The developing flow field in a parallel plate microchannel, induced by wall motion, has been modeled numerically. This type of flow simulates the physical driving mechanism that exists in electro-osmotically generated flow with large channel diameter-to-Debye length ratios (Z). The physics of the flow field were compared between the moving wall model (MWM) and electro-osmotic flow (EOF) at Reynolds numbers of 1 and 1800, and Z > 2500. Also, Z-values between 50 and 500 were studied to investigate the accuracy of the MWM. Results show that for Z-values greater than 100 the MWM shows good agreement with EOF. The dynamics of the developing flow field for the MWM were explored for channel length-to-hydraulic diameter ratios (aspect ratio) of 5, 10, 20 and 40 at ten Reynolds numbers, Re (based on the wall velocity), below Re < 2000. The results show that far from the inlet the maximum fluid velocity occurs at the walls, as is expected, and the minimum velocity occurs at the channel center. Near the channel inlet, however, the centerline velocity is not a minimum but reaches a local maximum due to a resulting pressure imbalance generated by the wall motion. As the aspect ratio increases, the centerline velocity tends to approach the wall velocity far downstream from the inlet. Increases in the Reynolds number have the opposite effect on the centerline velocity. The hydrodynamic developing region, defined by that section of the channel where the wall shear stress is changing, also depends on the channel aspect ratio and Re, and is greater than the developing region for classical pressure-driven flow of a parallel plate channel. Also, the flow field physics was analyzed for a process called electro-mobility focusing (EMF). EMF is a process that separates and detects species of like charge with the use of electro-phoresis and EOF utilizing a varying voltage gradient. The velocity distribution and the effective diffusion were solved for analytically, for both a linear and non-linear voltage gradient, using the MWM and the creeping flow approximations. The resulting equations aid in optimizing the detection system by forcing the lowest effective diffusion (uniform velocity profile) to the detection location.