چكيده به لاتين
Microfluidics is one of the leading technologies that has shown a promising growth leveraging particular properties of fluids on micro and nano-scale reducing costs and test time, making it desirable for biomedical and industrial applications. A microfluidic device is a chip made of silicon, glass, or polymer where micron channels are embedded providing favorable flow conditions on a micro-scale. The pivotal role of microfluidics, especially droplet microfluidics in biology and biomedical research is now scientifically recognized. Particularly, droplet microfluidics could be thought of as a research tool in studying biological compatibility, encapsulation of micro-organisms, environmental research, and so on. From a general point of view, this research is composed of five main stages, which due to the plurality of the parameters affecting the process in droplet microfluidics, provide more control over the process. First, after reviewing the literature, simulations under various input flow conditions are done, using Comsol Multiphysics. Then the data obtained at the previous stage are analyzed and engineered, with a plot of future application in machine learning models in mind. In the next step, to estimate the droplet diameter, estimate the droplet production frequency, and determine the droplet production flow regime, two machine learning problems were defined and relevant machine learning models were developed and optimized. As the next step, the simulated microfluidic device was microfabricated and used for evaluating and experimenting with the whole process later on. Monodisperse w/o microfluidic droplets were generated using the experimental setup which weer loaded with Lactobacillus Plantarum probiotics. In the final step, the viability of these encapsulated probiotics was evaluated under simulated acidic stomach environments. In the end, we tried to elaborate on the limitations of this study and make suggestions for further development and improvement of the proposed research methodology.
