Array Of Ion-selective Electrodes Research Articles

Multicomponent Titration of Calcium + Magnesium Mixtures Employing a Potentiometric Electronic‐Tongue

A fast novel potentiometric titration procedure is proposed, in which the detection system is formed by a potentiometric electronic tongue. The titration consists in a reduced number of fixed titrant additions to the sample and the recording of the potentials of an array of Ion Selective Electrodes. The obtained data matrix is entered to an Artificial Neural Network response model, previously trained to furnish concentrations of a multicomponent mixture. The principle is demonstrated with automated EDTA titration of mixtures of Ca2+ and Mg2+ at fixed pH 8.5. In this case, five 2 ml fixed volume additions plus the readings of four sensors were adequate for the resolution of the mixture. In the conditions used, titration was feasible up to 3.3 mM total ion concentration, the precision was estimated as 3.20% RSD for Ca2+ and 2.76% RSD for Mg2+ (n=5), and the detection limits 0.16 mM Ca2+ and 0.26 mM Mg2+. The procedure was applied to mineral waters and compared with reference methods (correlation=0.92 for Ca2+ and correlation=0.89 for Mg2+, n=14).

Simultaneous titration of ternary alkaline–earth mixtures employing a potentiometric electronic tongue

A fast novel complexometric titration procedure is proposed, in which equivalence concentrations were determined using a potentiometric electronic tongue. The titration consists in a reduced number of fixed additions of titrant and the recording of the potentials of an array of ion-selective electrodes with cross-selectivity response. The generated data (number of sensors × number of additions) were used as input data to an artificial neural network response model previously trained with standards. The application of this procedure is demonstrated with automated EDTA titrations of Ca 2+, Mg 2+ and Sr 2+ mixtures, up to 3.3 mM total ion concentration. In the studied case, good results predicting the three concentrations were obtained using five 1 mL fixed volume additions plus the readings of eight sensors. The unique additional step required was the pH buffering of the sample to pH 9.5. Good comparisons were observed between obtained and expected concentrations for the three cations with the external validation samples ( n = 17, with RMSE values of 0.18, 0.28 and 0.32 mM for Ca 2+, Mg 2+ and Sr 2+, respectively). The proposed procedure was applied to mineral waters and compared with reference methods.

Microscale mineralization pathways in surface sediments: A chemical sensor study in Lake Baikal

We used an array of ion-selective electrodes (oxygen [O2], hydrogen [H+], carbonate [CO32-], calcium [Ca2+], ammonium [NH4+], and nitrate [NO3-]) with a micromanipulator to study mineralization processes in the surface sediments in Lake Baikal. Concentration profiles at submillimeter resolution were measured in sediment cores from four depths (160-1,400 m) in the South Basin. Oxidation rates of organic carbon (C) estimated from O2 and NO3- profiles measured in March and July 2001 ranged between 2.2 and 4.9 mmol C m-2 d-1. The characteristic shape of the O2 profiles allowed separation of oxidation of organic carbon from reoxidation of reduced compounds at the oxic-anoxic boundary. Of the benthic carbon turnover, 60-75% was metabolized through oxic respiration and 11-28% through anoxic mineralization. The remainder (12-14%) was due to denitrification. Carbon dioxide (CO2) profiles calculated from O2 agreed well with those from pH and CO32-, supporting the concept that oxic respiration was the prevailing mineralization pathway. Alkalinity balance calculated from flux rates of reduced compounds and bicarbonate (HCO3-) calculated from pH and CO32- profiles showed that the sediment was a sink for alkalinity. The flux rates in the range of 0.13-1.0 mmol m-2 d-1 were caused by buffering the hydrogen ions (H+) generated from reoxidation processes of reduced compounds. Potential dark CO2 assimilation by chemoautotrophic bacteria in the sediment was 0.03-0.1 mmol C m-2 d-1. Because of the long O2 exposure time of 25-2,500 yr, however, only 3-14% of the initially settled organic carbon was finally buried in the sediments, forming the paleolimnological record of Lake Baikal.

