Research Bio
Paige is a 5th year grad electrobuff. Paige's research is broadly focused on multi-scale computational modeling of electrochemical systems. She works closely with experimentalists to develop deep insights and rational handles for control and optimization of complex reactions and interfaces. Paige utilizes a variety of techniques such as Grand-Canonical Density Functional Theory (GC-DFT), microkinetic modeling, and continuum transport modeling. Her current projects include studying nitrate reduction, CO2 reduction, and integrated carbon capture and conversion systems. Paige is co-advised by Dr. Charles B. Musgrave.
Paige's favorite thing to do depends on the season! She loves to ski, trail run, and climb. She also enjoys baking bread, playing chess, learning about the plants and animals that live in the mountain west, and reading (her favorite author is Octavia Butler)!
Education
- M.S in Chemical Engineering, University of Colorado Boulder, 2021
- B.S. in Chemical Engineering, University of Utah, 2019
Awards
- Graduate Assistantship in Areas of National Need, U.S. Department of Education, (2019-present)
Selected Publications
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Jinyu Guo, Paige Brimley, Matthew Liu, Elizabeth Corson, Carolina Munoz, Wilson Smith, William Tarpeh, "Mass Transport Modifies the Interfacial Electrolyte to Influence Electrochemical Nitrate Reduction," ChemArxiv. URL.
- Brimley, Paige; Almajed, Hussain; Alsunni, Yousef; Alherz, Abdulaziz*; Bare, Zachary; Smith, Wilson; and Charles Musgrave*, “Electrochemical CO2 Reduction over Metal/Nitrogen-Doped Graphene Single Atom Catalysts Modeled Using Grand-Canonical Density Functional Theory,” ACS Catal., 12, 2022, 10161-10171. DOI: doi.org/10.1021/acscatal.2c01832
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Brimley, P. N., Machalek, D., and Powell, K. M., “Smart Scheduling of a Batch Manufacturer’s Operations by Utilization of a Genetic Algorithm to Minimize Electrical Demand,” Smart and Sustainable Manufacturing Systems. 2019. vol 3, no. 2. 53–67. DOI: https://doi.org/10.1520/SSMS20190018
