Simultaneous Determination of Linear and Nonlinear Electrophoretic Mobilities of Cells and Microparticles

Direct-current insulator-based electrokinetics (DC-iEK) is a branch of microfluidics that has demonstrated to be an attractive and efficient technique for manipulating micro- and nano- particles, including microorganisms. A unique feature of DC-iEK devices is that nonlinear EK effects are enhanced by the presence of regions of higher field intensity between the insulating structures. Accurate computational models, describing particle and cell behavior, are crucial to optimize the design and improve the performance of DC-iEK devices. The electrokinetic equilibrium condition (E-EEC) is a recently introduced fundamental concept that has radically shifted the perspective behind the analysis of particle manipulation in these microfluidic devices. The E-EEC takes into consideration previously neglected nonlinear effects on particle migration and indicates that these effects are central to control particle motion in DC-iEK devices. In this study, we present a simultaneous experimental characterization of linear and nonlinear electrokinetic (EK) parameters, that is, the electrophoretic mobility (mu((1))(EP)), the particle zeta potential (zeta(P)), the E-EEC, and the electrophoretic mobility of the second kind (mu((3))(EP)), for four types of polystyrene microparticles and four cell strains. For this, we studied the electromigration of polystyrene microparticles ranging in size from 2 to 6.8 mu m, three bacteria strains (B. cereus, E. coli, and S. enterica) and a yeast cell (S. cerevisiae), ranging in size from 1 to 6.3 mu m, in a polydimethylsiloxane (PDMS) microfluidic channel with a rectangular cross-section. The results illustrated that electrokinetic particle trapping can occur by linear and nonlinear electrophoresis and electroosmosis reaching an equilibrium, without the presence of insulating posts. The experimentally measured parameters reported herein will allow optimizing the design of future DC-iEK devices for a wide range of applications (e.g., to separate multiple kinds of particles and microorganisms) and for developing computational models that better represent reality.