Individual droplets can be isolated within microfluidic systems by use of an immiscible carrier layer. This type of two phase systems, often termed "digital microfluidics", find wide ranging applications in chemical synthesis and analysis. To conduct on-chip biochemical analysis, a key step is to be able to merge droplets selectively in order to initiate the required reactions. In this paper, a novel microfluidic chip integrating interdigital transducers is designed to merge multiple droplets on-demand. The approach uses surface acoustic wave induced acoustic radiation forces to immobilize droplets as they pass from a channel into a small expansion chamber, there they can be held until successive droplets arrive. Hence, no requirement is placed on the initial spacing between droplets. When the merged volume reaches a critical size, drag forces exerted by the flowing oil phase act to overcome the retaining acoustic radiation forces, causing the merged volume to exit the chamber. This will occur after a predetermined number of droplets have merged depending on the initial droplet size and selected actuation power.

the recent boom in single-cell omics has brought researchers one step closer to understanding the biological mechanisms associated with cell heterogeneity. Rare cells that have historically been obscured by bulk measurement techniques are being studied by single cell analysis and providing valuable insight into cell function. To support this progress, novel upstream capabilities are required for single cell preparation for analysis. Presented here is a droplet microfluidic, image-based single-cell sorting technique that is flexible and programmable. The automated system performs real-time dualcamera imaging (brightfield & fluorescent), processing, decision making and sorting verification. To demonstrate capabilities, the system was used to overcome the Poisson loading problem by sorting for droplets containing a single red blood cell with 85% purity. Furthermore, fluorescent imaging and machine learning was used to load single K562 cells amongst clusters based on their instantaneous size and circularity. the presented system aspires to replace manual cell handling techniques by translating expert knowledge into cell sorting automation via machine learning algorithms. This powerful technique finds application in the enrichment of single cells based on their micrographs for further downstream processing and analysis. Cell populations are remarkably heterogeneous. Even considering a similarly tasked sub-population of cells; there exists significant variability in their morphological properties and more so in their active genes and regulations 1,2. The recent genome-scale downstream capabilities (DNA 3 , RNA 4,5) at the single-cell resolution provide unprecedented insight into cellular heterogeneity, especially in cases where rare cell populations are masked by bulk measurements. This calls for alternative upstream techniques of selecting single cells for analysis. Compared to conventional manual cell handling techniques, microfluidics offers significant reagent volume reduction and ease of automation with enhanced repeatability and detection accuracy on low-cost, disposable chips 6. In this work, a droplet microfluidic system is developed to isolate single cells by image-based sorting. The platform aims to provide researchers with the methods to capture single cells of interest and study some of the deeper mechanisms involved in cell regulation and function. There already exists well-established methods for the detection and isolation of single cells for analysis. Some of these techniques are label-free 7-12 while others rely on fluorescent 13-16 or magnetic labelling. Labelling might not be possible for some systems due to lack of biomarkers or potential cytotoxicity of the labels (e.g. DNA intercalators) 17. Additionally, labels might be undesired if they interfere with the study such as in stem cell differentiation 18. Well known techniques for single cell preparation are Fluorescence Activated Cell Sorting 19,20 , Magnetic Assisted Cell Sorting 21,22 , Fluorescence Activated Droplet Sorting 23 , Laser Capture Mi...

Digital microfluidic systems, in which isolated droplets are dispersed in a carrier medium, offer a method to study biological assays and chemical reactions highly efficiently. However, it's challenging to manipulate these droplets in closed microchannel devices. Here, we present a method to selectively steer plugs (droplets with diameters larger than the channel's width) at a specially designed Y-junction within a microfluidic chip. The method makes use of surface acoustic waves (SAWs) impinging on a multiphase interface in which an acoustic contrast is present. As a result, the liquid-liquid interface is subjected to acoustic radiation forces. These forces are exploited to steer plugs into selected branches of the Y-junction. Furthermore, the input power can be finely tuned to split a plug into two uneven plugs. The steering of plugs as a whole, based on plug volume and velocity is thoroughly characterized. The results indicate that there is a threshold plug volume after which the steering requires elevated electrical energy input. This plug steering method can easily be integrated to existing lab-on-a-chip devices and it offers a robust and active plug manipulation technique in closed microchannels.