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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Radiative transition probabilities of neutral and singly ionized rare earth elements (La, Ce, Pr, Nd, Sm, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu) estimated by laser-induced breakdown spectroscopy

Rare earth elements (REEs) are essential to society given their prevalence in many modern technologies. Quantitative elemental analysis of REEs is therefore a critical capability. Calibration free–laser-induced breakdown spectroscopy (CF-LIBS) is a rapidly maturing and promising approach to quantitative elemental analysis with many attractive qualities. The application of CF-LIBS to analyzing samples containing the REEs is hindered by a lack of fundamental data, specifically transition probabilities (TPs). As such, this study seeks to help address this knowledge deficiency by reporting 967 previously unreported TPs for 13 REEs (lanthanum, cerium, praseodymium, neodymium, samarium, gadolinium, terbium, dysprosium, holmium, erbium, thulium, ytterbium, and lutetium). The method developed in this study to estimate the TPs requires small amounts of material (a few nanograms) compared with other approaches (~ milligrams), uses non-specialized equipment, and does not involve complicated sample preparations.

(LIBS)↗
DOE OSTI2021

Phase transition mechanism and bandgap engineering of Sb2S3 at gigapascal pressures

Abstract Earth-abundant antimony trisulfide (Sb 2 S 3 ), or simply antimonite, is a promising material for capturing natural energies like solar power and heat flux. The layered structure, held up by weak van-der Waals forces, induces anisotropic behaviors in carrier transportation and thermal expansion. Here, we used stress as mechanical stimuli to destabilize the layered structure and observed the structural phase transition to a three-dimensional (3D) structure. We combined in situ x-ray diffraction (XRD), Raman spectroscopy, ultraviolet-visible spectroscopy, and first-principles calculations to study the evolution of structure and bandgap width up to 20.1 GPa. The optical band gap energy of Sb 2 S 3 followed a two-step hierarchical sequence at approximately 4 and 11 GPa. We also revealed that the first step of change is mainly caused by the redistribution of band states near the conduction band maximum. The second transition is controlled by an isostructural phase transition, with collapsed layers and the formation of a higher coordinated bulky structure. The band gap reduced from 1.73 eV at ambient to 0.68 eV at 15 GPa, making it a promising thermoelectric material under high pressure.

Cui, Zhongxun (ORCID:0000000275044177)↗
DOE OSTI2020

The chemical and physical properties of tetravalent lanthanides: Pr, Nd, Tb, and Dy

The fundamental redox chemistry and valence electronic structure of the lanthanides in molecular complexes and extended solids continues to be a fertile area of research. The contemporary understanding of the accessible oxidation states of the lanthanide elements and the variability in their electronic structure is the result of several paradigm shifts. While the lanthanide elements have already found widespread use in technical and consumer applications, the continued reevaluation of basic redox properties is a central chemical concern to establish a more complete description of periodic properties. This fundamental understanding of valence electronic structure as it is derived from oxidation state and coordination environment is essential for the continued development of lanthanides in quantum information science and quantum materials research. This review presents the chemical and physical properties of tetravalent lanthanide ions in extended solids and molecules with a focus on the elements apart from cerium: praseodymium, neodymium, terbium, and dysprosium.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Four-electron oxidation and one-electron reduction of the bis(terphenylthiolate) U( II ) complex, U(SAr iPr6 ) 2 [Ar iPr6 = C 6 H 3 -2,6-(C 6 H 2 -2,4,6- i Pr 3 ) 2 ]

Here, the utility of the sterically bulky terphenylthiolate ligand, (SAr iPr6 ) 1− in expanding uranium reductive chemistry has been explored. Reduction of U(SAr iPr6 ) 2 I forms the U(II) complex, U(SAr iPr6 ) 2 , in which the metal is protected by the flanking arene rings of the ligand, but they move out of the way to accommodate the four electron reduction of PhN=NPh to form the U(VI) bis(imido) product U(SAr iPr6 ) 2 (=NPh) 2 (THF) 2 . Here, the KC 8 reduction of U(SAr iPr6 ) 2 generates a more reduced complex, KU(μ-SAr iPr6 ) 2 , initially identified by a −2.55 V vs. Fc + /Fc electrochemical reduction event in THF.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Comparative many-body study of Pr 4 Ni 3 O 8 and NdNiO 2

