Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “specific 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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Determining the Conformation of Supported Complexes Using an 17 O TEDOR-like NMR Experiment

Dynamic nuclear polarization surface-enhanced nuclear magnetic resonance (NMR) spectroscopy has enabled the determination of the three-dimensional configuration of surface sites, in particular supported metal complexes of relevance to single-site heterogeneous catalysis. These approaches have chiefly leveraged the application of NMR double-resonance experiments that either reveal the complex conformation via point-to-point intramolecular distances between spin-labeled atoms or the complex-surface orientation via distances between the spins and the surface plane. Either method typically requires expensive isotope labeling and each reports on different structural features. The application of an experiment that simultaneously reveals both types of distances with chemical resolution would be ideal. Here, in this article, we describe an 17 O{ 1 H} pseudo-3D correlation experiment that achieves this goal. Specifically, Si–O–Si and Si–O–M oxygens are well-resolved by 17 O NMR; therefore, distances can be simultaneously measured radially, between Si– 17 O–M and the 1 H’s of the ligands, and vertically to the Si– 17 O–Si linkages of the silica support. We demonstrate the experiment using supported yttrium and zirconium complexes. Good agreement is obtained when comparing the experimental results to theoretical predictions from density functional theory calculations, highlighting the reliability of this relatively simple experiment.

alkyls↗
DOE OSTI2020

Antimicrobial Properties of Corn Stover Lignin Fractions Derived from Catalytic Transfer Hydrogenolysis in Supercritical Ethanol with a Ru/C Catalyst

Converting lignin to value-added products at high yields provides an avenue for making ethanol biorefineries more profitable while reducing the carbon footprint of products generally derived from petroleum. In this study, corn stover lignin was depolymerized by catalytic transfer hydrogenolysis (CTH) in supercritical ethanol with a Ru/C catalyst. The lignin-derived bio-oil was then sequentially extracted utilizing hexane, petroleum ether, chloroform, and ethyl acetate as solvents in order of less polar to polar, and the subsequent bio-oils were characterized using GPC, GC/MS, and HSQC NMR. In this work, results show that the monomers in the bio-oil fractions contained primarily alkylated phenols, hydrogenated hydroxycinnamic acid derivatives, syringol and guaiacol-type lignins created from reductive cleavages of ether linkages, which were sequentially extracted into groups depending on the solvent polarity. The antimicrobial properties of the bio-oils were screened against Gram-positive (Bacillus subtilis, Lactobacillus amylovorus, and Staphylococcus epidermidis) and Gram-negative (Escherichia coli) bacteria and yeast (Saccharomyces cerevisiae) by examining microbial growth inhibition. Results show that CTH-derived bio-oils inhibited all tested organisms at concentrations less than 3 mg/mL. Total monomer concentration and the presence of specific monomers (i.e., syringyl propane) showed correlations to antimicrobial activity, likely due to cell death or membrane damage. This study provides insights into using sequential extraction to fractionate lignin-derived compounds and correlations between the properties of the extracted compounds and their antimicrobial activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Landscape topography structures the soil microbiome in arctic polygonal tundra

Global temperature increases are resulting in thaw of permafrost soil in the arctic with increased emission of greenhouse gases (GHGs). Soil microorganisms are responsible for degradation of the trapped organic carbon (C) in permafrost and emission of GHG as it thaws. However, environmental factors governing microbial degradation of soil C and GHG emissions are poorly understood. Here we determined the functional potential of soil microbiomes in arctic tundra across a cryoperturbed polygonal landscape in Barrow, Alaska. Using a combination of metagenome sequencing and gas flux measurements, we found that the soil microbiome composition, diversity and functional potential varied across the polygon transect and that specific microbes and functional genes were correlated to GHG measurements. Several draft genomes of novel species were obtained with genes encoding enzymes involved in cycling of complex organic compounds. These results have larger implications for prediction of the influence of the soil microbiome on soil C flux from arctic regions undergoing environmental change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Structural and mechanistic insights into protective non-neutralizing antibodies targeting Crimean-Congo hemorrhagic fever virus nucleocapsid protein

