Electric field gradient focusing with electro-osmotic flow to reduce analyte dispersion: Concept and numerical...

In proteomics, the need to precisely examine the protein compounds of small samples, requires sensitive analytical methods which can separate and enrich compounds with high precision. Current techniques require a minimal analysis time to obtain satisfactory compound separation where longer analysis time means better separation of compounds. But, molecular diffusion will create broadening of the separated compound bands over time, increasing the peak width, and thus reducing the resolution and the enrichment. Electric field gradient focusing (EFGF) is a separation technique, in which proteins are simultaneously separated and enriched by balancing a gradient electrostatic force with a constant hydrodynamic drag force. Because of this balance, analytes are continuously pushed back to their focusing point, limiting the time-dependent peak broadening due to molecular diffusion. Current EFGF techniques are however still suffering from peak broadening because of flow-profile inhomogeneities. In this paper, we propose to use AC electro-osmotic flow (AC EOF) to create a homogeneous flow in EFGF. The interference between the electric field gradient and the AC EOF was thoroughly analysed and the concept was validated using numerical simulations. The results show that a plug flow is obtained on top of a small, distorted boundary layer. While applying different DC electric fields in the electrolyte, a constant flow velocity can be obtained by including a DC offset to the electrodes generating the AC EOF. The plug flow is then maintained over the whole separation channel length, while an electric field gradient is applied. This way, the flow-induced contribution to peak broadening can be minimized in EFGF devices. By modelling the separation of green fluorescent protein (GFP) and R-Phycoerythrin (R-PE), it was shown that the peak width of separated compounds can be reduced and that the separation resolution can be improved, compared to current EFGF methods.