We study the many-body electronic structure of the stoichiometric and electron-doped trilayer nickelate Pr4Ni 3 O 8 in comparison to that of the stoichiometric and hole-doped infinite layer nickelate NdNiO 2 within the framework of density functional plus dynamical mean field theory, noting that Pr4Ni 3 O 8 has the same nominal carrier concentration as NdNiO2 doped to a level of 1/3 holes/Ni. In this work, we find that the correlated Ni-3$\textit{d}$ shells of both of these low valence nickelates have similar many-body configurations with correlations dominated by the $d_{x^2 - y^2}$ orbital. Additionally, when compared at the same nominal carrier concentration, the materials exhibit similar many-body electronic structures, self energies, and correlation strengths, but differ in Fermiology. Compared to cuprates, these materials are closer to the Mott-Hubbard regime due to their larger charge transfer energies. Moreover, doping involves the charge reservoir provided by the rare earth 5$\textit{d}$ electrons, as opposed to cuprates where it is realized via the oxygen 2$\textit{p}$ electrons.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

PV Generation and Load Forecasting for Adjuntas PR Community Microgrids

Existing frameworks to forecast time-series photovoltaic (PV) output power and consumer load for microgrid operations and controls assume a near-continuous availability of real-time input features from the field assets such as PV inverters, energy meters, and weather station. These incoming data points are used to periodically retrain models and update forecast snapshots over a moving horizon window, be it one hour-ahead, one-day ahead, or one-week ahead. However, such frameworks are not resilient to disruptions in data availability caused by losses in communications between the field sensors and data loggers. Hence, there is a need for programs that assume no availability of real-time microgrid asset data and still make reliable forecasts that can be used for decision-making. Such programs would be apt to function in extreme weather events such as hurricanes and would use lightweight recursive time-series models to independently forecast solar irradiance and ambient temperature, then compute PV power from those forecasts, as well as independently forecast consumer load. The codebase performs forecasting for the scenario of when the microgrid does not have a reliable access to forecasts or real-time observations of solar irradiance (I) and ambient temperature (AT) and load (Load) to be able to adequately forecast, in real-time, the PV power production or a business' load. In this case, using historical values of PV power and load, a univariate forecasting of generation and consumption are respectively made. The use-case in particular has two sub-scenarios: one, a normal 7-day ahead forecast where the unavailability of real-time data is assumed due to infrastructure issues such as loss of communication or sensor maintenance or service downtimes. Whereas a hurricane-caused unavailability of real-time data requires a second model trained specifically on historical hurricane days to be able to capture the extreme day behavior of generation in particular, and load if applicable. A gradient boosted regression tree comprises an ensemble of additive models that map between the input of historical values (be it irradiance, temperature, or load) and their corresponding output forecasts of a given horizon such that the individual learner predictions are summed up over the total number of such learners in the ensemble to produce an aggregate forecast. A weighting mechanism is applied to the training data in each iteration, where actual and forecast values are compared to penalize incorrect forecasts by increasing the weight and reducing it to reward correct forecasts. The code's benefits are that it: (a) accounts for a contingency where communication loss renders newly measured real-time data unavailable for model tuning and snapshot updates; (b) presents blind forecasting that recursively determines the next time-step value in a horizon using the forecast of the same attribute from a prior step; and (c) employs lightweight models that, once trained, can reliably generalize for different horizons, which make them suitable for enhancing the resilience of field microgrids prone to extreme events that encounter disruptions to data availability.

Sundararajan, Aditya [Oak Ridge National Laborator↗
DOE OSTI2023

Wave Measurements taken NW of Culebra Is., PR, 2023

Wave and sea surface temperature measurements collected by a Sofar Spotter buoy in 2023. The buoy was deployed on July 27, 2023 at 11:30 UTC northwest of Culebra Island, Puerto Rico, (18.3878 N, 65.3899 W) and recovered on Nov 5, 2023 at 12:45 UTC. Data are saved here in netCDF format, organized by month, and include directional wave statistics, GPS, and SST measurements at 30-minute intervals. Figures produced from these data are provided here as well. They include timeseries of wave height/period/direction and SST, GPS location, wave roses, and directional spectra. Additionally, raw CSV files from the Spotter's memory card can also be found below. NetCDF files can be read in python using the netCDF4 or Xarray packages, or through MATLAB using the "ncread()" command.

16 TIDAL AND WAVE POWER↗