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Results for “correlation screening”

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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Glomerular filtration drug injury: In vitro evaluation of functional and morphological podocyte perturbations

Highlights: • The use of nephrotoxic drugs is often unavoidable. • Human podocyte cell line allows the study of the simplified glomerular filtration. • Filtration perturbations are correlated with morphological and phenotypic changes. • Functional and morphological tests are interesting tools for drug screening. The kidney is an organ that plays a major role in the excretion of numerous compounds such as drugs and chemicals. However, a great number of pharmacological molecules are nephrotoxic, affecting the efficiency of the treatment and increasing morbidity or mortality. Focusing on glomerular filtration, we propose in this study a simple and reproducible in vitro human model that is able to bring to light a functional podocyte injury, correlated with morphologic/phenotypic changes after drug exposure. This model was used for the evaluation of paracellular permeability of FITC-dextran molecules as well as FITC-BSA after different treatments. Puromycin aminonucleoside and adriamycin, compounds known to induce proteinuria in vivo and that serve here as positive nephrotoxic drug controls, were able to induce an important increase in fluorescent probe passage through the cell monolayer. Different molecules were then evaluated for their potential effect on podocyte filtration. Our results demonstrated that a drug effect could be time dependent, stable or scalable and relatively specific. Immunofluorescence studies indicated that these functional perturbations were due to cytoskeletal perturbations, monolayer disassembly or could be correlated with a decrease in nephrin expression and/or ZO-1 relocation. Taken together, our results demonstrated that this in vitro human model represents an interesting tool for the screening of the renal toxicity of drugs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Solubility Behavior of CO 2 in Ionic Liquids Based on Ionic Polarity Index Analyses

Ionic liquids (ILs) can serve as effective CO 2 solvents with an appropriate selection of different anions and cations. However, due to the large library of potential IL compositions, rapid screening methods are needed for characterizing and ranking the expected properties. We have recently proposed the ionic polarity index (IPI) parameter, effectively connecting volume-based approaches and electrostatic potential analyses, providing a single metric that can potentially be used to rapidly screen for desirable IL properties. In this work, the corresponding anion and cation IPIs are used to generate correlations with respect to the CO 2 volumetric solubility in ILs. The relationships are generally applicable to groups of ILs within a homologous ion series, and this can be particularly valuable for pre-screening different ion pairings for maximizing gas solvation performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1975

Ionospheric scintillation by a random phase screen Spectral approach

The theory developed by Briggs and Parkin, given in terms of an anisotropic gaussian correlation function, is extended to a spectral description specified as a continuous function of spatial wavenumber with an intrinsic outer scale as would be expected from a turbulent medium. Two spectral forms were selected for comparison: (1) a power-law variation in wavenumber with a constant three-dimensional index equal to 4, and (2) Gaussian spectral variation. The results are applied to the F-region ionosphere with an outer-scale wavenumber of 2 per km (approximately equal to the Fresnel wavenumber) for the power-law variation, and 0.2 per km for the Gaussian spectral variation. The power-law form with a small outer-scale wavenumber is consistent with recent F-region in-situ measurements, whereas the gaussian form is mathematically convenient and, hence, mostly used in the previous developments before the recent in-situ measurements. Some comparison with microwave scintillation in equatorial areas is made.

Rufenach, C. L.↗
DOE OSTI2020

US EPA Superfund Radon Vapor Intrusion Screening Level (RVISL) Electronic Calculator - 20340

Currently, there is no U.S. Environmental Protection Agency (EPA) guidance on correlating soil or groundwater levels of radon with indoor radon concentrations at Superfund sites. EPA is developing the Radon Vapor Intrusion Level Calculator (RVISL) Calculator which is an online tool. The RVISL calculator: (1) lists three radon isotopes (Rn-219, Rn-220 and Rn-222) known to pose a potential cancer risk through the inhalation pathway; (2) provides generally recommended risk and Applicable or Relevant and Appropriate Requirements (ARAR) based preliminary remediation goals (PRG) for groundwater, soil gas (exterior to buildings and sub-slab) and indoor air for default target risk and ARAR based levels and exposure scenarios; and (3) allows calculation of site-specific PRGs based on user-defined target risk and ARAR based levels and exposure scenarios. EPA developed the RVISL calculator to help risk assessors, remedial project managers, and others involved with risk assessment and decision making at radioactively contaminated sites. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Survival of the most transferable at the top of Jacob’s ladder: Defining and testing the ω B97M(2) double hybrid density functional

