Numerical Modeling of Solid-Phase Microextraction: Binding Matrix Effect on Equilibrium Time

• Published in: Analytical chemistry (Washington) Vol. 87; no. 19; pp. 9846 - 9854
• Main Authors: Alam, Md. Nazmul, Ricardez-Sandoval, Luis, Pawliszyn, Janusz
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 06.10.2015
• Subjects: Asymptotic methods; Asymptotic properties; Binding; Binding sites; Computer simulation; Equilibrium; Extraction; Kinetics; Mathematical analysis; Mathematical models; Optimization; Sampling; Simulation
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/acs.analchem.5b02239

Solid-phase microextraction (SPME) is a well-known sampling and sample preparation technique used for a wide variety of analytical applications. As there are various complex processes taking place at the time of extraction that influence the parameters of optimum extraction, a mathematical model and computational simulation describing the SPME process is required for experimentalists to understand and implement the technique without performing multiple costly and time-consuming experiments in the laboratory. In this study, a mechanistic mathematical model for the processes occurring in SPME extraction of analyte(s) from an aqueous sample medium is presented. The proposed mechanistic model was validated with previously reported experimental data from three different sources. Several key factors that affect the extraction kinetics, such as sample agitation, fiber coating thickness, and presence of a binding matrix component, are discussed. More interestingly, for the first time, shorter or longer equilibrium times in the presence of a binding matrix component were explained with the help of an asymptotic analysis. Parameters that contribute to the variation of the equilibrium times are discussed, with the assumption that one binding matrix component is present in a static sample. Numerical simulation results show that the proposed model captures the phenomena occurring in SPME, leading to a clearer understanding of this process. Therefore, the currently presented model can be used to identify optimum experimental parameters without the need to perform a large number of experiments in the laboratory.