A barrier in scaling laboratory processes into automated microfluidic devices has been the transfer of laboratory based assays: Where engineering meets biological protocol. One basic requirement is to reliably and accurately know the distribution and number of biological cells being dispensed. In this study, a novel optical counting technique to efficiently quantify the number of cells flowing into a microtube is presented. REH, B-lymphoid precursor leukemia, are stained with a fluorescent dye and frames of moving cells are recorded using a charge coupled device ͑CCD͒ camera. The basic principle is to calculate the total fluorescence intensity of the image and to divide it by the average intensity of a single cell. This method allows counting the number of cells with an uncertainty Ϯ5%, which compares favorably to the standard biological methodology, based on the manual Trypan Blue assay, which is destructive to the cells and presents an uncertainty in the order of 20%. The use of a microdevice for vertical hydrodynamic focusing, which can reduce the background noise of out of focus cells by concentrating the cells in a thin layer, has further improved the technique. Computational fluid dynamics ͑CFD͒ simulation and confocal laser scanning microscopy images have shown an 82% reduction in the vertical displacement of the cells. For the flow rates imposed during this study, a throughput of 100-200 cells/s is achieved.

Micro Particle Image Velocimetry (μ-PIV) is a non-intrusive technique widely used nowadays to experimentally obtain the velocity field of a micro flow. The main goal of this research was to examine the influence of particle concentration and the number of images acquired, on the accuracy of the μ-PIV velocity measurement. For this reason, a comparison between experimental and analytical values was made. It has been demonstrated that the influence of the seeding concentration on the accuracy of the velocity measurements, into the investigated range, can be considered insignificant. On the other hand, the number of images selected for the cross-correlation is more important for the accuracy of the measurements. By increasing the quantity of images processed it is possible to artificially increase the seeding concentration and reduce the scatter. However, this considerably increases the processing time for the experiment. A trade-off is required between obtaining a highly accurate result without losing precious experimental down time. When the range of the concentration is fixed, it is possible to set the maximum inaccuracy allowance tolerated for the experiment. There is a compromise between a better precision and adequate time to process the data.