Date of Award


Document Type

Honors Thesis



First Advisor

Dr. Pere Miro

Second Advisor

Dr. Bess Vlaisavljevich

Third Advisor

Dr. James Hoefelmeyer


nuclear fuel cycle, uranyl-peroxide, nanocapsules, density functional theory, speciation, machine learning

Subject Categories



Uranyl-peroxide nanocapsules are a unique family of self-assembled actinide species. Uranyl ions rapidly self-assemble in basic peroxidic media through a myriad of reactions to coalesce into a single nanocapsule that includes both peroxide and hydroxide bridging groups between the uranyl moieties. A wide variety of capsules can be formed, and it has been proposed that square and pentagonal building blocks assemble prior to nanocapsule formation. We have studied the speciation of the pentagonal 2) uranyl-peroxide nanocapsule building blocks using density functional theory calculations. We predicted the most favorable speciation pathways for the self-assembly of the building blocks prior to cluster formation including the effect of pH, temperature, and alkali counterions. In addition, we also mapped the potential energy surface by scanning the molecular normal modes and created a large database containing uranyl monomers We then used the atomistic machine learning package to train a neural network potential in order to create a cheap structure-energy connection that could be used to predict quantum mechanics energetics of larger uranyl-peroxide systems for a fraction of the computational cost.

Included in

Chemistry Commons



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