A meta-generalized gradient approximation, range-separated double hybrid (DH) density functional with VV10 non-local correlation is presented. The final 14-parameter functional form is determined by screening trillions of candidate fits through a combination of best subset selection, forward stepwise selection, and random sample consensus (RANSAC) outlier detection. The MGCDB84 database of 4986 data points is employed in this work, containing a training set of 870 data points, a validation set of 2964 data points, and a test set of 1152 data points. Following an xDH approach, orbitals from the ωB97M-V density functional are used to compute the second-order perturbation theory correction. The resu|ting functional, ωB97M(2) is benchmarked against a variety of leading double hybrid density functionals, including B2PLYP-D3(BJ), B2GPPLYP-D3(BJ), ωB97X-2(TQZ), XYG3, PTPSS-D3(0), XYGJ-OS, DSD-PBEP86-D3(BJ), and DSD-PBEPBE-D3(BJ). Encouragingly, the overall performance of ωB97M(2) on nearly 5000 datq points clearly surpasses that of all of the tested density funstionals. As a Rung 5 density functional, ωB97M(2) completes our family of combinatorially optimized functionals, complementing B97M-V on Rung 3, and ωB97X-V and ωB97M-V on Rung 4. The results suggest that ωB97M(2) has the potential to serve as a powerful predictive tool for accurate and efficient electronic structure calculations of main-group chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Laplacian-level meta-generalized gradient approximation for solid and liquid metals

In this work, we derive and motivate a Laplacian-level, orbital-free meta-generalized-gradient approximation (LL-MGGA) for the exchange-correlation energy, targeting accurate ground-state properties of sp and sd metallic condensed matter, in which the density functional for the exchange-correlation energy is only weakly nonlocal due to perfect long-range screening. Our model for the orbital-free kinetic energy density restores the fourth-order gradient expansion for exchange to the r 2 SCAN meta-GGA [Furness et al., J. Phys. Chem. Lett. 11, 8208 (2020)], yielding a LL-MGGA we call OFR2. OFR2 matches the accuracy of SCAN for prediction of common lattice constants and improves the equilibrium properties of alkali metals, transition metals, and intermetallics that were degraded relative to the PBE GGA values by both SCAN and r 2 SCAN. We compare OFR2 to the r 2 SCAN -L LL-MGGA [Mejia-Rodriguez and Trickey, Phys. Rev. B 102, 121109(R) (2020)] and show that OFR2 tends to outperform r 2 SCAN -L for the equilibrium properties of solids, but r 2 SCAN -L much better describes the atomization energies of molecules than OFR2 does. For best accuracy in molecules and nonmetallic condensed matter, we continue to recommend SCAN and r 2 SCAN. Numerical performance is discussed in detail, and our paper provides an outlook to machine learning.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Evaluation of Contact Separation Force Testing as a Screening Methodology for Electrical Socket Contacts

During system level testing intermittent and permanent open circuit failures of mated, crimp removable, electrical contact pairs were experienced. The root cause of the failures was determined to be low (but not zero) contact forces applied by the socket contact tines against the engaging pin. The low contact force reduces the effectiveness of the wiping action of the socket tines against the pin. The observed failure mode may be produced when insufficient wiping during mate, demate and small relative movement in use allows for the accumulation of debris or insulating films that electrically separate the contact pair. The investigation identified at least three manufacturing process control problems associated with the socket contacts that enabled shipment of contacts susceptible to developing low contact forces: (1) Improper heat treatment of the socket tines resulting in plastic rather than elastic behavior; (2) Overly thinned socket tines at their base resulting in reduced pin retention forces; (3) Insufficient screening tests to identify parts susceptible to the aforementioned failure mechanisms. The results from an extensive screening program of socket contacts utilizing the industry standard contact separation force test procedures are described herein. The investigation shows this method to be capable of identifying initially weak sockets. However, sockets whose contact retention forces may degrade during use may not be screened out by pin retention testing alone. Further investigations are required to correlate low contact retention forces with increased electrical contact resistance in the presence of insulating films that may accumulate in the use environment.

