PhD Proposal: Revati Kadolkar

High performance liquid chromatography (HPLC) is a well-established method for the analysis and characterization of biological molecules, but there is an increasing global demand for its application at small-scale and in distributed biomanufacturing. This project aims to address that need by creating innovative analytical chromatography-based microfluidic sensors and by developing computational methods to enhance their functionality and to better understand their performance. The interdisciplinary nature of this project spans across sensor fabrication, electrochemical and optical detection, microfluidics, liquid chromatography and computational methods. Currently, chromatography-based sensors of the type considered here are not well understood or widely available, making this effort novel in the field. The sensors developed in this project are low-cost, rapid, easy to use on-site and thus ideal for point-of-care applications. This project encompasses three main objectives: (i) application of data-driven statistical modeling for developing a microfluidic sensor based on affinity chromatography with on-column detection by electrochemical methods, (ii) application of multiphysics modeling for understanding the physicochemical processes in microfluidic size exclusion chromatography, and (iii) application of hybrid mechanistic modeling using a symbolic-numeric programming environment to develop, among other objectives, a signal processing approach to understand and control band broadening in small-scale linear gradient elution chromatography. 

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