Chemical Separations for Actinide Chemistry and CO2 Capture Applications using Quantum Mechanics and Machine Learning

Nuclear energy is a promising and powerful alternative to providing energy to assist in alleviating current energy demands. With the growing concern of climate change due to an increase in CO₂ atmospheric concentration, many nations have made a commitment to reaching net zero carbon emissions by 2050. Nuclear energy is a key component in providing clean energy for global energy demands with a prominent disadvantage of producing radioactive waste which is a subject of interest to overcome in the nuclear community. However, reducing carbon emissions alone will not reduce the pre-existing CO₂ in the atmosphere from past emissions. Due to this, direct air capture (DAC) technologies provide a pivotal instrument for helping to reduce the carbon concentration in the atmosphere. The utilization of nuclear energy and DAC technologies together will help in reaching net zero emissions as well as reducing the overall carbon concentration in the atmosphere. This dissertation is focused on the modeling and predictions of actinide ligation for the treatment of nuclear waste as well as the separation of actinium from various sources for repurposing in nuclear medicine and on molecular discovery for ligand-based actinide separations for nuclear waste treatment and CO₂ adsorption thermochemical analysis utilizing host-guest chemistry for solid-DAC applications.