Abstract Crimean-Congo Hemorrhagic Fever Virus (CCHFV) is a tick-borne virus endemic to Africa, Asia, and expanding regions within Europe. With mortality rates approaching 40%, rising incidence, and no currently approved countermeasures, CCHFV is recognized as a priority public health threat. CCHFV nucleocapsid protein (NP) has long been a key target for diagnostics. Recently, NP-specific humoral responses have also been correlated with protection conferred by protective vaccines candidates. Additionally, the first non-neutralizing monoclonal antibody (mAb) 9D5 demonstrated protective efficacy against CCHFV challenge, underscoring NP as a viable antiviral target. Here, nine anti-NP mAb were utilized to identify four antigenic sites on NP as well as localize these sites to the head or stalk domains. These mAb also revealed variable levels of in vivo protection, independent from whether the epitope site is located in the head or stalk regions. Additionally, three X-ray crystallography structures were obtained that included CCHFV NP from strain Afg09-2990 in complex with the most potent mAb (9D5). This, along with additional structures of two unbound NPs, revealed structural elements critical for mAb-9D5 broad-spectrum protective characteristics. These findings provide a path towards the rapid identification of broadly protective anti-NP mAb countermeasures.

Moresco, Vanessa↗
DOE OSTI2025

Accurate and uncertainty-aware multi-task prediction of HEA properties using prior-guided deep Gaussian processes

Surrogate modeling techniques have become indispensable in accelerating the discovery and optimization of high-entropy alloys (HEAs), especially when integrating computational predictions with sparse experimental observations. This study systematically evaluates the training and testing performance of four prominent surrogate models—conventional Gaussian processes (cGP), Deep Gaussian processes (DGP), encoder-decoder neural networks for multi-output regression and eXtreme Gradient Boosting (XGBoost)—applied to a hybrid dataset of experimental and computational properties of the 8-component HEA system Al-Co-Cr-Cu-Fe-Mn-Ni-V. We specifically assess their capabilities in predicting correlated material properties, including yield strength, hardness, modulus, ultimate tensile strength, elongation, and average hardness under dynamic/quasi-static conditions, alongside auxiliary computational properties. The comparison highlights the strengths of hierarchical deep modeling approaches in handling heteroscedastic, heterotopic, and incomplete data commonly encountered in materials science. Our findings illustrate that combined surrogate models such as DGPs infused with machine-learned priors outperform other surrogates by effectively capturing inter-property correlations and by assimilating prior knowledge. This enhanced predictive accuracy positions the combined surrogate models as powerful tools for robust and data-efficient materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Two-dimensional electronic–vibrational spectroscopy: Exploring the interplay of electrons and nuclei in excited state molecular dynamics

Two-dimensional electronic-vibrational spectroscopy (2DEVS) is an emerging spectroscopic technique which exploits two different frequency ranges for the excitation (visible) and detection (infrared) axes of a 2D spectrum. In contrast to degenerate 2D techniques, such as 2D electronic or 2D infrared spectroscopy, the spectral features of a 2DEV spectrum report cross correlations between fluctuating electronic and vibrational energy gaps rather than autocorrelations as in the degenerate spectroscopies. The center line slope of the spectral features reports on this cross correlation function directly and can reveal specific electronic-vibrational couplings and rapid changes in the electronic structure, for example. The involvement of the two types of transition moments, visible and infrared, makes 2DEVS very sensitive to electronic and vibronic mixing. 2DEV spectra also feature improved spectral resolution, making the method valuable for unraveling the highly congested spectra of molecular complexes. The unique features of 2DEVS are illustrated in this paper with specific examples and their origin described at an intuitive level with references to formal derivations provided. Although early in its development and far from fully explored, 2DEVS has already proven to be a valuable addition to the tool box of ultrafast nonlinear optical spectroscopy and is of promising potential in future efforts to explore the intricate connection between electronic and vibrational nuclear degrees of freedom in energy and charge transport applications.