Christopher Green↗
DOE OSTI2020

ASSESSING FUEL PROPERTY EFFECTS ON CAVITATION AND EROSION PROPENSITY USING A COMPUTATIONAL FUEL SCREENING TOOL

To advance compression ignition combustion strategies, researchers have evaluated fuel property effects and their impact on achieving higher efficiencies and lower emissions levels relative to current capabilities. Within the Department of Energy's Co-Optima initiative, there has been a recent focus on understanding the influence of fuel properties on fuel injection performance. To help identify candidate fuels that can meet desired injector performance metrics, a computational fuel screening tool is under development that can link fuel properties with the tendency of a given fuel to cavitate and lead to cavitation-induced erosion. In the initial development of this tool, five liquid fuel properties were selected to represent candidate fuels, namely density, viscosity, vapor pressure, surface tension, and heat of vaporization. A design of experiments methodology was employed to generate a set of pseudo fuel cases, which can represent the main effects and interactions among the fuel properties and be related to cavitation erosion predictions. Large eddy simulations were performed using a mixture modeling approach to predict the cavitation and erosion propensity of these pseudo-fuels in terms of the mean fuel vapor mole fraction and stored impact energy from repeated cloud collapse events. The low order regression models generated from this study revealed the importance of liquid fuel density on cavitation formation, whereas liquid viscosity was found to have a strong negative correlation with erosion severity. The surrogate models were then used in the fuel screening tool to rank the cavitation and erosion tendency of four candidate single-component fuels: methyl decanoate, isooctane, ethanol and n-dodecane. While the fuel screening tool was not able to quantitatively predict the cavitation and erosion response metrics, the tool was able to accurately rank the relative cavitation and erosion propensity of the four fuels. Overall, ethanol and iso-octane were observed to produce the highest levels of cavitation and erosion, respectively.

cavitation-induced erosion↗
DOE OSTI2019

Nanocrystals in Molten Salts and Ionic Liquids: Experimental Observation of Ionic Correlations Extending beyond the Debye Length

The nature of the interface between the solute and the solvent in a colloidal solution has attracted attention for a long time. For example, the surface of colloidal nanocrystals (NCs) is specially created to impart colloidal stability in a variety of polar and nonpolar solvents. This work focuses on a special type of colloids where the solvent is a molten inorganic salt or organic ionic liquid. The stability of such colloids is difficult to rationalize because solvents with high density of mobile charges efficiently screen the electrostatic double-layer repulsion, and purely ionic molten salts represent an extreme case where the Debye length is only similar to 1 angstrom. We introduce a detailed investigation of NC dispersions in molten salts and ionic liquids using small-angle X-ray scattering (SAXS), atomic pair distribution function (PDF) analysis and molecular dynamics (MD) simulations. Our SAXS analysis confirms that a wide variety of NCs (Pt, CdSe/CdS, InP, InAs, ZrO 2 ) can be uniformly dispersed in molten salts like AlCl 3 /NaCl/KCl (AlCl 3 /AlCl 4 - ) and NaSCN/KSCN and in ionic liquids like 1-butyl-3-methylimidazolium halides (BMIM + X - , where X = Cl, Br, I). By using a combination of PDF analysis and molecular modeling, we demonstrate that the NC surface induces a solvent restructuring with electrostatic correlations extending an order of magnitude beyond the Debye screening length. These strong oscillatory ion-ion correlations, which are not accounted by the traditional mechanisms of steric and electrostatic stabilization of colloids, offer additional insight into solvent-solute interactions and enable apparently "impossible" colloidal stabilization in highly ionized media.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Theoretical Phase Diagram for the Room-Temperature Electron–Hole Liquid in Photoexcited Quasi-Two-Dimensional Monolayer MoS 2

Strong correlations between electrons and holes can drive the existence of an electron–hole liquid (EHL) state, typically at high carrier densities and low temperatures. The current emergence of quasi-two-dimensional (2D) monolayer transition metal dichalcogenides (TMDCs) provides ideal systems to explore the EHL state since ineffective screening of the out-of-plane field lines in these quasi-2D systems allows for stronger charge carrier correlations in contrast to conventional 3D bulk semiconductors and enables the existence of the EHL at high temperatures. In this work, we construct the phase diagram for the photoinduced first-order phase transition from a plasma of electron–hole pairs to a correlated EHL state in suspended monolayer MoS 2 . We illustrate that the quasi-2D nature of monolayer TMDCs and the ineffective screening of the out-of-plane field lines allow for this phase transition to occur at and above room temperature, thereby opening avenues for studying many-body phenomena without the constraint of cryogenics.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Interpretable, extensible linear and symbolic regression models for charge density prediction using a hierarchy of many-body correlation descriptors

