Potentiometric detection of mercury(II) ions using a carbon paste electrode modified with substituted thiourea-functionalized highly ordered nanoporous silica.

A potentiometric sensor is reported for the mercury(II) detection, which uses substituted thiourea-functionalized nanoporous silica (FTU-LUS-1) as the sensitive material. Substituted thiourea (FTU) and FTU-LUS-1 were first prepared and then characterized by 1H NMR, 19F NMR, 13C NMR, FTIR, XRD, TG and CNS elemental analysis. The electrodes with FTU-LUS-1 proportion of 10.0 wt% demonstrated very stable potentials. The prepared electrodes exhibit a Nernstian slope of 28.4 +/- 1.0 mV decade(-1) for mercury(II) ion over a wide concentration range of 1.0 x 10(-7) to 1.0 x 10(-1) mol dm(-3). The electrode exhibited a detection limit of 7.0 x 10(-8) mol dm(-3). Moreover, the selectivity coefficient, response time, performance, sensitivity and stability of the modified electrode were investigated. The electrode presented a response time of about 35 s, a high performance and sensitivity in a wide range of cation activities as well as good long term stability (more than 9 months). The method was satisfactory and could also be used to monitor the mercury(II) ion concentration in waste water and fish samples.

Development of a partial least-squares calibration model for simultaneous determination of elements by inductively coupled plasma-atomic emission spectrometry.

A partial least-squares (PLS) calibration model was developed for simultaneous multicomponent elemental analysis with inductively coupled plasma-atomic emission spectrometry (ICP-AES) in the presence of spectral interference. The best calibration model was obtained using a PLS2 algorithm. Validation was performed with an artificial test set. Multivariate calibration models were constructed using 2 series of synthetic mixtures (Zn, Cu, Fe, and U, V). Accuracy of the method was evaluated with unknown synthetic and real samples.

Prediction of acute in vivo toxicity of some amine and amide drugs to rats by multiple linear regression, partial least squares and an artificial neural network.

The oral acute in vivo toxicity of 32 amine and amide drugs was related to their structural-dependent properties. Genetic algorithm-partial least-squares and stepwise variable selection was applied to select of meaningful descriptors. Multiple linear regression (MLR), artificial neural network (ANN) and partial least square (PLS) models were created with selected descriptors. The predictive ability of all three models was evaluated and compared on a set of five drugs, which were not used in modeling steps. Average errors of 0.168, 0.169 and 0.259 were obtained for MLR, ANN and PLS, respectively.

Solid phase extraction and graphite furnace atomic absorption spectrometric determination of ultra trace amounts of bismuth in water samples.

A simple and reliable method for the selective extraction and determination of bismuth in water as well as alloy samples using octadecyl bonded silica cartridge modified with cyanex 301 and graphite furnace atomic absorption spectrometry is described. Extraction efficiency and influence of sample matrix, optimum amount of extraction ligand, type and least amount of proper eluent and flow rates were evaluated. The limit of detection of the proposed method is 0.01 ng ml(-1). The influence of potential interfering cations in water samples on the recovery of bismuth was investigated. The method was successfully applied to the extraction and determination of bismuth in natural water and alloy samples.