Simultaneous determination of six inorganic anions in drinking water by non-suppressed ion chromatography

Adsorption of Nitrate Ions onto Sepiolite and Surfactant-Modified Sepiolite

The adsorption of nitrate ions onto clay minerals has not been given much attention, possibly because clay surfaces are negatively charged. In order to increase the positive charge on the surface, sepiolite was modified in the present studies by treatment with dodecylethyldimethylammonium (DEDMA) bromide. After such modification, it was found that the maximum amount of nitrate ion adsorbed occurred at a pH value of 2.0.

The chemical composition of natural sepiolite was determined by EDX methods and surface characterization of both natural and modified sepiolite samples was undertaken using FT-IR spectroscopic techniques. The adsorption of nitrate ions onto these adsorbents was determined by ion chromatography. The kinetic parameters of the adsorption process were calculated and it was shown that the reaction kinetic data could be fitted using the pseudo-second-order rate model. The calculated results indicate that both natural and surfactant-modified sepiolite are effective sorbents for the removal of anionic contaminants; however, the surfactant-modified sepiolite (453 mmol/kg) was more effective than the unmodified sepiolite (408 mmol/kg) in this respect. The adsorption data obtained were well described by the Freundlich adsorption isotherm.

A colorimetric probe based on desensitized ionene-stabilized gold nanoparticles for single-step test for sulfate ions

Desensitized ionene-stabilized gold nanoparticles have been prepared and applied as a colorimetric probe for the single-step test for sulfate ions at the relatively high concentration level. The approach is based on aggregation of the nanoparticles leading to the change in absorption spectra and color of the solution. These nanoparticles are characterized by the decreased sensitivity due to both electrostatic and steric stabilization, which allows for simple, and rapid direct single-step determination of sulfate at the relatively high concentration level in real water samples without sample pretreatment or dilution. Influence of different factors (the time of interaction, pH, the concentrations of sulfate ions and the nanoparticles) on the aggregation and analytical performance of the procedure was investigated. The method allows for the determination of sulfate ions in the mass range of 0.2-0.4 mg with RSD of 5% from the sample volume of less than 2 mL. It has a sharp dependence of the colorimetric response on the concentration of sulfate, which makes it prospective for indicating deviations of the sulfate concentration regarding some declared value chosen within the above range. The time of the analysis is 2 min. The method was applied to the analysis of mineral water samples.

Application of artificial neural networks for gradient elution retention modelling in ion chromatography

Gradient elution in ion chromatography (IC) offers several advantages: total analysis time can be significantly reduced, overall resolution of a mixture can be increased, peak shape can be improved (less tailing) and effective sensitivity can be increased (because there is little variation in peak shape). More importantly, it provides the maximum resolution per time unit. The aim of this work was the development of a suitable artificial neural network (ANN) gradient elution retention model that can be used in a variety of applications for method development and retention modelling of inorganic anions in IC. Multilayer perceptron ANNs were used to model the retention behaviour of fluoride, chloride, nitrite, sulphate, bromide, nitrate and phosphate in relation to the starting time of gradient elution and the slope of the linear gradient elution curve. The advantage of the developed model is the application of an optimized two-phase training algorithm that enables the researcher to make use of the advantages of first- and second-order training algorithms in one training procedure. This results in better predictive ability, with less time required for the calculations. The number of hidden layer neurons and experimental data points used for the training set were optimized in terms of obtaining a precise and accurate retention model with respect to minimization of unnecessary experimentation and time needed for the calculation procedures. This study shows that developed, ANNs are the method of first choice for retention modelling of inorganic anions in IC.

Ion chromatographic method development for monitoring of seawater quality used in over-the-counter pharmaceutical industry

A methodology is developed for the analysis of inorganic anions (fluoride, chloride, bromide, sulphate) in seawater used for over-the-counter (OTC) nasal spray production. The eluent flow rate and concentration of eluent competing ions are optimised by using an artificial neural network resolution model in combination with normalised resolution product criterion function. The developed artificial neural network resolution model shows good predictive ability R2 > or = 0.9973. The determined ion chromatographic parameters enable baseline separation of all components of interest. By performing a validation procedure and a number of statistical tests, it is shown that the developed ion chromatographic method has superior performance characteristics: linearity R2 > or = 0.9993, recovery = 99.77-100.65%, repeatability RSD < or = 1.85%. This result proves that the proposed method can be used for routine quality assurance analysis in OTC pharmaceutical industry.

Optimization of artificial neural networks used for retention modelling in ion chromatography

The aim of this work is the development of an artificial neural network model, which can be generalized and used in a variety of applications for retention modelling in ion chromatography. Influences of eluent flow-rate and concentration of eluent anion (OH-) on separation of seven inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) were investigated. Parallel prediction of retention times of seven inorganic anions by using one artificial neural network was applied. MATLAB Neural Networks ToolBox was not adequate for application to retention modelling in this particular case. Therefore the authors adopted it for retention modelling by programming in MATLAB metalanguage. The following routines were written; the division of experimental data set on training and test set; selection of data for training and test set; Dixon's outlier test; retraining procedure routine; calculations of relative error. A three-layer feed forward neural network trained with a Levenberg-Marquardt batch error back propagation algorithm has been used to model ion chromatographic retention mechanisms. The advantage of applied batch training methodology is the significant increase in speed of calculation of algorithms in comparison with delta rule training methodology. The technique of experimental data selection for training set was used allowing improvement of artificial neural network prediction power. Experimental design space was divided into 8-32 subspaces depending on number of experimental data points used for training set. The number of hidden layer nodes, the number of iteration steps and the number of experimental data points used for training set were optimized. This study presents the very fast (300 iteration steps) and very accurate (relative error of 0.88%) retention model, obtained by using a small amount of experimental data (16 experimental data points in training set). This indicates that the method of choice for retention modelling in ion chromatography is the artificial neural network.

High-resolution microanalysis of nitrite and nitrate in neuronal tissues by capillary electrophoresis with conductivity detection

Nitrites and nitrates are widely used reporters of endogenous activity of nitric oxide synthases (NOS), an important group of enzymes producing the gaseous signal molecule nitric oxide (NO). However, due to the great chemical heterogeneity of neuronal tissues, standard analytical protocols for evaluation of neuronal nitrite/nitrate concentrations are inefficient. We optimized a high-performance capillary zone electrophoresis (CZE) technique to analyze nitrite/nitrate concentrations in submicroliter samples from mammalian neuronal tissues. The measurements were made using a PrinCE 476 computerized capillary electrophoresis system with a Crystal 1000 contact conductivity detector. Isotachophoretic stacking injection of 1000- to 10000-fold diluted samples, which had been pretreated with a custom-designed solid-phase microextraction (SPME) cartridge, was employed to assay micromolar and nanomolar nitrite and nitrate levels in the presence of the high millimolar chloride concentrations characteristic of many biological samples. In the presented technique, a 10-microl volume of diluted ganglionic sample was used for chloride removal and sample cleanup. The method yields high analytical performance, including good reproducibility, resolution, and accuracy. The limits of detection relative to undiluted sample matrix were 8.9 nM (0.41 ppb) and 3.54 nM (0.22 ppb) for nitrite and nitrate, respectively. In addition, this technique resolves other anions that are present in neuronal tissues at sub-nanomolar concentrations and can be broadly applied for high-throughput anionic profiling. In rat dorsal root ganglia, endogenous levels of nitrate (231+/-29 microM; n=6) and nitrite (24-96 microM) were found. These concentrations exceeded those previously found in neuronal tissue homogenates using different techniques.