Estimation and Optimization of Potentiometric Sensor Response Parameters from Surfactant Titration Data Using Microsoft Excel <I>Solver</I> and <I>Mathematica</I>

Abstract

Theoretical models for potentiometric titration curves of some ionic and nonionic surfactants are proposed in this paper. The experimental data were compared with appropriate theoretical curves in which the sensor response parameters and analyte properties (sensor slope, constant potential term, solubility product constant, analyte concentration) were estimated and optimized with Solver (Excel) and Wolfram Research Mathematica using least-squares criterion to fit theoretical curves to an experimental data set. The initial parameter settings were defined, and no constraints on the variables were applied. The macro SolvStat provided the regression statistics of the Solver, and these statistics were confirmed by the NonlinearModelFit macro of Wolfram Research Mathematica by calculation of the standard deviations of the parameters, correlation coefficients and standard errors of the y estimate SE(y. The two software tools gave almost the same estimates of the parameter models and the corresponding statistics. The theoretical models fit satisfactorily to the experimental values for all of the investigated surfactants.

Two Novelties in Ion‐pair Formation‐based Potentiometric Titrations of Anionic Surfactants

“…In our opinion, such evaluation is quite important. Although this titration method was introduced at the end of the last century, it is still required (as shown by numerous improvements published recently .…”