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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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Reliability and risk metrics to assess operational adequacy and flexibility of power grids
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Spatiotemporal patterns of phenological metrics and their relationships with environmental drivers in grasslands
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Metrics to describe changes in the power system need for demand response resources
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True cost of electric service: What reliability metrics alone fail to communicate
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The Intermetallic Reactivity Database: Compiling Chemical Pressure and Electronic Metrics toward Materials Design and Discovery
Here, the advent of high-throughput Density Functional Theory (DFT) calculations has supported the creation of large databases containing the quantitative output necessary for constructing theoretical phase diagrams and predicting physical properties. In this Article, we present a complementary resource, the Intermetallic Reactivity Database (IRD), focused on the chemical bonding features of solid-state structures and indicators of potential structural transformations. Each IRD entry augments common features, such as band structures and density of states (DOS) distributions, with chemically motivated information including DFT-Chemical Pressure (CP) schemes and visualizable representations of the atomic charges. Together, these data types enable the rationalization and prediction of potential structural phenomena encountered in intermetallic chemistry, as we illustrate with four examples: the origins of the Y 2 Ni 2 Mg structure in terms of CP features of its parent structures, the anticipation of intergrowth phases from the net atomic CPs collected in Al-containing binary phases, the correlation between trends in the CP schemes of CaCu 5 -type phases and experimentally observed structural variations, and finally, the development of theoretical methodology with the testing of a streamlined method generating DFT-CP schemes. Altogether, these examples highlight how the IRD supports the creation of models of structural chemistry that extend beyond the bounds of its entries.
Island Sliding Barriers: A first-principles metric for determining remote epitaxy viability
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Quantitative Analysis and Prediction of Thermal Runaway Metrics of High-Nickel Oxide Cathodes by Machine Learning Models
The pursuit of higher energy density in lithium-ion batteries has made high-nickel (Ni) layered oxides leading cathode candidates for next-generation electric vehicles. However, their poor thermal stability, particularly at Ni contents ≥ 90%, increases the risk of cathode-initiated thermal runaway. Furthermore, we present a data-driven framework combining linear and nonlinear machine learning models to predict key thermal runaway descriptors from a high-throughput differential scanning calorimetry database. With cathode composition and state of charge (SOC) as input features, the ensemble model accurately predicts peak temperature, heat release, and peak heat flow. SHAP analysis identifies Ni content and SOC as the dominant factors controlling thermal runaway temperature, while SOC primarily governs heat release and peak heat flow. Al, Mg, and Mn improve thermal stability by strengthening metal–oxygen bonding and delaying structural transformation, whereas B mainly reduces heat release through surface passivation. Validation with a new cathode composition confirms accurate prediction of SOC-dependent thermal runaway behavior and critical SOC.
Gas diffusion electrodes, reactor designs and key metrics of low-temperature CO 2 electrolysers
We report that CO 2 emissions can be recycled via low-temperature CO 2 electrolysis to generate products such as carbon monoxide, ethanol, ethylene, acetic acid, formic acid and propanol. In recent years, progress has been made towards an industrially relevant performance by leveraging the development of gas diffusion electrodes (GDEs), which enhance the mass transport of reactant gases (for example, CO 2 ) to the active electrocatalyst. Innovations in GDE design have thus set new benchmarks for CO 2 conversion activity. In this Review, we discuss GDE-based CO 2 electrolysers, in terms of reactor designs, GDE composition and failure modes, to identify the key advances and remaining shortfalls of the technology. This is combined with an overview of the partial current densities, efficiencies and stabilities currently achieved and an outlook on how phenomena such as carbonate formation could influence the future direction of the field. Our aim is to capture insights that can accelerate the development of industrially relevant CO 2 electrolysers.
A new metric to control nucleation and grain size distribution in hybrid organic–inorganic perovskites by tuning the dielectric constant of the antisolvent
In perovskite research, there is a widely exploited but poorly explained phenomenon in which the addition of “antisolvents (ATS)” to precursor solutions results in higher-quality films. We explain the mechanism and driving force underlying an antisolvent-driven solvent extraction process. Density functional theory calculations uncover the defining effects of antisolvent choice on the extent of complexation between a lead salt and a methylammonium cation in solution. We experimentally validate the computational results using ultraviolet-visible spectroscopy and 207 Pb nuclear magnetic spectroscopy of methylammonium lead iodide solutions, containing both a processing solvent and an antisolvent. Furthermore, we uncover, and subsequently identify, the appearance of new species in solution as a result of the addition of the antisolvent. Furthermore, we observe that the choice of antisolvent has a substantial effect on the nature of the complexation of the methylammonium lead iodide (MAPbI 3 ) precursor species, whose origin we explain at an atomic level; specifically, the lower the dielectric of the antisolvent, the stronger the intermolecular binding energy between methylammonium cation (MA + ) cation and PbI 3 - plumbate, independent of the solvent or antisolvent interaction with the lead salt. Thin films were characterized using scanning electron microscopy; images of the films show how the addition of an antisolvent influences and, importantly, can be used to alter thin-film grain size. Grain size and distribution in thin films is reflected by the choice of antisolvent, promoting slower nucleation rates, a lower nucleation density, and hence larger final grain size.
A quantitative metric for organic radical stability and persistence using thermodynamic and kinetic features
Molecular descriptors encoding kinetic and thermodynamic stabilization capture the difference between transient and persistent organic radicals.
Metric geometry tools for automatic structure phase map generation
We present an automated method to extract phase maps from experimental data that is of the functional form ( e.g. : spectroscopy, scattering, diffraction) using the notion of shape distance between two curves represented as one dimensional functions.
From material properties to device metrics: a data-driven guide to battery design
This perspective highlights the material properties, cell design decisions, and manufacturing costs with the biggest influence on the energy, power, cost, lifetime, and safety of a battery.
Capacity-weighted figures-of-merit for battery transport metrics
Fast-charging materials are necessary for a battery-centric future. Ionic and electronic transport crucially determine performance where their capacity-weighted figures-of-merit account for performance across all states-of-charge.
Estimation of stagnation performance metrics in magnetized liner inertial fusion experiments using Bayesian data assimilation
Here we present a new analysis methodology that allows for the self-consistent integration of multiple diagnostics including nuclear measurements, x-ray imaging, and x-ray power detectors to determine the primary stagnation parameters, such as temperature, pressure, stagnation volume, and mix fraction in magnetized liner inertial fusion (MagLIF) experiments. The analysis uses a simplified model of the stagnation plasma in conjunction with a Bayesian inference framework to determine the most probable configuration that describes the experimental observations while simultaneously revealing the principal uncertainties in the analysis. We validate the approach by using a range of tests including analytic and three-dimensional MHD models. An ensemble of MagLIF experiments is analyzed, and the generalized Lawson criterion χ is estimated for all experiments.