ISE-based sensor array system for classification of foodstuffs

A system composed of an array of polymeric membrane ion-selective electrodes and a pattern recognition block—a so-called ‘electronic tongue’—was used for the classification of liquid samples: milk, fruit juice and tonic. The task of this system was to automatically recognize a brand of the product. To analyze the measurement set-up responses various non-parametric classifiers such as k-nearest neighbours, a feedforward neural network and a probabilistic neural network were used. In order to enhance the classification ability of the system, standard model solutions of salts were measured (in order to take into account any variation in time of the working parameters of the sensors). This system was capable of recognizing the brand of the products with accuracy ranging from 68% to 100% (in the case of the best classifier).

Analysis of Simulated Martian Regolith Using an Array of Ion Selective Electrodes

AbstractA prototype miniature array of polymer membrane and solid state ion selective electrodes was developed for the purpose of performing an in‐situ analysis of the soluble ionic species in Martian regolith (soil). The array contains a total of 27 electrodes for K+, Na+, Ca2+, Mg2+, NH$\rm{ {_{4}^{+}}}$, Ba2+, NO$\rm{ {_{3}^{-}}}$, Cl−, and Li+, each in triplicate. Barium electrodes were used to indirectly monitor sulfate through precipitation by the addition of barium chloride while the lithium electrodes served as a reference for the array by having a constant lithium concentration as a background for all solutions. The array was tested with several types of simulants, soils, and sawdust from a Mars meteorite, all with varying salt content, meant to approximate the various hypotheses regarding the ionic composition of the Martian soil. The activities of anions and cations determined with the array were compared to ion chromatography data.

Classification of beverages using a reduced sensor array

An electronic tongue based on the sensor array of ion-selective electrodes (ISEs) combined with pattern recognition tools is applied to qualitative analysis of mineral waters and apple juices. The procedure of reducing of total number of the sensors in the array is described. Before and after reducing of number of the sensors, this device is capable of reliable discrimination between different brands of mineral waters and apple juices.Validation of this method is also confirmed using artificial neural networks as a classifier. The ability to recognize different brands of waters and juices after reduction of number of the sensors in the array is comparable to the ability to recognize the same samples by the system before reduction of number of the sensors.This method enables to specify quantitatively the capability of a sensor to discriminate between different classes of samples and can be used for any electronic tongues and noses applications.

Microfabrication and characterization of an ion-selective microelectrode array platform

The design and the fabrication of an ion-selective electrode array platform aimed at in vitro intracellular recording are presented. The platform is composed of two parts: (i) a glass chip with microchannels whose bottom part is patterned with a platinum layer and (ii) a silicon chip in which an array of 50 μm long silicon nitride micropipettes with an inner diameter of 5 μm are structured. The 24 micropipettes are individually filled with a Ca 2+ selective membrane, based on the neutral ionophore ETH 129. Preliminary tests in Ca 2+ buffered solutions have shown a linear range from 10 −6 to 0.1 M Ca 2+ with a sensitivity of 27 mV/decade.

Polymeric membrane ion-selective and cross-sensitive electrode-based electronic tongue for qualitative analysis of beverages.

An electronic tongue based on the sensor array of polymeric membrane ion-selective electrodes combined with pattern recognition tools was applied to qualitative analysis of various brands of orange juice, tonic, and milk. The capability of this device to reliably discriminate between different brands of those products was presented. The tests of the system were performed using products of the same brand, but with different manufacture dates (and thus comparable by the term of taste). The fusion of two types of sensors-classical selective ones and partially selective in one versatile array, and working out the sensor array's response by means of principal component analysis and back propagation neural network methods allowed the discrimination between different brands of various beverages with very high accuracy (90-100%). The real performance of the electronic tongue was evaluated applying testing samples from another manufacture lot, than the samples used in the learning set.

Ion chromatography detector based on solid-state ion-selective electrode array

A variety of neutral carrier type ionophores for monovalent cations were employed to prepare solid-state cation-selective electrodes (SSEs) for use as a detector in single-column ion chromatography (IC). The polyurethane-based pseudoreference electrode made it possible to assemble an array type SSE detector for IC. An SSE-based detector provides not only the overall chromatogram for the separated ion species (monensin methyl ester–nonactin-based membrane), but also the enhanced chromatogram for specified ions of interest (valinomycin as K + and nonactin for NH 4 +). This feature makes it possible to perform highly quantitative analysis with low detection limits even if the separation efficiency of the ion-exchange is not sufficient. Since SSE-based IC detectors are easily miniaturized and replaceable at low cost, they are an ideal component of a portable IC system.

