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RSS FeedsSensors, Vol. 19, Pages 5482: Synergetic Sensing Effect of Sodium Carboxymethyl Cellulose and Bismuth on Cadmium Detection by Differential Pulse Anodic Stripping Voltammetry (Sensors)

 
 

12 december 2019 14:02:37

 
Sensors, Vol. 19, Pages 5482: Synergetic Sensing Effect of Sodium Carboxymethyl Cellulose and Bismuth on Cadmium Detection by Differential Pulse Anodic Stripping Voltammetry (Sensors)
 


In the present work, a novel electrochemical sensor was developed for the detection of trace cadmium with high sensitivity and selectivity in an easy and eco-friendly way. Firstly, a glassy carbon electrode (GCE) was modified with nontoxic sodium carboxymethyl cellulose (CMC) by a simple drop-casting method, which was applied to detect cadmium by differential pulse anodic stripping voltammetry (DPASV) in a solution containing both target cadmium and eco-friendly bismuth ions, based on a quick electro-codeposition of these two metal ions on the surface of the modified electrode (CMC-GCE). Investigated by scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and Fourier transform infrared spectroscopy (FT-IR), both CMC (with good film-forming ability) and bismuth (with well-defined stripping signal) were found to be well complexed with target cadmium, leading to vital signal amplification for cadmium detection at a sub-nanomolar level. Under the optimal conditions, the proposed sensor exhibited a good linear stripping signal response to cadmium (II) ion, in a concentration range of 0.001 μmol/L–1 μmol/L with a limit of detection of 0.75 nmol/L (S/N = 3). Meanwhile, the results demonstrate that this novel electrochemical sensor has excellent sensitivity and reproducibility, which can be used as a promising detection technique for testing natural samples such as tap water.


 
218 viewsCategory: Chemistry, Physics
 
Sensors, Vol. 19, Pages 5483: Electro-Oxidation and Simultaneous Determination of Indole-3-Acetic Acid and Salicylic Acid on Graphene Hydrogel Modified Electrode (Sensors)
Sensors, Vol. 19, Pages 5481: Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging (Sensors)
 
 
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