Here, density functional theory (DFT) is routinely used to make electronic structure predictions for high-throughput screening of materials and molecules for technologically relevant areas, like the identification of better catalysts, electronic materials, and drug discovery. However, the DFT formalism is limited by (a) its poor (quadratic-to-quartic) scaling, and (b) the need to perform repeated eigenvalue computations of the electronic Hamiltonian as part of its self-consistent field (SCF) iteration procedure to obtain the converged ground state electron density, ρ (r). Approaches that directly predict ρ (r) of a structure with high accuracy can accelerate conventional SCF calculations and can also be used in linearly scaling methods such as orbital-free DFT. To this end, we present a procedure to predict the ground state electron density of molecular and periodic three-dimensional systems directly from the atomic structure with a particular emphasis on physical interpretability. In our framework, ρ (r) is modeled using many-body correlation descriptors that accurately capture the effects of local atomic arrangements in the neighborhood of a grid point. Our use of a linear regression scheme to fit to charge density data enables transparent analysis of the relative contributions of various types of local atomic correlations. By systematically including increasingly complex correlations, our model is shown to accurately predict ρ (r) for a variety of chemically and electronically diverse systems — amorphous Ge, Al(001) slab, crystalline Ga 2 O 3 , molecular benzene, and polyethylene. We then demonstrate a symbolic regression-based protocol to construct easily computable, interpretable features from lower-order correlations that significantly improves our electron density predictions with effectively no increase in the computational cost.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Importance of σ Bonding Electrons for the Accurate Description of Electron Correlation in Graphene

Electron correlation in graphene is unique because of the interplay between the Dirac cone dispersion of π electrons and long-range Coulomb interaction. Because of the zero density of states at Fermi level, the random phase approximation predicts no metallic screening at long distance and low energy, so one might expect that graphene should be a poorly screened system. However, empirically graphene is a weakly interacting semimetal, which leads to the question of how electron correlations take place in graphene at different length scales. Here, we address this question by computing the equal time and dynamic structure factor S(q) and S(q,ω) of freestanding graphene using ab initio fixed-node diffusion Monte Carlo simulations and the random phase approximation. We find that the σ electrons contribute strongly to S(q,ω) for relevant experimental values of ω even at distances up to around 80 Å. These findings illustrate how the emergent physics from underlying Coulomb interactions results in the observed weakly correlated semimetal.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Experimental validation and model development for thermal transmittances of porous window screens and horizontal louvred blind systems

Virtually every home in the US has some form of shades, blinds, drapes, or other window attachment, but few have been designed for energy savings. In order to provide a common basis of comparison for thermal performance it is important to have validated simulation tools. This study outlines a review and validation of the ISO 15099 centre-of-glass thermal transmittance correlations for naturally ventilated cavities through measurement and detailed simulations. The focus is on the impacts of room-side ventilated cavities, such as those found with solar screens and horizontal louvred blinds. The thermal transmittance of these systems is measured experimentally, simulated using computational fluid dynamics analysis, and simulated utilizing simplified correlations from ISO 15099. Finally, correlation coefficients are proposed for the ISO 15099 algorithm that reduces the mean error between measured and simulated heat flux for typical solar screens from 16% to 3.5% and from 13% to 1% for horizontal blinds.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Multivariate Machine Learning Models of Nanoscale Porosity from Ultrafast NMR Relaxometry

Abstract Nanoporous materials are of great interest in many applications, such as catalysis, separation, and energy storage. The performance of these materials is closely related to their pore sizes, which are inefficient to determine through the conventional measurement of gas adsorption isotherms. Nuclear magnetic resonance (NMR) relaxometry has emerged as a technique highly sensitive to porosity in such materials. Nonetheless, streamlined methods to estimate pore size from NMR relaxometry remain elusive. Previous attempts have been hindered by inverting a time domain signal to relaxation rate distribution, and dealing with resulting parameters that vary in number, location, and magnitude. Here we invoke well‐established machine learning techniques to directly correlate time domain signals to BET surface areas for a set of metal‐organic frameworks (MOFs) imbibed with solvent at varied concentrations. We employ this series of MOFs to establish a correlation between NMR signal and surface area via partial least squares (PLS), following screening with principal component analysis, and apply the PLS model to predict surface area of various nanoporous materials. This approach offers a high‐throughput, non‐destructive way to assess porosity in c.a. one minute. We anticipate this work will contribute to the development of new materials with optimized pore sizes for various applications.