Simultaneous determination of chloride and potassium in carbohydrate electrolyte beverages using an array of ion-selective electrodes controlled by a microcomputer

Este trabalho descreve a elaboracao de um sistema potenciometrico controlado por microcomputador para uso com um conjunto de eletrodos ion-seletivo. O sistema empregou uma placa de interface modelo ACL-8111 da ADLink Technology, para controlar a selecao dos eletrodos ion-seletivo, atraves de uma chave analogica (ADG201), com simultânea aquisicao de dados. Um programa escrito na linguagem VISUAL BASIC 4.0 foi empregado para controle e aquisicao de dado. Este sistema foi avaliado na determinacao simultânea de cloreto e potassio em amostras de bebidas energeticas. Os resultados obtidos na analise destes ions foram concordantes com aqueles dos metodos de referencia levando em consideracao o erro estimado nas determinacoes. This work describes the building of a potentiometric system composed of an array of ion-selective electrodes and controlled by microcomputer. The system employed an acquisition card model ACL8111 from ADLink Technology for the selection control of the ion-selective electrodes through an analogic switch (ADG201) with simultaneous data acquisition. Software implemented on the VISUAL BASIC 4.0 language was employed for electrode control and data acquisition. This system was evaluated in the simultaneous determination of chloride and potassium in samples of carbohydrateelectrolyte beverages. The results obtained for these determinations were in agreement with those obtained by the reference method taking into account the estimated error in the determinations.

Application of neural network calibrations to an halide ISE array

The Neural Network (NN) technique was applied to the calibration of an ion selective electrode (ISE) array comprising a bromide selective electrode, two chloride ISEs and one thiocyanate ISE. The measured samples were synthetic mixture solutions of chlorides and bromides in concentration ranges such that interference occurs. The NN method allowed to perform the calibration without estimating the coefficients of the Nikolskii–Eisenman theoretical relation. Only the determination of bromide was detailed. The results obtained using this method were better than those obtained using linear multivariate calibration methods.

Applications of SPICE for modeling miniaturized biomedical sensor systems.

This paper proposes a model for a miniaturized signal conditioning system for biopotential and ion-selective electrode arrays. The system consists of three main components: sensors, interconnections, and signal conditioning chip. The model for this system is based on SPICE. Transmission-line based equivalent circuits are used to represent the sensors, lumped resistance-capacitance circuits describe the interconnections, and a model for the signal conditioning chip is extracted from its layout. A system for measurements of biopotentials and ionic activities can be miniaturized and optimized for cardiovascular applications based on the development of an integrated SPICE system model of its electrochemical, interconnection, and electronic components.

Halide ion-selective electrode array calibration

The calibration of several ions (Cl −, Br −, F − and OH −) measured with an ion selective electrodes (ISE) array has been carried out in the presence of interferents using an experimental design and multivariate calibration methods. Partial least squares regression and principal component regression do not seem to improve the test set prediction compared to multivariate linear regression. In the case of very slight or no interference on the ISE, each ion can be determined using the corresponding ISE and univariate calibration methods, but the use of multivariate methods does not lead to worse results.

Multivariate standardization techniques on ion-selective sensor arrays

Multivariate standardization techniques [slope/bias (S/B) correction, single wavelength standardization (SWS) and piecewise direct standardization (PDS)] were used to attempt to correct changes over time in multivariate calibration models for potassium and calcium. These models were constructed with ion-selective electrode (ISE) arrays. Multivariate PDS local models which included the correlation between the sensors of the array were better than the other simple techniques. We considered the relationship between the variables (sensors) and, in the PDS treatment, we have indicated their arrangement which is taken from the loadings plot. We used the Kennard–Stone algorithm to select the standardization samples from the original responses of the samples and the partial least squares (PLS) scores of each model. These scores include information about the concentrations. The models and standardizations were validated by predictions on real samples such as natural waters. The best standardization conditions provided unbiased predictions with no loss of precision.

Calibration procedures for a halide Ion-Selective Electrode array

Univariate and multivariate Chemometric methods are compared for the determination of ion concentrations measured with a 6 Ion-Selective Electrode array. Principal component analysis is applied as an exploratory method to observe the extent to which interferences occur. Two univariate calibration methods are compared. It is shown that univariate inverse calibration per- forms at least as well as classical calibration. The use of the former is therefore recommended. The use of partial least squar es, principal component regression and multivariate regression is discussed and the possibilities of the method are described.