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

Moiré engineering in van der Waals heterostructures

Isolated atomic planes can be assembled into a multilayer van der Waals (vdW) heterostructure in a precisely chosen sequence. These heterostructures feature moiré patterns if the constituent 2D material layers are stacked in an incommensurable way, due to a lattice mismatch or twist. This design-by-stacking has opened up the promising area of moiré engineering, a term that can be understood in two different perspectives, namely, (i) structural—engineering a moiré pattern by introducing twist, relative strain, or defects that affect the commensurability of the layers and (ii) functional—exploiting a moiré pattern to find and tune resulting physical properties of a vdW heterostructure. The latter meaning, referring to the application of a moiré pattern, is seen in the literature in the specific context of the observation of correlated electronic states and unconventional superconductivity in twisted bilayer graphene. The former meaning, referring to the design of the moiré pattern itself, is present in the literature but less commonly discussed or less understood. The underlying link between these two perspectives lies in the deformation field of the moiré superlattice. In this Perspective, we describe a path from designing a moiré pattern to employing the moiré pattern to tune physical properties of a vdW heterostructure. We also discuss the concept of moiré engineering in the context of twistronics, strain engineering, and defect engineering in vdW heterostructures. Although twistronics is always associated with moiré superlattices, strain and defect engineering are often not. Here, in this work, we demonstrate how strain and defect engineering can be understood within the context of moiré engineering. Adopting this perspective, we note that moiré engineering creates a compelling opportunity to design and develop multiscale electronic devices.

2D materials↗
DOE OSTI2024

Generative machine learning for detector response modeling with a conditional normalizing flow

In this paper, we explore the potential of generative machine learning models as an alternative to the computationally expensive Monte Carlo (MC) simulations commonly used by the Large Hadron Collider (LHC) experiments. Our objective is to develop a generative model capable of efficiently simulating detector responses for specific particle observables, focusing on the correlations between detector responses of different particles in the same event and accommodating asymmetric detector responses. Here, we present a conditional normalizing flow model ($\mathcal{CNF}$) based on a chain of Masked Autoregressive Flows, which effectively incorporates conditional variables and models high-dimensional density distributions. We assess the performance of the $\mathcal{CNF}$ model using a simulated sample of Higgs boson decaying to diphoton events at the LHC. We create reconstruction-level observables using a smearing technique. We show that conditional normalizing flows can accurately model complex detector responses and their correlation. This method can potentially reduce the computational burden associated with generating large numbers of simulated events while ensuring that the generated events meet the requirements for data analyses. We make our code available at https://github.com/allixu/normalizing_flow_for_detector_response

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Seabird distribution is better predicted by abundance of prey than oceanography. A case study in the Gulf of Cadiz (SW, Iberian Peninsula)

Abstract Quantifying factors that influence marine predator distributions is essential to understanding the current and future change in marine biodiversity. Here, we test whether marine predator distribution relates to prey, or is better predicted by other specific habitat features. We examine the correlation between spatial distribution of three seabird species and their prey, as well as environmental proxies (oceanographic characteristics) in the Gulf of Cadiz, NE Atlantic. We modeled the at-sea distribution of Cory's shearwater, Balearic shearwater and Northern gannet, based on: (i) pelagic fish abundance according to acoustic surveys, and (ii) a forecast-model of remotely sensed environmental variables (productivity, sea surface temperature, and salinity). In general, seabird distributions were better predicted by abundance of fish than by environmental variables at the habitat scale. We obtained consistent correlations between seabird presence and the abundance of medium-sized (10–20 cm) sardines, anchovies and Mediterranean horse mackerel, providing information on their preferred prey. Additionally, oceanographic productivity variables moderately contributed to seabird distribution models, with better predictive value for the critically endangered Balearic shearwater and Northern gannet in the summer, whilst the model for Cory's shearwater's produced poorer predictions. Predator–prey combined studies may represent essential tools for an efficient ecosystem-based management of marine environments.

