Bipyrimidine solubility modeling for applications in NON-Aqueous redox flow batteries

Update Item Information
Publication Type honors there
School or College College of Science
Department Chemistry
Faculty Mentor Matthew S. Sigman
Creator Galinat, Shelby
Title Bipyrimidine solubility modeling for applications in NON-Aqueous redox flow batteries
Date 2022
Description Energy storage research has undergone transformative changes in the past 50 years. The increasingly concerning threat of climate change has emphasized the need for energy storage technology to facilitate renewable energy incorporation into the grid. Without energy storage, the fluctuations of wind and solar energy could cause undesirable grid surges or troughs. Redox flow batteries (RFBs) offer a compelling solution to this problem, providing a large-scale energy storage option that can charge when excess renewable energy is supplied and discharge the stored energy on demand. Non-aqueous RFBs (NRFBs) have increasingly become an area of focus, as the use of organic solvents instead of water allows for significantly higher cell potentials to be reached, maximizing energy density and efficiency1. Dr. Jeremy Griffin and Adam Pancoast, as part of Dr. Matt Sigman's Group at the University of Utah, have recently developed 2,2'-bipyrimidine scaffolds as two electron anolytes for applications in NRFBs with low reduction potentials of E1⁄2 of -1.72 and -1.98 (E/V vs Fc/Fc+) and capacity fade rates on the order of 0.66% per hour2. Though these are promising results, a remaining hurdle for NRFB development is electrolyte solubility, and therefore, battery capacity, since charged electrolytes are typically less soluble in organic solvents. This work will aim to optimize the solubility of the bipyrimidine electrolyte developed by Griffin et al. through testing a library of derivatives of the anolyte for solubility in acetonitrile2. Using computational tools available in the Sigman lab, ground state optimizations of each molecule have been computed and various molecular descriptors (parameters) have been collected. These parameters will be incorporated into a statistical model to identify molecular features responsible for anolyte solubility. Finally, this model will ideally predict a highly soluble novel anolyte.
Type Text
Publisher University of Utah
Language eng
Rights Management (c) Shelby Galinat
Format Medium application/pdf
Permissions Reference URL https://collections.lib.utah.edu/ark:/87278/s6xnenzx
ARK ark:/87278/s6ny8r1a
Setname ir_htoa
ID 2019682
Reference URL https://collections.lib.utah.edu/ark:/87278/s6ny8r1a
Back to Search Results