Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “density correlation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Accurate simulation of surfaces and interfaces of ten FCC metals and steel using Lennard–Jones potentials

Abstract The earlier integration of validated Lennard–Jones (LJ) potentials for 8 fcc metals into materials and biomolecular force fields has advanced multiple research fields, for example, metal–electrolyte interfaces, recognition of biomolecules, colloidal assembly of metal nanostructures, alloys, and catalysis. Here we introduce 12-6 and 9-6 LJ parameters for classical all-atom simulations of 10 further fcc metals (Ac, Ca (α), Ce (γ), Es (β), Fe (γ), Ir, Rh, Sr (α), Th (α), Yb (β)) and stainless steel. The parameters reproduce lattice constants, surface energies, water interfacial energies, and interactions with (bio)organic molecules in 0.1 to 5% agreement with experiment, as well as qualitative mechanical properties under standard conditions. Deviations are reduced up to a factor of one hundred in comparison to earlier Lennard–Jones parameters, embedded atom models, and density functional theory. We also explain a quantitative correlation between atomization energies from experiments and surface energies that supports parameter development. The models are computationally very efficient and applicable to an exponential space of alloys. Compatibility with a wide range of force fields such as the Interface force field (IFF), AMBER, CHARMM, COMPASS, CVFF, DREIDING, OPLS-AA, and PCFF enables reliable simulations of nanostructures up to millions of atoms and microsecond time scales. User-friendly model building and input generation are available in the CHARMM-GUI Nanomaterial Modeler. As a limitation, deviations in mechanical properties vary and are comparable to DFT methods. We discuss the incorporation of reactivity and features of the electronic structure to expand the range of applications and further increase the accuracy.

Kanhaiya, Krishan (ORCID:0000000236222655)↗
DOE OSTI2025

Radiation damages the silicates present in polyphasic mineral aggregates causing concrete’s degradation

While many U.S. nuclear power plants have submitted Subsequent License Renewal Application to operate beyond 60 years, others are already considering Operations Beyond Eighty years. In such cases, concrete biological shields are exposed to neutron and gamma radiation exceeding prescribed thresholds. Radiation-induced volumetric expansion (RIVE), extensively studied in single crystals, may also contribute to the degradation of polycrystalline aggregates. Since minerals differ in atomic structure and chemistry, radiation can affect them in distinct ways. This study examines quartzite, marble, and limestone to evaluate how irradiation affects their physical attributes and chemical reactivity. Results show crystalline silicates experience significant RIVE damage and enhanced reactivity in alkaline solutions compared to non-irradiated phases. Enhanced intra- and inter-granular dissolution could compromise aggregate integrity. An empirical correlation links silicate dissolution rate to atomic constraints (density, rigidity) and radiation dose, providing a predictive framework for estimating changes in silicate aggregate properties within radiation-exposed concrete.

Bouissonnié, Arnaud [Univ. of California, Los Ange↗
DOE OSTI2020

Controlling protein adsorption modes electrostatically

Protein adsorption on surfaces is ubiquitous in biology and in biotechnology. There are various forces required for controlling protein adsorption. Here, we introduce an explicit ion coarse-grained molecular dynamics simulation approach for studying the effects of electrostatics on protein adsorption, and 2D protein assembly on charged surfaces. Our model accounts for the spatial distribution of protein charges. We use catalase as our model protein. We find that the preferential adsorption mode of proteins at low protein concentration on a charged surface is “standing up”. When the protein concentration in a solution increases to reach a critical density on the surface, the adsorption mode switches from “standing up” to a mixed state “flat on” and “standing up”, which increases the lateral correlations among the adsorbed proteins. As such, the changes in the adsorption mode arise from the protein adsorption that cancel the surface charge and the protein–protein repulsion. This correlated surface structure melts as the salt concentration increases because the charged surface is cancelled by the salt ions and the proteins de-adsorb. For the case of strongly charged surfaces the “standing up” conformation remains more favorable even at high protein adsorption at low salt concentrations since in that conformation the surface charge is cancelled more effectively, generating an even more laterally correlated structure. We elucidate the effects of parameters such as surface charge density, salt concentration, and protein charges on the different adsorption modes and the structure and organization of proteins on the charged surfaces. Finally, this study provides a guide for controlling protein assembly on surfaces.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Excited states in variational Monte Carlo using a penalty method

