Abstract
In this study, a novel method based on a Bi/glassy carbon electrode (Bi/GCE) for quantitatively and directly detecting Cd2+ in the presence of Cu2+ without further electrode modifications by combining square-wave anodic stripping voltammetry (SWASV) and a back-propagation artificial neural network (BP-ANN) has been proposed. The influence of the Cu2+ concentration on the stripping response to Cd2+ was studied. In addition, the effect of the ferrocyanide concentration on the SWASV detection of Cd2+ in the presence of Cu2+ was investigated. A BP-ANN with two inputs and one output was used to establish the nonlinear relationship between the concentration of Cd2+ and the stripping peak currents of Cu2+ and Cd2+. The factors affecting the SWASV detection of Cd2+ and the key parameters of the BP-ANN were optimized. Moreover, the direct calibration model (i.e., adding 0.1 mM ferrocyanide before detection), the BP-ANN model and other prediction models were compared to verify the prediction performance of these models in terms of their mean absolute errors (MAEs), root mean square errors (RMSEs) and correlation coefficients. The BP-ANN model exhibited higher prediction accuracy than the direct calibration model and the other prediction models. Finally, the proposed method was used to detect Cd2+ in soil samples with satisfactory results.
Highlights
Effectively detecting Cd2+ has become increasingly important because Cd2+ content in water and soil poses a serious threat to ecological systems and public health via the food chain due to its non-biodegradability and toxicity [1,2,3].Anodic stripping voltammetry (ASV), an electrochemical technique, has been widely used for the analysis of heavy metal ions (HMs) at trace levels because of its extraordinary characteristics, such as good selectivity, portability, low cost, fast analysis speed and excellent sensitivity [4,5,6,7,8]
The scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) analysis were carried out on JSM-6701F field emission scanning electron microscope produced by JEOL Ltd
This method was based on a combination of square-wave anodic stripping voltammetry (SWASV) and a back-propagation artificial neural network (BP-artificial neural network (ANN)) but did not require further electrode modifications or added ferrocyanide
Summary
Anodic stripping voltammetry (ASV), an electrochemical technique, has been widely used for the analysis of heavy metal ions (HMs) at trace levels because of its extraordinary characteristics, such as good selectivity, portability, low cost, fast analysis speed and excellent sensitivity [4,5,6,7,8]. During the analysis of HMs using ASV, the HMs were electrodeposited onto the electrode surface via an electrodeposition process and stripped off the electrode surface via a stripping process [9]. The concentration of the HMs was proportional to their stripping peak currents, which flow during the stripping process [10]. The presence of Cu2+ , which is the most pronounced interference ion, obviously influences the stripping currents of Cd2+ [11,12,13], leading to inaccurate detection results
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