalized ratio between two characteristic frequencies of 7 MHz and 8.9 MHz, Z′f=7 MHz/Z′f=8.9 MHz. 3-kNN classifier built with the ratio Z′f=7 MHz/Z′f=8.9 MHz, and Z′f=8 MHz/Z′f=8.9 MHz, demonstrates accuracy upto 90% for the identification of single or both microplastic types in samples. These results confirm impedance spectroscopy, permitting rapid identification of microplastic without labeling and skillful techniques, as a potential rapid sensor.
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