Here, in this article, the authors present a technique using variational Monte Carlo to solve for excited states of electronic systems. This technique is based on enforcing orthogonality to lower energy states, which results in a simple variational principle for the excited states. Energy optimization is then used to solve for the excited states. This technique is applied to the well-characterized benzene molecule, in which ~10 000 parameters are optimized for the first 12 excited states. Agreement within ~0.2 eV is obtained with higher scaling coupled cluster methods; small disagreements with experiment are likely due to vibrational effects.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Infrared spectroscopy of RG–Co + (H 2 O) complexes (RG = Ar, Ne, He): The role of rare gas “tag” atoms

RG n –Co + (H 2 O) cation complexes (RG = Ar, Ne, He) are generated in a supersonic expansion by pulsed laser vaporization. Here, complexes are mass-selected using a time-of-flight spectrometer and studied with infrared laser photodissociation spectroscopy, measuring the respective mass channels corresponding to the elimination of the rare gas “tag” atom. Spectral patterns and theory indicate that the structures of the ions with a single rare gas atom have this bound to the cobalt cation opposite the water moiety in a near-C 2v arrangement. The O–H stretch vibrations of the complex are shifted compared to those of water because of the metal cation charge-transfer interaction; these frequencies also vary systematically with the rare gas atom attached. The efficiencies of photodissociation also vary with the rare gas atoms because of their widely different binding energies to the cobalt cation. The spectrum of the argon complex could only be measured when at least three argon atoms were attached. In the case of the helium complex, the low binding energy allows the spectra to be measured for the low-frequency H–O–H scissors bending mode and for the O–D stretches of the deuterated analog. The partially resolved rotational structure for the antisymmetric O–H and O–D stretches reveals the temperature of these complexes (6 K) and establishes the electronic ground state. The helium complex has the same 3 B 1 ground state as the tag-free complex studied previously by Metz and co-workers (2013), but the A rotational constant is contaminated by vibrational averaging from the bending motion of the helium.

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

Structures of glasses created by multiple kinetic arrests

X-ray scattering has been used to characterize glassy itraconazole (ITZ) prepared by cooling at different rates. Faster cooling produces ITZ glasses with lower (or zero) smectic order with more sinusoidal density modulation, larger molecular spacing, and shorter lateral correlation between the rod-like molecules. We find that each glass is characterized by not one, but two fictive temperatures T f (the temperature at which a chosen order parameter is frozen in the equilibrium liquid). The higher T f is associated with the regularity of smectic layers and lateral packing, while the lower T f with the molecular spacings between and within smectic layers. This indicates that different structural features are frozen on different timescales. The two timescales for ITZ correspond to its two relaxation modes observed by dielectric spectroscopy: the slower δ mode (end-over-end rotation) is associated with the freezing of the regularity of molecular packing and the faster α mode (rotation about the long axis) with the freezing of the spacing between molecules. Finally, our finding suggests a way to selectively control the structural features of glasses.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

High-throughput ab initio reaction mechanism exploration in the cloud with automated multi-reference validation

Quantum chemical calculations on atomistic systems have evolved into a standard approach to studying molecular matter. These calculations often involve a significant amount of manual input and expertise, although most of this effort could be automated, which would alleviate the need for expertise in software and hardware accessibility. Here, we present the AutoRXN workflow, an automated workflow for exploratory high-throughput electronic structure calculations of molecular systems, in which (i) density functional theory methods are exploited to deliver minimum and transition-state structures and corresponding energies and properties, (ii) coupled cluster calculations are then launched for optimized structures to provide more accurate energy and property estimates, and (iii) multi-reference diagnostics are evaluated to back check the coupled cluster results and subject them to automated multi-configurational calculations for potential multi-configurational cases. All calculations are carried out in a cloud environment and support massive computational campaigns. Key features of all components of the AutoRXN workflow are autonomy, stability, and minimum operator interference. We highlight the AutoRXN workflow with the example of an autonomous reaction mechanism exploration of the mode of action of a homogeneous catalyst for the asymmetric reduction of ketones.

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

The surface chemistry of colloidal lead halide perovskite nanowires

This study explored the interplay between the ligand–surface chemistry of colloidal CsPbBr 3 nanowires (NWs) and their optical properties. The ligand equilibrium was probed using nuclear magnetic resonance spectroscopy, and by perturbing the equilibrium via dilution, the gradual removal of ligands from the CsPbBr 3 surface was observed. This removal was correlated with an increase in the surface defect density, as suggested by a broadening of the photoluminescence (PL) spectrum, a decrease in the PL quantum yield (PLQY), and quenching of the PL decay. These results highlight similar surface binding between the traditional CsPbBr 3 quantum dots and our NWs, thereby expanding the scope of well-established ligand chemistry to a relatively unexplored nanocrystal morphology. In conclusion, by controlling the dilution factor, it was revealed that CsPbBr 3 NWs achieve a PLQY of 72% ± 2% and a relatively long average PL lifetime of 400 ± 10 ns, without relying on additional surface passivation techniques, such as ligand exchange.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Variation in supercurrent connectivity and vortex pinning of state-of-the-art pulsed laser deposited RE Ba 2 Cu 3 O 7- x coated conductors