Potentiometric Nonlinear Multivariate Calibration with Genetic Algorithm and Simplex Optimization

In this contribution, a genetic algorithm (GA) and a modified Simplex technique are investigated as a means of developing nonlinear multivariate calibration models for an array of ion-selective electrodes. The responses of an array of ammonium-, sodium-, potassium-, and calcium-selective electrodes employed in a flow injection analysis system were modeled over the concentration range of 1 × 10-4 to 1 × 10-2 M using the GA and simplex techniques to optimize the cell potentials, slopes, and selectivity coefficient parameters of the Nikolskii−Eisenman equation for each electrode. Correlations between activities predicted from the calibration model and the actual activities of the solutions presented to the array ranged from 0.98 to 0.88 for the four ions.

Composite Graphite Ion Selective Electrode Array Potentiometry for the Detection of Mercury and Other Relevant Ions in Aquatic Systems

Simultaneous detection of many ions in situ offers advantages in environmental monitoring, including the ability to distinguish a variety of complexed species. We demonstrate here a small mercury ion selective electrode responsive to changing halide concentrations, interpretable as mercury halide speciation in solution. We also present an array of similary constructed small halide-detecting electrodes capable of differentiating halides and sulfide activities. These are a necessary first step toward development of an ion selective microelectrode array for speciation detection of heavy metals in natural waters. Eletrodes consist of a composite of a conductive epoxy and an insoluble salt of the species to be detected. Electrodes studied have an easily renewable surface of approximately 13 mm 2

Ammonium detection using an ion‐selective electrode array in flow‐injection analysis

AbstractAn ammonium detection system using ion‐selective electrodes (ISEs) in flow‐injection analysis (FIA) is described. Because of the low selectivity of the nonactin ammonium selective electrode toward some common ions, notably potassium, different selectivity enhancement techniques have been examined. An array of ISEs selective for ammonium, sodium, potassium, and calcium was used as the FIA detector, and the array response was modeled via the Nikolskii‐Eisenmann equation and the Projection Pursuit regression model. The interfacing of the system to a PC‐386SX computer using an Analog Devices RTI‐815 I/O card is also described. The results show that the use of an array of ISEs under FIA regimes for the detection of ammonium in the activity range 10 to 0.01 mM gives much improved results for the determination of ammonium in aqueous samples than the use of a single ammonium electrode. The performance of the array under steady‐state and FIA conditions is compared.

Nonlinear calibration of ion-selective electrode arrays for flow injection analysis

The application of an ion-selective electrode (ISE) array modeled via the Nlkolskll-Elsenmann equation for the simultaneous determination of sodium, potassium, and calcium in a flow injection analysis system is described. The array consists of three highly selective electrodes and a fourth sensor which responds selectively to the three cations but to differing degrees. The response surface of each electrode within the array is determined using mixed calibration solutions and this response modeled using mixed calibration solutions and this response modeled using simplex optimization

Nonlinear calibration using projection pursuit regression: application to an array of ion-selective electrodes

Abstract : Much effort has been expended in the field of analytical chemistry toward the development of selective sensors. The ultimate goal of this area of research is to build sensors that respond to only one analyte while ignoring all other analytes (interferents) that are present in the samples. Perhaps the most common example of the result of this effort is the development of ion selective electrodes (ISE) for the determination of ion concentrations in solutions. While some ISEs are relatively selective for the desired ions, all suffer from some degree of non-specificity. Unfortunately, in the field of sensor development this is a common occurrence. Another approach to solving the problem of interferents is to use multiple non-selective sensors and employ multivariate mathematics to perform the calibration and prediction. This was the approach taken in two recent papers where arrays of ISEs were used to quantify mixtures of analytes. Analyte quantitation was achieved using either linear or non-linear regression techniques to model the response of the electrodes to the concentration of analytes in mixture samples. In both papers, the sensor responses were assumed to obey the relationship found in the set of extended Nernst equations. A relatively new method of regression analysis called projection pursuit regression is introduced. This method performs linear and nonlinear regression and differs from the more classical methods in that it is able to determine the form of the model as well as the model parameters.