Sensitive rGO/MOF based electrochemical sensor for penta-chlorophenol detection: a novel artificial neural network (ANN) application

Abstract

Hazardous phenols including penta-chlorophenol (5-CP) are considered an emerging global issue because they are toxic and harmful not only to the environment but also to the human health. Hence, detection of 5-CP traces is crucial for the safety. Compared to other analytical tools, electrochemical sensing approaches are cheap, fast, robust, and accurate in reliable characterization, identification, and quantification of 5-CP. For this purpose, this paper reports for the first time a novel synthesis procedure of an outstanding nanocomposite rGO/MOF and investigates its applicability to 5-CP identification. The synthesized materials were characterized by using scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray fluorescence (XRF). The electrochemical analysis of the developed sensor using cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and square wave voltammetry (SWV) demonstrate that the synthesized sensor had a high electrical conductivity and a significantly high catalytic activity. At a potential of 0.7 V (vs. SCE), 5-CP exhibits distinct oxidation peaks in the measured CV curves. The sensor works well over a wide linear range of 5-CP concentrations ranging from 50 μM to 200 μM. It achieves a sensitivity of 3.4 nA nM⁻¹ and a limit of detection of 75.63 nM, while the quantification limit is estimated to be around 254.54 nM. In addition, an artificial neural network (ANN) algorithm was developed and used to analyze the experimental data and offer an accurate estimation of 5-CP concentrations. The obtained results of the sensor are promising for the development of a low-cost 5-CP sensing system for in-field investigations (screening) of aquatic environments requiring the detection of environmental hazards.