de la Cruz, Andrés (ORCID:0000000203632498)↗
DOE OSTI2023

On the impact of the galaxy window function on cosmological parameter estimation

One important source of systematics in galaxy redshift surveys comes from the estimation of the galaxy window function. Up until now, the impact of the uncertainty in estimating the galaxy window function on parameter inference has not been properly studied. In this paper, we show that the uncertainty and the bias in estimating the galaxy window function will be salient for ongoing and next-generation galaxy surveys using a simulation-based approach. With a specific case study of cross-correlating emission-line galaxies from the DESI Legacy Imaging Surveys and the Planck cosmic microwave background lensing map, we show that neural network-based regression approaches to modelling the window function are superior in comparison to linear regression-based models. Here, we additionally show that the definition of the galaxy overdensity estimator can impact the overall signal-to-noise of observed power spectra. Finally, we show that the additive biases coming from the window functions can significantly bias the modes of the inferred parameters and also degrade their precision. Thus, a careful understanding of the window functions will be essential to conduct cosmological experiments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Magnon heat transport in a two-dimensional Mott insulator

Whether or not anomalies in the thermal conductivity in insulating cuprates can be attributed to antiferromagnetic order and magnons in a 2D Mott insulator remains an intriguing open question. To shed light on this issue, we investigate the thermal conductivity $\kappa$ and its relationship with the specific heat $c_v$ in the half-filled 2D single-band Hubbard model, using the numerically exact determinant quantum Monte Carlo algorithm and maximum entropy analytic continuation. At low temperatures where the charge degrees of freedom are gapped-out and $c_v$ exhibits a clear magnon peak, we observe that thermal conductivity $\kappa$ also tends to form a peak at similar temperatures. Reducing temperature further produces a sharp upturn in $\kappa$, associated with an increasing mean-free path. Here we identify this as the high-temperature side of the anomalous peak in insulating cuprates, where the mean-free path eventually is cut-off by other scattering effects, including phonons, disorder, and physical size. Different scattering effects in our model are identified and analyzed in the thermal diffusivity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Free energy and specific heat near a quantum critical point of a metal

We analyze the free energy and the specific heat for fermions interacting with a gapless boson at a quantum-critical point (QCP) in a metal. We use the Luttinger-Ward-Eliashberg formula for the free energy in the normal state, which includes contributions from bosons, fermions, and their interaction, all expressed via fully dressed fermionic and bosonic propagators. The sum of the last two contributions is the free energy F γ of an effective low-energy model of fermions with boson-mediated dynamical 4-fermion interaction V(Ω m ) ∝ 1/|Ω m | γ (the γ model). This purely electronic model has been used to analyze the interplay between non-Fermi liquid behavior and pairing near a QCP, which are both independent of the upper energy cutoff Λ. However, the specific heat C γ (T), obtained from F γ , does depend on Λ. We argue that this dependence is spurious and cancels out, once we include the contribution from bosons. We further argue that the full C(T) is the sum of the contribution from free fermions and the one from a critical boson, with the fully dressed propagator, other terms cancel out. We compare the full C(T) with the C γ (T), obtained using recently proposed regularization of F γ . We argue that for γ < 1, the full C(T) and the regularized C γ (T) differ by a γ-dependent prefactor, while for γ > 1, the full C(T) and C γ (T) differ by the positive contribution from free massless fermions (a positive constant for the electron-phonon case γ = 2). For these γ, C γ (T) is negative, but the full C(T) is positive. We argue that only the full C(T) matters as the positive and the negative contributions originate from the term in C(T) which contains the fully dressed bosonic propagator. Here, we then argue that the normal state remains stable until the pairing instability develops.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Selectivity of Electronic Coherence and Attosecond Ionization Delays in Strong-Field Double Ionization

