Chemical hard modeling as a new approach for designing optimum chemical sensor arrays based on indicator displacement assay

The superior features of cross-reactive colorimetric sensor arrays have made them very attractive in the past two decades. In the sensor array system, a group of responses provided by semi-selective sensor elements, produce a distinct pattern (signature of each analyte) for the identification and discrimination of several analytes[1]. Colorimetric sensor arrays have been widely explored as a method of detection and discrimination for a range of analytes (i.e. metal ions, amino acids, pesticides) [2]. Indicator displacement assays (IDAs) with simple metal ions or complexes as receptors are typically not very selective, but they are well suited for building colorimetric sensor arrays. In indicator displacement assays (IDAs) a suitable indicator must be able to reversibly bind to the receptor, also the analyte must be able to bind to the receptor[3]. In the presence of analyte, the binding equilibrium between indicator and receptor would be altered because of competitive binding, which would cause changing optical properties of indicator, which are sensitive to the analyte total concentration. Recently, it has been noticed the sensitivity and selectivity of an IDA can be modulated by simply altering ratios and concentrations of a receptor–indicator couple [4]. In this study, the IDA system has been applied as sensor elements, due to knowing the chemical model of this system, it is possible to design optimum sensor array in order to qualitative and quantitative determination of mixtures of analytes. In our research a couple consisting of pyrocatechol violet (PV) and Cu2+ (indicator and receptor respectively) in different concentrations produce the sensor array. Using complexation constants for equilibrium reactions between indicator and receptor, and equilibrium constant for displacement of analytes with indicator, the optimum concentrations of receptor and indicator have been computed by chemical hard modeling, and then the results of hard modeling optimization have been practically used for simultaneous determination of two different amino acids in different samples. Linear and nonlinear calibrations have been used for the prediction of amino acids concentration in different mixtures.