REBa 2 Cu 3 O 7-x (REBCO, RE: rare earth) coated conductors (CCs) suffer from great critical current I c differences between different manufacturers, I c variations within individual manufacturers, and often significant lengthwise fluctuations. The understanding of such variations is complicated by the lack of a direct correlation between I c and the critical current density J c . In fact, although J c is the fundamental property determined by the local vortex pinning landscape, I c is often limited by variable current blocking mechanisms. An important practical complexity is commercial practices of performing J c and I c evaluations based on I c at 77 K and self-field (sf), where connectivity variation dominates over vortex pinning variations. However, at higher fields and lower temperatures, vortex pinning becomes more complex and highly variable, making predictions of I c and J c at arbitrary temperature T, magnetic field H, and field orientation θ, quite uncertain. To address some aspects of this problem, we conducted detailed spool-to-spool performance characterization on recently manufactured REBCO CCs. Despite J c (77 K, sf) varies by only ∼11%, J c (77 K, 1 T) of its minimum and maximum (for H//ab-plane) show variations of ∼21% and ∼32%, respectively. This emphasizes the shortcoming in using J c (77 K, sf) as parameter for evaluating even the low field performance. An even more remarkable spool-to-spool J c variation of ∼68% was observed at 20 K and 15 T for H//ab-plane. To identify the origin of such lack of reproducibility we performed microstructural characterizations, which revealed, within the REBCO layer, large variation in the density of copper oxide (CuO x ) particles ranging from 0.1 to 2 μm in size. We believe that they play a decisive role in reducing the effective cross-section of the REBCO layer by not simply blocking current themselves, but also by nucleating off-axis REBCO grains, whose misoriented grain boundaries adversely impact REBCO grain-to-grain connectivity. The REBCO growth associated with high density CuO x particles also leads to the more disordered spatial arrangement of BaHfO 3 precipitate arrays, which, when self-aligning along the ab-planes, generate stronger pinning enhancing J c (H//ab) at all temperatures. In this way, we established that variations of both connectivity and vortex pinning are thus directly coupled. Our results also explain why the so-called ‘lift-factor’, typically defined by the ratio between I c (T, H) and I c (77 K, sf), frequently turns out to be unreliable.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Adaptive machine learning for time-varying systems: low dimensional latent space tuning

Machine learning (ML) tools such as encoder-decoder convolutional neural networks (CNN) can represent incredibly complex nonlinear functions which map between combinations of images and scalars. For example, CNNs can be used to map combinations of accelerator parameters and images which are 2D projections of the 6D phase space distributions of charged particle beams as they are transported between various particle accelerator locations. Despite their strengths, applying ML to time-varying systems, or systems with shifting distributions, is an open problem, especially for large systems for which collecting new data for re-training is impractical or interrupts operations. Particle accelerators are one example of large time-varying systems for which collecting detailed training data requires lengthy dedicated beam measurements which may no longer be available during regular operations. We present a novel method of adaptive ML for time-varying systems. Our approach is to map very high (N ≈ 100k) dimensional inputs (a combination of scalar parameters and images) into the low dimensional (N ≈ 2) latent space at the output of the encoder section of an encoder-decoder CNN. We then actively tune the low dimensional latent space-based representation of complex system dynamics by the addition of an adaptively tuned feedback vector directly before the decoder sections builds back up to our image-based high-dimensional phase space density representations. This method allows us to learn correlations within and to quickly tune the characteristics of incredibly large parameter space systems and to track their evolution in real time based on feedback without massive new data sets for re-training. We demonstrate that our method can accurately predict and track the phase space of charged particle beams at various locations in a particle accelerator by adaptively adjusting in real-time while the unknown input beam distribution of the accelerator is changing in shape, charge, and offset and while the RF system of the accelerator itself is also changing in an unpredictable way. For FACET-II we demonstrate that such an approach has the potential to use transverse deflecting cavity and energy spread spectrum beam measurements to accurately predict 2D projections of the 6D phase space of the electron beam at the plasma wakefield acceleration interaction point where such diagnostics are unavailable.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

The completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: large-scale structure catalogues and measurement of the isotropic BAO between redshift 0.6 and 1.1 for the Emission Line Galaxy Sample

