Cathodic Stripping Voltammetry for Determination of Trace Manganese with Graphite/Styrene-Acrylonitrile Copolymer Composite Electrodes

Validation of Electrochemical Sensor for Determination of Manganese in Drinking Water

Manganese (Mn) is an essential nutrient for metabolic functions, yet excessive exposure can lead to neurological disease in adults and neurodevelopmental deficits in children. Drinking water represents one of the routes of excessive Mn exposure. Both natural enrichment from rocks and soil, and man-made contamination can pollute groundwater that supplies drinking water for a substantial fraction of the U.S. population. Conventional methods for Mn monitoring in drinking water are costly and involve a long turn-around time. Recent advancements in electrochemical sensing, however, have led to the development of miniature sensors for Mn determination. These sensors rely on a cathodic stripping voltammetry electroanalytical technique on a miniaturized platinum working electrode. In this study, we validate these electrochemical sensors for the determination of Mn concentrations in drinking water against the standard method using inductively coupled plasma mass spectrometry (ICP-MS). Drinking water samples (n = 78) in the 0.03 ppb to 5.3 ppm range were analyzed. Comparisons with ICP-MS yielded 100% agreement, ∼70% accuracy, and ∼91% precision. We envision the use of our system for rapid and inexpensive point-of-use identification of Mn levels in drinking water, which is especially valuable for frequent monitoring where contamination is present.

Trace manganese detection via differential pulse cathodic stripping voltammetry using disposable electrodes: additively manufactured nanographite electrochemical sensing platforms

Additive manufacturing is a promising technology for the rapid and economical fabrication of portable electroanalytical devices.

Voltammetric determination of Pb, Cd, Zn, Cu and Se in milk and dairy products collected from Iran: An emphasis on permissible limits and risk assessment of exposure to heavy metals

This study was carried out to determine the concentrations of some heavy metals in dairy products, collected from five industrial regions in Iran (n = 250 samples) during winter and summer in 2013. The samples were analyzed using the differential pulse anodic and cathodic stripping voltammetry technique. The obtained ranges of mean Pb, Cd, Cu, Zn and Se were as follow: in raw milk 14.0, 1.11, 427, 571, 2.19 μg kg(-1), in pasteurized milk 9.59, 1.0, 378, 447, 1.78 μg kg(-1), in cheese 14.5, 1.25, 428, 586, 1.68 μg kg(-1), in yoghurt 7.54, 0.99, 399, 431, 1.23 μg kg(-1) and in doogh 7.2, 0.84, 320, 369, 0.99 μg kg(-1), respectively. In nearly all cases the concentrations of the metals were below the international permissible limits and do not pose a health concern for the consumption of milk and dairy products in Iran.

Spectrophotometric simultaneous determination of metal ions in cows' milk and vegetables with the aid of artificial neural networks using synthetic 2-benzylspiro[isoindoline-1,5'-oxazolidine]-2',3,4'-trione

BACKGROUND

A simple, selective and sensitive multi-component method for the simultaneous determination of Zn(2+) , Mn(2+) and Fe(3+) based on complex formation with 2-benzylspiro[isoindoline-1,5'-oxazolidine]-2',3,4'-trione using artificial neural networks is proposed.

RESULTS

The analytical data showed that metal-to-ligand ratios in Zn(2+) and Fe(3+) complexes was 1:1 and for Mn(2+) complex was 1:2. It was found at pH 6.5 and 5 min after mixing, the complexation reactions were completed. The coloured complexes exhibited absorption bands in the wavelength range 200-400 nm. The results showed that Zn(2+) , Mn(2+) and Fe(3+) could be determined in the range 0.1-18.0, 0.3-10.0 and 0.5-20.0 mg L(-1) , respectively.

CONCLUSION

The data obtained from synthetic mixtures of metal ions were processed by radial basis function networks (RBFNs) and back-propagation neural network. The optimal conditions of the neural networks were obtained by adjusting various parameters. Satisfactory precision and accuracy were obtained with all networks, although, because of surprisingly lower root mean square error (%) values, RBFNs were the preferred approach. The proposed approach was tested by analysing the composition of the different mixtures containing Zn(2+) , Mn(2+) and Fe(3+) . The proposed method was successfully applied to the simultaneous determination of Zn(2+) , Mn(2+) and Fe(3+) ions in milk and vegetable samples.