Fricke, Sophia N.↗
DOE OSTI2024

Multivariate Machine Learning Models of Nanoscale Porosity from Ultrafast NMR Relaxometry

Abstract Nanoporous materials are of great interest in many applications, such as catalysis, separation, and energy storage. The performance of these materials is closely related to their pore sizes, which are inefficient to determine through the conventional measurement of gas adsorption isotherms. Nuclear magnetic resonance (NMR) relaxometry has emerged as a technique highly sensitive to porosity in such materials. Nonetheless, streamlined methods to estimate pore size from NMR relaxometry remain elusive. Previous attempts have been hindered by inverting a time domain signal to relaxation rate distribution, and dealing with resulting parameters that vary in number, location, and magnitude. Here we invoke well‐established machine learning techniques to directly correlate time domain signals to BET surface areas for a set of metal‐organic frameworks (MOFs) imbibed with solvent at varied concentrations. We employ this series of MOFs to establish a correlation between NMR signal and surface area via partial least squares (PLS), following screening with principal component analysis, and apply the PLS model to predict surface area of various nanoporous materials. This approach offers a high‐throughput, non‐destructive way to assess porosity in c.a. one minute. We anticipate this work will contribute to the development of new materials with optimized pore sizes for various applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Second-order spectral line shift comparisons

The second-order spectral line width formulae from the projection operator and kinetic theory methods were recently compared. It was shown that a systematic expansion of the projection operator width expression including initial correlations formally agrees with the second-order kinetic theory result. It is now shown that the second-order dynamic shifts are also formally the same. The static shifts, however, differ due to an ad hoc treatment of electron-electron correlations in the projection operator method. The approximation is necessary in order to screen the radiator-electron interactions. The differences, however, are expected to be small. Finally, the results suggest using the rigorous and more compact second-order width and shift expressions from the kinetic theory method as the starting point for spectral line shape calculations. At line center, however, the projection operator second-order expression for the width and shift simplifies and reduces to the kinetic theory result.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Liquid–Vapor Phase Equilibrium in Molten Aluminum Chloride (AlCl 3 ) Enabled by Machine Learning Interatomic Potentials

Molten salts are promising candidates in numerous clean energy applications, where knowledge of thermophysical properties and vapor pressure across their operating temperature ranges is critical for safe operations. Due to challenges in evaluating these properties using experimental methods, fast and scalable molecular simulations are essential to complement the experimental data. In this study, we developed machine learning interatomic potentials (MLIP) to study the AlCl 3 molten salt across varied thermodynamic conditions (T = 473–613 K and P = 2.7–23.4 bar), which allowed us to predict temperature-surface tension correlations and liquid–vapor phase diagram from direct simulations of two-phase coexistence in this molten salt. Two MLIP architectures, a Kernel-based potential and neural network interatomic potential (NNIP), were considered to benchmark their performance for AlCl 3 molten salt using experimental structure and density values. The NNIP potential employed in two-phase equilibrium simulations yields the critical temperature and critical density of AlCl 3 that are within 10 K (∼3%) and 0.03 g/cm 3 (∼7%) of the reported experimental values. An accurate correlation between temperature and viscosities is obtained as well. In doing so, we report that the inclusion of low-density configurations in their training is critical to more accurately represent the AlCl 3 system across a wide phase-space. The MLIP trained using PBE-D3 functional in the ab initio molecular dynamics (AIMD) simulations (120 atoms) also showed close agreement with experimentally determined molten salt structure comprising Al 2 Cl 6 dimers, as validated using Raman spectra and neutron structure factor. Furthermore, the PBE-D3 as well as its trained MLIP showed better liquid density and temperature correlation for AlCl 3 system when compared to several other density functionals explored in this work. Overall, the demonstrated approach to predict temperature correlations for liquid and vapor densities in this study can be employed to screen nuclear reactors-relevant compositions, helping to mitigate safety concerns.

Ab initio molecular dynamics↗