Experiments are presented on real-time probing of coherent electron dynamics in xenon initiated by strong-field double ionization. Attosecond transient absorption measurements allow for characterization of electronic coherences as well as relative ionization timings in multiple electronic states of $Xe^{+}$ and Xe$^{2+}$. A high degree of coherence g=0.4 is observed between $^{3}P_{2}^{0}-^{3}P_{0}^{0}$ of $Xe^{2+}$, whereas for other possible pairs of states the coherences are below the detection limits of the experiments. A comparison of the experimental results with numerical simulations based on an uncorrelated electron-emission model shows that the coherences produced by strong-field double ionization are more selective than predicted. Surprisingly short ionization time delays, 0.85 fs, 0.64 fs, and 0.75 fs relative to $Xe^{+}$ formation, are also measured for the $^{3}P_{2}$, $^{3}P_{0}$, and $^{3}P_{1}$ states of Xe$^{2+}$, respectively. Both the unpredicted selectivity in the formation of coherence and the subfemtosecond time delays of specific states provide new insight into correlated electron dynamics in strong-field double ionization.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Lifelong cytomegalovirus and early‐LIFE irradiation synergistically potentiate age‐related defects in response to vaccination and infection

Abstract While whole‐body irradiation (WBI) can induce some hallmarks of immune aging, (re)activation of persistent microbial infection also occurs following WBI and may contribute to immune effects of WBI over the lifespan. To test this hypothesis in a model relevant to human immune aging, we examined separate and joint effects of lifelong latent murine cytomegalovirus (MCMV) and of early‐life WBI over the course of the lifespan. In late life, we then measured the response to a West Nile virus (WNV) live attenuated vaccine, and lethal WNV challenge subsequent to vaccination. We recently published that a single dose of non‐lethal WBI in youth, on its own, was not sufficient to accelerate aging of the murine immune system, despite widespread DNA damage and repopulation stress in hematopoietic cells. However, 4Gy sub‐lethal WBI caused manifest reactivation of MCMV. Following vaccination and challenge with WNV in the old age, MCMV‐infected animals experiencing 4Gy, but not lower, dose of sub‐lethal WBI in youth had reduced survival. By contrast, old irradiated mice lacking MCMV and MCMV‐infected, but not irradiated, mice were both protected to the same high level as the old non‐irradiated, uninfected controls. Analysis of the quality and quantity of anti‐WNV immunity showed that higher mortality in MCMV‐positive WBI mice correlated with increased levels of MCMV‐specific immune activation during WNV challenge. Moreover, we demonstrate that infection, including that by WNV, led to MCMV reactivation. Our data suggest that MCMV reactivation may be an important determinant of increased late‐life mortality following early‐life irradiation and late‐life acute infection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Evidence for lack of direct causality between pain and affective disturbances in a rat peripheral neuropathy model

Chronic pain is frequently accompanied by the manifestation of emotional disturbances and cognitive deficits. While a causality relation between pain and emotional/cognitive disturbances is generally assumed, several observations suggest a temporal dissociation and independent mechanisms. We therefore studied Sprague‐Dawley rats that presented a natural resistance to pain manifestation in a neuropathy model (spared nerve injury [SNI]) and compared their performance in a battery of behavioral paradigms—anxiety, depression and fear memory—with animals that presented a pain phenotype. Afterward, we performed an extensive volumetric analysis across prefrontal, orbitofrontal and insular cortical areas. The majority of SNI animals manifested mechanical allodynia (low threshold [LT]), but 13% were similar to Sham controls (high threshold [HT]). Readouts of spontaneous hypersensivity (paw flinches) were also significantly reduced in HT and correlated with allodynia. To increase the specificity of our findings, we segregated the SNI animals in those with left (SNI‐L) and right (SNI‐R) lesions and the lack of association between pain and behavior still remains. Left‐lesioned animals, independent of the LT or HT phenotype, presented increased anxiety‐like behaviors and decreased well‐being. In contrast, we found that the insular cortex (agranular division) was significantly smaller in HT than in LT. To conclude, pain and emotional disturbances observed following nerve injury are to some extent segregated phenomena. Also, HT and LT SNI presented differences in insular volumes, an area vastly implicated in pain perception, suggesting a supraspinal involvement in the manifestation of these phenotypes.

