Date of Graduation

Spring 2024

Degree

Master of Science in Chemistry

Department

Chemistry and Biochemistry

Committee Chair

Matthew Siebert

Abstract

The world today relies on hydrocarbon combustion for many reasons, including its high energy density that provides ease of transportation. However, hydrocarbons sourced from fossil fuels are not expected to last forever. Biodiesel, a renewable alternative, has many attractive benefits but comes with other downsides. Biodiesel can gel in cold environments and may leave residue in an engine. Pyrolysis of biodiesel has shown promise in addressing these common detriments. Inducing pyrolysis on biodiesel feedstock (commonly soybean oil in the USA) would be an attractive option presuming it continues to produce fossil fuel analogs similar to biodiesel pyrolysis. Herein, Langevin molecular dynamics were employed to simulate the pyrolysis of 6400 soybean oil-based triglycerides (SOBTs). One hundred runs containing 64 triglycerides each were performed at 2000K for 10 picoseconds with 1 femtosecond timesteps. ANI-2x, a machine-learned interatomic potential, was used as the energy calculator. Bond breaking and forming events in each run were observed and analyzed. The results matched expectations from bond dissociation energy (BDE) values for oleic and linoleic acids (those with BDE data available).

Keywords

renewable fuel, pyrolysis, thermal cracking, triglycerides, soybean oil, molecular dynamics, Langevin dynamics, machine learning, interatomic potential, computational chemistry, python

Subject Categories

Computational Chemistry | Organic Chemistry | Physical Chemistry

Copyright

© Tanner Garrett Rust

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