Grant: Research Tools and Instruments - Category 1 (<$150,000)

The proposed research aims to develop droplet-based microfluidic technology for high-throughput combinatorial studies which are critical to drug discovery and disease diagnosis applications. The majority of microfluidic chips are based on single phase systems consisting of various miscible aqueous streams, which has several limitations, particularly for large-scale combinatorial studies in chemistry, proteomics, genomics, drug screening and biology because it is plagued with cross-contamination between streams. Such a system also scales up poorly as it requires an approximately linear increase in active components and device footprint for each additional reactor. Two-phase droplet-based microfluidics is a viable alternative where discrete droplets are formed from immiscible fluids like water and oil. Each droplet is essentially an ideal microreactor with a specified volume that compartmentalizes reagents preventing them from diffusing away. Microfluidic droplets can scale up performance effectively as they can be generated at rates between 1-20 kHz over a range of picolitres to nanolitres with a standard deviation of only 1-3 %. To develop such promising technology and devices, characterizing the flow field and droplet motion is essential. Flow field data can be obtained with Micro Particle Image Velocimetry (µ-PIV). A standard µ-PIV is available in the applicant's lab; however, it cannot be used to visualize droplet transport in microfluidic devices because the temporal nature of two phase flow usually limits the number of sequential images that can be obtained for averaging the flow field. Therefore, the current system must be upgraded by adding: i) a programmable motorized stage together with a high-speed, high-resolution camera (>2000 frames per second) for capturing the time dependent flow field at a high frequency; and ii) a phase contrast function for the visualization involving two phases and cells. The high-speed camera allows the droplet volume, speed, frequency of generation, spacing, and number to be analyzed using image analysis algorithms. Without these key parameters, it is extremely difficult, if not possible, to develop droplet-based microfluidic technology.