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Sensors 2017, 17(7), 1558; doi:10.3390/s17071558

Direct Quantification of Cd2+ in the Presence of Cu2+ by a Combination of Anodic Stripping Voltammetry Using a Bi-Film-Modified Glassy Carbon Electrode and an Artificial Neural Network

1,2
,
1,2
and
1,2,*
1
Key Lab of Modern Precision Agriculture System Integration Research, Ministry of Education of China, China Agricultural University, Beijing 100083, China
2
Key Lab of Agricultural Information Acquisition Technology, Ministry of Agricultural of China, China Agricultural University, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Received: 22 May 2017 / Revised: 29 June 2017 / Accepted: 30 June 2017 / Published: 3 July 2017
(This article belongs to the Section Chemical Sensors)
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Abstract

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. View Full-Text
Keywords: bismuth-film electrode; artificial neural network; square-wave anodic stripping voltammetry; Cu2+; Cd2+; quantitative detection bismuth-film electrode; artificial neural network; square-wave anodic stripping voltammetry; Cu2+; Cd2+; quantitative detection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhao, G.; Wang, H.; Liu, G. Direct Quantification of Cd2+ in the Presence of Cu2+ by a Combination of Anodic Stripping Voltammetry Using a Bi-Film-Modified Glassy Carbon Electrode and an Artificial Neural Network. Sensors 2017, 17, 1558.

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