ABSTRACT We present the Emission Line Galaxy (ELG) sample of the extended Baryon Oscillation Spectroscopic Survey from the Sloan Digital Sky Survey IV Data Release 16. We describe the observations and redshift measurement for the 269 243 observed ELG spectra, and then present the large-scale structure catalogues, used for the cosmological analysis, and made of 173 736 reliable spectroscopic redshifts between 0.6 and 1.1. We perform a spherically averaged baryon acoustic oscillations (BAO) measurement in configuration space, with density field reconstruction: the data two-point correlation function shows a feature consistent with that of the BAO, the BAO model being only weakly preferred over a model without BAO (Δχ2 < 1). Fitting a model constrained to have a BAO feature provides a 3.2 per cent measurement of the spherically averaged BAO distance DV(zeff)/rdrag = 18.23 ± 0.58 at the effective redshift zeff = 0.845.

Raichoor, Anand↗
DOE OSTI2022

Exploiting non-linear scales in galaxy–galaxy lensing and galaxy clustering: A forecast for the dark energy survey

ABSTRACT The combination of galaxy–galaxy lensing (GGL) and galaxy clustering is a powerful probe of low-redshift matter clustering, especially if it is extended to the non-linear regime. To this end, we use an N-body and halo occupation distribution (HOD) emulator method to model the redMaGiC sample of colour-selected passive galaxies in the Dark Energy Survey (DES), adding parameters that describe central galaxy incompleteness, galaxy assembly bias, and a scale-independent multiplicative lensing bias Alens. We use this emulator to forecast cosmological constraints attainable from the GGL surface density profile ΔΣ(rp) and the projected galaxy correlation function wp, gg(rp) in the final (Year 6) DES data set over scales $r_p=0.3\!-\!30.0\, h^{-1} \, \mathrm{Mpc}$. For a $3{{\ \rm per\ cent}}$ prior on Alens we forecast precisions of $1.9{{\ \rm per\ cent}}$, $2.0{{\ \rm per\ cent}}$, and $1.9{{\ \rm per\ cent}}$ on Ωm, σ8, and $S_8 \equiv \sigma _8\Omega _m^{0.5}$, marginalized over all halo occupation distribution (HOD) parameters as well as Alens. Adding scales $r_p=0.3\!-\!3.0\, h^{-1} \, \mathrm{Mpc}$ improves the S8 precision by a factor of ∼1.6 relative to a large scale ($3.0\!-\!30.0\, h^{-1} \, \mathrm{Mpc}$) analysis, equivalent to increasing the survey area by a factor of ∼2.6. Sharpening the Alens prior to $1{{\ \rm per\ cent}}$ further improves the S8 precision to $1.1{{\ \rm per\ cent}}$, and it amplifies the gain from including non-linear scales. Our emulator achieves per cent-level accuracy similar to the projected DES statistical uncertainties, demonstrating the feasibility of a fully non-linear analysis. Obtaining precise parameter constraints from multiple galaxy types and from measurements that span linear and non-linear clustering offers many opportunities for internal cross-checks, which can diagnose systematics and demonstrate the robustness of cosmological results.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Evidence of galaxy assembly bias in SDSS DR7 galaxy samples from count statistics

We present observational constraints on the galaxy–halo connection, focusing particularly on galaxy assembly bias from a novel combination of counts-in-cylinders statistics, P(N CIC ), with the standard measurements of the projected two-point correlation function w p (r p ), and number density n gal of galaxies. We measure n gal , w p (r p ), and P(N CIC ) for volume-limited, luminosity-threshold samples of galaxies selected from SDSS DR7, and use them to constrain halo occupation distribution (HOD) models, including a model in which galaxy occupation depends upon a secondary halo property, namely halo concentration. We detect significant positive central assembly bias for the M r < -20.0 and M r <-19.5 samples. Central galaxies preferentially reside within haloes of high concentration at fixed mass. Positive central assembly bias is also favoured in the M r < -20.5 and M r < -19.0 samples. We find no evidence of central assembly bias in the M r < -21.0 sample. We observe only a marginal preference for negative satellite assembly bias in the M r < -20.0 and M r < -19.0 samples, and non-zero satellite assembly bias is not indicated in other samples. Our findings underscore the necessity of accounting for galaxy assembly bias when interpreting galaxy survey data, and demonstrate the potential of count statistics in extracting information from the spatial distribution of galaxies, which could be applied to both galaxy–halo connection studies and cosmological analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

The impact of varying inhomogeneous reionization histories on metrics of Ly α opacity

