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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Cathode active materials having improved particle morphologies

Mixed-metal oxides and lithiated mixed-metal oxides are disclosed that involve compounds according to, respectively, NixMnyCozMeαOβ and Li1+γNixMnyCozMeαOβ. In these compounds, Me is selected from B, Na, Mg, Al, Si, K, Ca, Sc, Ti, V, Cr, Fe, Cu, Zn, Ga, Ge, Zr, Nb, Mo, Ru, Ag, In, and combinations thereof; 0≤x≤1; 0≤y≤1; 0≤z<1; x+y+z>0; 0≤α≤0.5; and x+y+α>0. For the mixed-metal oxides, 1≤β≤5. For the lithiated mixed-metal oxides, −0.1≤γ≤1.0 and 1.9≤β≤3. The mixed-metal oxides and the lithiated mixed-metal oxides include particles having an average density greater than or equal to 90% of an ideal crystalline density.

Dai, Hongli↗
DOE OSTI2020

The Effect of Artificial SEIs on Lithium Metal Anode Morphology and Conductivity

Lithium metal has often been termed the “holy grail” of battery technology, due to its high capacity (2,061 mAh/cm3) and low electrochemical potential (-3.04 V vs. SHE). However, lithium’s beneficially low potential also causes it to be highly reactive in the presence of organic solvents, making it difficult to harness the high energy capabilities for rechargeable batteries. Common failure of lithium metal batteries occurs by rapid dendrite growth leading to a hazardous short-circuit. Various methods have been brought forth to better understand the fundamental mechanisms of lithium metal batteries, as well as modifications made to better stabilize lithium metal. A common route of modification is done through interface engineering, with attempts to artificially provide a stable, homogeneous solid-electrolyte interface (SEI). The stability of the SEI has direct effects on the heterogeneity of the electrode surface, which initiates a waterfall effect of for progressive reactions such as dendrite growth, dead lithium accumulation, and electrolyte consumption. This work aims to produce a mixed LiI-LiF artificial SEI to improve ionic conductivity while retaining a high elastic modulus.

25 ENERGY STORAGE↗
DOE OSTI2022

Morphological-informed Thermal Property Prediction in the Engineering Domain

Using first-principle, atomic scale methods to predict thermal carrier (electron and phonon) behavior at nanoscale yields highly resolved thermal properties. Density functional theory simulations can model myriad effects on the transport of phonons and electrons (e.g., phonon and electron coupling, electron correlation, defect presence, carrier scattering), and yield thermal properties for small geometric domains such as thermal and electrical conductivity, or heat capacity. While these thermal properties are accurate in atomistic and nanoscopic systems, they must be scaled up to the microstructural domain to be a useful predictor for experimental basis, as the microscale is where myriad changes and physical phenomenon occur within a material (e.g., grain boundaries, precipitate aggregations, interfaces, defect clusters). My talk will discuss the necessity of thermal property predictions for nuclear fuels applications and more. Additionally I will discuss the necessity in developing multi-physics, multi-scale methods for future applications in predictions for not only nuclear fuels, but other materials applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

MORPHOLOGICAL AND RADIATION DAMAGE INFORMED THERMAL PROPERTY PREDICTION IN SCALED GEOMETRIC DOMAINS

This proposed work has the potential to rewrite the way the nuclear industry investigates new fuel and nuclear material designs. The current rubric of nuclear material design has myriad steps in the process, and while certain physics are modeled accurately, each step must be connected in order to obtain an entire description of the process. At present, neutronic, thermal, microstructural, fission product chemistry and migration, and radiation defect analysis (hereafter referred to together as “combined analysis”) are performed, albeit separately. There is no existing method which combines these physics in an attempt to understand the natural interactions between these phenomena. Consequently, the timeline for design, fabrication, experiment, validation, and licensing can take years. A disruptive approach is required to accelerate the development of new technology. This proposed undertaking creates a validated computational framework, generating a new microscopic-to-macroscopic methodology yielding thermal property predictions for nuclear fuels and materials at an engineering spatial scale.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