Guimarães, Marco R.↗
DOE OSTI2023

Coarse-grained explicit-solvent molecular dynamics simulations of semidilute unentangled polyelectrolyte solutions

In this study, we present results from explicit-solvent coarse-grained molecular dynamics (MD) simulations of fully charged, salt-free, and unentangled polyelectrolytes in semidilute solutions. The inclusion of a polar solvent in the model allows for a more physical representation of these solutions at concentrations, where the assumptions of a continuum dielectric medium and screened hydrodynamics break down. The collective dynamic structure factor of polyelectrolytes, S(q, t), showed that at q > q*, where q* = 2π/ξ is the polyelectrolyte peak in the structure factor S(q) and ξ is the correlation length, the relaxation time obtained from fits to stretched exponential was $\tau$ KWW ~ q -3 , which describes unscreened Zimm-like dynamics. This is in contrast to implicit-solvent simulations using a Langevin thermostat where $\tau$ KWW ~ q -2 . At q < q*, a crossover region was observed that eventually transitions to another inflection point $\tau$ KWW ~ q -2 at length scales larger than ξ for both implicit- and explicit-solvent simulations. The simulation results were also compared to scaling predictions for correlation length, ξ ~ c$-½\atop{p}$, specific viscosity, η sp ~ c$½\atop{p}$, and diffusion coefficient, D ~ c$0\atop{p}$, where c p is the polyelectrolyte concentration. The scaling prediction for ξ holds; however, deviations from the predictions for η sp and D were observed for systems at higher c p , which are in qualitative agreements with recent experimental results. This study highlights the importance of explicit-solvent effects in molecular dynamics simulations, particularly in semidilute solutions, for a better understanding of polyelectrolyte solution behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2022

MemHC: An Optimized GPU Memory Management Framework for Accelerating Many-body Correlation

The many-body correlation function is a fundamental computation kernel in modern physics computing applications, e.g., Hadron Contractions in Lattice quantum chromodynamics (QCD). This kernel is both computation and memory intensive, involving a series of tensor contractions, and thus usually runs on accelerators like GPUs. Existing optimizations on many-body correlation mainly focus on individual tensor contractions (e.g., cuBLAS libraries and others). In contrast, this work discovers a new optimization dimension for many-body correlation by exploring the optimization opportunities among tensor contractions. More specifically, it targets general GPU architectures (both NVIDIA and AMD) and optimizes many-body correlation’s memory management by exploiting a set of memory allocation and communication redundancy elimination opportunities: first, GPU memory allocation redundancy: the intermediate output frequently occurs as input in the subsequent calculations; second, CPU-GPU communication redundancy: although all tensors are allocated on both CPU and GPU, many of them are used (and reused) on the GPU side only, and thus, many CPU/GPU communications (like that in existing Unified Memory designs) are unnecessary; third, GPU oversubscription: limited GPU memory size causes oversubscription issues, and existing memory management usually results in near-reuse data eviction, thus incurring extra CPU/GPU memory communications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Electrolyte Solvation and Ionic Association. VII. Correlating Raman Spectroscopic Data with Solvate Species

Raman spectroscopy is a widely utilized method for determining information about the solvate species present in electrolyte solutions. Difficulties arise, however, when interpreting the Raman data in terms of correlating the spectroscopic vibrational bands with specific solvate species. Examples are provided to demonstrate how erroneous conclusions may be drawn with regard to the identity and population (distribution) of the solvent-separated ion pair (SSIP), contact ion pair (CIP) and aggregate (AGG) solvate species frequently mentioned in the scientific literature.

contact ion pairs↗