Abstract
Droplet-based microfluidics is capable of being a superior platform for biological and biomedical applications due to their higher accuracy, throughput and sensitivity, low reagent consumption and fast reaction time. However, the complex and nonlinear behavior of multiphase microfluidic devices and several parameters affecting droplet size simultaneously necessitates costly design iterations to generate droplets with the radius of interest for a certain application. To address this, we exploit soft computing methods to bridge fuzzy systems and neural networks in order to build an adaptive neural-fuzzy inference system (ANFIS) that predicts the droplet size generated in a flow-focusing microfluidic device based on six major parameters, that includes geometry, flow, and fluid properties. This model shows a significant accuracy with a coefficient of determination of 0.96 when compared to the observed data points. Once the ANFIS model is built and verified, we use it to study the effect of each input parameter on droplet size which is challenging and/or expensive to determine experimentally.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