The epoch of hydrogen reionization is complete by z = 5⁠, but its progression at higher redshifts is uncertain. Measurements of Ly α forest opacity show large scatter at z < 6⁠, suggestive of spatial fluctuations in neutral fraction, temperature, or ionizing background, either individually or in combination. However, there are degeneracies in the impact of such fluctuations, necessitating careful modelling. We develop a framework for modelling the reionization history and associated temperature fluctuations, with the intention of incorporating ionizing background fluctuations at a later time. We generate several reionization histories using seminumerical code AMBER, and implement them in the Nyx cosmological hydrodynamics code to examine the impact on the evolution of gas within the simulation and the associated metrics of the Ly α forest opacity. We find that the pressure smoothing scale within the intergalactic medium is strongly correlated with the adiabatic index of the temperature–density relation. We find that while models with 20 000 K photoheating at reionization are better able to reproduce the shape of the observed z = 5 1D flux power spectrum than colder ones, they fail to match the highest wavenumbers. The simulated autocorrelation function and optical depth distributions are systematically low and narrow, respectively, compared to the observed values, but are in better agreement when the reionization history is longer in duration, more symmetric in its distribution of reionization redshifts, or if there are remaining neutral regions at z < 6.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Liquid-Liquid Transition in Water from First Principles

A long-standing question in water research is the possibility that supercooled liquid water can undergo a liquid-liquid phase transition (LLT) into high- and low-density liquids. Here, we used several complementary molecular simulation techniques to evaluate the possibility of an LLT in an ab initio neural network model of water trained on density functional theory calculations with the SCAN exchange correlation functional. We conclusively show the existence of a first-order LLT and an associated critical point in the SCAN description of water, representing the first definitive computational evidence for an LLT in water from first principles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Pair distribution function analysis of discrete nanomaterials in PDFgui

Pair distribution functions (PDFs) are a leading tool for atomic structure analysis of nanomaterials. However, the most widely used programs for refining atomic structure against PDF data are based on extended crystallographic models, which cannot be applied to discrete, whole nanoparticles. Furthermore, this work describes a straightforward approach to simulate and refine atomistic models of discrete clusters and nanoparticles employing widely used PDF modelling programs such as PDFgui that utilize extended crystallographic models. In this approach, the whole particle to be modelled is contained within an expanded, and otherwise empty, unit cell that is sufficiently large to avoid correlations between atoms in neighbouring unit cells over the r range analyzed. The PDF of the particle is simulated as a composite using two conventional `phases': one that calculates the atom–atom correlations and one that approximates the local number density. This approach is first validated for large nanoparticles that are well modelled by a conventional shape factor model, and then applied to simulate the PDF of discrete particles and low-dimensional materials (graphene and MXene) and to model the experimental PDF data for single-layer FeS nanosheets. A comparison of this approach with the DiffPy-CMI program, which calculates the PDF of discrete species, shows that the composite modelling approach is equally or more accurate. Example input files for implementing this approach within PDFgui and TOPAS, and recommendations for selecting model parameters for reliable application of this refinement strategy, are provided.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Intelligent Electrochemical Sensors for Precise Identification of Volatile Organic Compounds Enabled by Neural Network Analysis

The volatile organic compounds (VOCs) in a wide spectrum of categories were identified as biomarkers in aquatic environments, playing an important role in marine and freshwater ecology and global atmospheric chemistry. VOCs released from biofuel have also attracted increasing attention. Although the importance has been recognized, the portable detection and analysis methods of VOC in aquatic systems have not yet been well developed and understood. Here, in this work, we innovatively proposed an intelligent electrochemical sensing approach to classify and quantify VOCs in solution. Utilizing the cyclic voltammetry (CV) method with an ionic liquid (IL)-based electrolyte, we analyzed 50 μL samples of various VOC analytes, including acetic acid (AC), acetone, dimethylformamide (DMF), dimethyl sulfoxide (DMSO), ethanol, formaldehyde, formic acid, methanol, methyl formate (MF), toluene, and a formaldehyde-methanol mixture, along with deionized water (DI water). The generated voltammograms were subsequently analyzed using our uniquely designed and optimized 1-D convolutional neural network (1D-CNN). This deep-learning algorithm achieved a 99.09% accuracy in VOC classification validated through fivefold cross-validation and demonstrated an impressive 94.4% test accuracy for methanol detection within a 10 μL error range. For quantification, the system accurately categorized methanol volumes ranging from 0 to 50 μL in 10 μL increments, achieving a 98.18% accuracy. A notable linear correlation (R2 = 95.56%) was found between max current density at the oxidation peak and methanol volume, with the limit of detection (LOD) at 9.3 μL. Such a sensing method exhibits potential for portability, high accuracy, and generalization in the classification and quantification, ultimately reshaping the realm of VOC analysis in solution.

42 ENGINEERING↗