Structural stability and electrochemical response of 3D printed and reactively sintered Ga-doped Li7La3Zr2O12 electrolytes
Not Available
Dandeneau, Christopher S.↗
Engineering topics
Hitchcock, Dale A.
Publications and source records attributed to Hitchcock, Dale A..
Deep Eutectic Solvents as Candidates for Lithium Isotope Enrichment
Nuclear fusion is a phenomenon that is well known within the nuclear physics community as a viable option for alternative energy as many natural gases and fossil fuels are phased out of commercial use. Deuterium and tritium fusion reactions are currently the leading candidates for nuclear fusion, with a major limiting factor being a means to produce tritium on an industrial scale. Lithium-6 is a well-known isotope that can produce tritium and helium following a fission reaction with a neutron. Unfortunately, the lithium-6 enrichment methods are limited to the COLEX process, which leaves behind an alarming amount of mercury waste as a potential environmental contaminant. Deep eutectic solvents are believed to be a potential alternative to lithium isotope separations due to the ease of generation, in addition to the minimum environmental waste generated when these solvents are employed. Previous studies have suggested that deep eutectic solvents are capable of separating lithium isotopes by utilizing a 2-thenoyltrifluoroacetone and trioctylphosphine oxide system that can biphasically react with a buffered solution containing lithium chloride. This system displays a separation factor of 1.068, which when compared to the 1.054 separation within the COLEX process, makes it a potential candidate for lithium-6/7 separation. Within this study, we investigate this system in comparison to two newly synthesized deep eutectic solvents and find that within these acetylacetone-based systems, little isotopic separation is observed. We investigate these systems both experimentally and computationally, showing the different lithium cation affinities, in addition to proposing how the electron-donating or -withdrawing nature can influence these systems.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Low-temperature formation of Ti2AlN during post-deposition annealing of reactive multilayer systems
M n+1 AXn-phase Ti 2 AlN thin-films were synthesized using reactive sputtering-based methods involving the deposition of single-layer TiAlN, and Ti/AlN and TiN/TiAl multilayers of various modulation periods at ambient temperature and subsequent annealing at elevated temperatures. Ex situ and in situ x-ray diffraction measurements were used to characterize the Ti 2 AlN formation temperature and phase fraction. During annealing, Ti/AlN multilayers yielded Ti 2 AlN at a significantly lower in situ temperature of 650 °C compared to TiN/TiAl multilayers or single-layer TiAlN (750 °C). The results suggest a reactive multilayer mechanism whereby distinct Ti and AlN layers react readily to release exothermic energy resulting in lower phase transition temperatures compared to TiN and TiAl layers or mixed TiAlN. With a modulation period of 5 nm, however, Ti/AlN multilayers yielded Ti 2 AlN at a higher temperature of 750 °C, indicating a disruption of the reactive multilayer mechanism due to a higher fraction of low-enthalpy interfacial TiAlN within the film.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Machine Learning Prediction of Fracture Toughness in Hydrogen-charged Stainless Steels
Austenitic stainless steels are structural materials utilized in tritium gas pressure boundaries since they are resistant to hydrogen isotope embrittlement [1-3]. However, exposure to tritium over long periods of time leads to tritium uptake which decays to result in helium ingrowth. This helium ingrowth results in further embrittlement effects which are synergistic with that from the hydrogen isotope [4]. Therefore, it is important for tritium facilities to understand the material limitations of stainless steel in this environment. The Savannah River National Laboratory (SRNL) has available a large experimental data set of austenitic stainless steels which have been exposed to tritium environments for various lengths of time. With the availability of this data set, machine learning (ML) algorithms provide an opportunity to model the embrittlement of stainless steel due to the algorithm’s ability to identify patterns in data sets that are difficult and costly to identify in other manners [5]. Ultimately, the amount and quality of the available data is one defining force in the ability of a ML model to accurately predict the desired outputs. The models developed herein will illustrate the ability for the various algorithms to predict the change in fracture toughness in stainless steels due to hydrogen-isotope embrittlement.
Hoar, Eric T.↗
Hydrogen isotope separation methods and systems
Methods and systems for the separation of hydrogen isotopes from one another are described. Methods include utilization of a hydrogen isotope selective separation membrane that includes a hydrogen isotope selective layer (e.g., graphene) and a hydrogen ion conductive supporting layer. An electronic driving force encourages passage of isotopes selectively across the membrane at an elevated separation temperature to enrich the product in a selected hydrogen isotope.
07 ISOTOPE AND RADIATION SOURCES↗