Literature DB >> 33863419

A computational simulation of electromembrane extraction based on Poisson - Nernst - Planck equations.

Roshanak Dolatabadi1, Ali Mohammadi2, Mostafa Baghani3.   

Abstract

Electromembrane extraction (EME) has attracted a great deal of interest in researchers because of its advantages. For analysis, design and optimization purposes, understanding the ion transport mechanisms in the organic supported liquid membrane (SLM) is of prominent importance, where the interplay between the passive diffusion and electric-driven mass transport across SLM affects the mass transfer. In present work, a 2D numerical simulation is developed to examine the mass transfer behavior and the analyte recovery in EME devices. The presented model is capable of describing the effect of different parameters on the recovery of the EME setup. Initial analyte concentration in the sample solution, SLM thickness, applied potential, permittivity, diffusion coefficient, and the reservoir pH within both the sample and acceptor, can be considered as process variables. Predicted results revealed that the most important factors playing key role in EME, are the analyte diffusivity, distribution coefficient of the analyte as well as the level of protonation in both the donor and acceptor solutions. The proposed model is helpful in predicting the mass transfer behavior of the EME process in practical applications.
Copyright © 2021 Elsevier B.V. All rights reserved.

Keywords:  Computational model; Drug concentration; Electromembrane extraction; Finite element method; Nernst-Planck-Poisson equations

Year:  2021        PMID: 33863419     DOI: 10.1016/j.aca.2021.338414

Source DB:  PubMed          Journal:  Anal Chim Acta        ISSN: 0003-2670            Impact factor:   6.558


  1 in total

1.  A simulation study of an electro-membrane extraction for enhancement of the ion transport via tailoring the electrostatic properties.

Authors:  Mahdiyeh Monesi; Mahdi Khatibi; Ahmad Rahbar-Kelishami
Journal:  Sci Rep       Date:  2022-07-16       Impact factor: 4.996

  1 in total

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