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dc.contributor.advisorWhyte, Graeme
dc.contributor.authorCrawford, Dean Frazer
dc.date.accessioned2021-10-15T14:33:21Z
dc.date.available2021-10-15T14:33:21Z
dc.date.issued2020-01
dc.identifier.urihttp://hdl.handle.net/10399/4356
dc.description.abstractThe field of microfluidics has been solving problems on the micro-scale for decades, but many in-flow analysis techniques only take single dimensional measurements. In this thesis, multi-dimensional, real-time image analysis has been used to improve and expand upon current microfluidic techniques in several microfluidic areas. Microdroplets within microfluidics are a promising technique for creating microscopic vessels for chemical and biochemical experiments, however accurately controlling such tiny objects can be difficult. The use of real-time image feedback has dramatically improved the monodispersity (coefficient of variation of 0.32%) and accurate loading of the contents of droplets. Beyond this, using these techniques, real-time analysis on the morphology of living cells can be carried out and used to isolate cells of interest. Machine learning algorithms have provided a rapid method to identify the cell populations based on quantitative parameters extracted from transmission or fluorescent images of the cells. By integrating fast piezo-based fluid manipulation, highly selective and accurate cell sorting can be performed within a lab-on-a-chip device for the isolation of subpopulations of cells based on their morphological features. Using this method, K562 cells have been sorted from a mixed population with an efficiency of 91.3% and a purity of 99.4%.en
dc.language.isoenen
dc.publisherHeriot-Watt Universityen
dc.publisherEngineering and Physical Sciencesen
dc.titleReal-time image-based feedback for microfluidic applicationsen
dc.typeThesisen


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