[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5695/JSSE.2022.55.3.156

Suggestion for deep learning approach to solve the interference effect of ammonium ion on potassium ion-selective electrode

Kim, Min-Yeong (Department of Electrochemistry, Surface Technology Division, Korea Institute of Materials Science (KIMS))
Heo, Jae-Yeong (Department of Electrochemistry, Surface Technology Division, Korea Institute of Materials Science (KIMS))
Oh, Eun Hun (Department of Mechanical Engineering, Pusan National University (PNU))
Lee, Joo-Yul (Department of Electrochemistry, Surface Technology Division, Korea Institute of Materials Science (KIMS))
Lee, Kyu Hwan (Department of Electrochemistry, Surface Technology Division, Korea Institute of Materials Science (KIMS))

Publication Information

Journal of the Korean institute of surface engineering / v.55, no.3, 2022 , pp. 156-163 More about this Journal

Abstract

An ammonium ion with a size and charge similar to that of potassium can bind to valinomycin, which is used as an ion carrier for potassium, and cause a meaningful interference effect on the detection of potassium ions. Currently, there are few ion sensors that correct the interference effect of ammonium ions, and there are few studies that specifically suggest the mechanism of the interference effect. By fabricating a SPCE-based potassium ion-selective electrode, the electromotive force was measured in the concentration range of potassium in the nutrient solution, and the linear range was measured to be 10^-5 to 10^-2 M, and the detection limit was 10^-5.19 M. And the interference phenomenon of the potassium sensor was investigated in the concentration range of ammonium ions present in the nutrient solution. Therefore, a data-based analysis strategy using deep learning was presented as a method to minimize the interference effect.

Keywords

K ion detection; ion-selective electrode; $NH_4^+$ interference behavior; $K^+$ sensor; deep learning;

Citations & Related Records

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