Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “generalized functions”

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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Chronic obstructive pulmonary disease among former United States Department of Energy workers: comorbidities and lung function changes

Chronic Obstructive Pulmonary Disease (COPD) is a major cause of morbidity and mortality in the United States and is frequently associated with multiple comorbidities which lead to poor COPD outcomes in the general population. However, little is known regarding COPD comorbidities in occupational cohorts whose exposure experiences could result in differences in comorbidities compared to the general population. These differences may also be important for assessing COPD outcomes such as lung function changes or decline. Therefore, the objectives of this study were to: (1) identify and describe clusters of COPD comorbidities among Department of Energy (DOE) former workers; (2) assess if the attributes of the identified clusters differ from those identified among the general population based on the published literature, and (3) identify predictors of lung function changes and decline among DOE former workers.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Identification of recG genetic interactions in Escherichia coli by transposon sequencing

ABSTRACT Maintaining the integrity of the genome is of utmost importance for cell division and propagation. In Escherichia coli , the RecG protein has been implicated in processing branched recombination intermediates during DNA repair processes, but the primary cellular role(s) of RecG and the repair pathways in which it acts have been difficult to define. To gain additional insight into RecG function, we employed transposon sequencing to identify recG genetic interactions and reveal complementary or redundant functions. The strongest hits from the screen were the dam , uvrD , rnhA , radA , and rep genes. The conditional importance of these five genes in cells lacking recG was confirmed using a plasmid-based assay, revealing synthetic lethal interactions for most double deletion strains. Several of the synthetic lethal gene combinations (with uvrD, rep, and radA , but not rnhA or dam ) were suppressed by deletion of recF or recO , indicating that their genetic relationships involved roles in post-replication gap repair. Additionally, loss of the RecG/SSB interaction phenocopied a recG deletion when combined with dam , uvrD , radA , or rnhA deletions but not with rep . The results reinforce the idea of RecG as a general genome guardian. RecG has at least two functions. It plays an important role in the resolution of joint molecules behind the fork, formed during post-replication gap repair. RecG is also required to suppress genome over-replication caused by unscheduled replication initiation at R-loops, at double-strand breaks caused by dam inactivation, and during replication termination. IMPORTANCE DNA damage and subsequent DNA repair processes are mutagenic in nature and an important driver of evolution in prokaryotes, including antibiotic resistance development. Genetic screening approaches, such as transposon sequencing (Tn-seq), have provided important new insights into gene function and genetic relationships. Here, we employed Tn-seq to gain insight into the function of the recG gene, which renders Escherichia coli cells moderately sensitive to a variety of DNA-damaging agents when they are absent. The reported recG genetic interactions can be used in combination with future screens to aid in a more complete reconstruction of DNA repair pathways in bacteria.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Deep potential generation scheme and simulation protocol for the Li 10 GeP 2 S 12 -type superionic conductors

We report solid-state electrolyte materials with superior lithium ionic conductivities are vital to the next-generation Li-ion batteries. Molecular dynamics could provide atomic scale information to understand the diffusion process of Li-ion in these superionic conductor materials. Here, we implement the deep potential generator to set up an efficient protocol to automatically generate interatomic potentials for Li 10 GeP 2 S 12 -type solid-state electrolyte materials (Li 10 GeP 2 S 12 , Li 10 SiP 2 S 12 , and Li 10 SnP 2 S 12 ). The reliability and accuracy of the fast interatomic potentials are validated. With the potentials, we extend the simulation of the diffusion process to a wide temperature range (300 K–1000 K) and systems with large size (~1000 atoms). Important technical aspects such as the statistical error and size effect are carefully investigated, and benchmark tests including the effect of density functional, thermal expansion, and configurational disorder are performed. The computed data that consider these factors agree well with the experimental results, and we find that the three structures show different behaviors with respect to configurational disorder. Our work paves the way for further research on computation screening of solid-state electrolyte materials.

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

IMG Annotation Pipeline (IMGAP) v5.1.13

The IMG Annotation Pipeline is a collection of Bash and Python scripts to control a workflow for structural and functional annotation of prokaryotic genomes, metagenomes, and metatranscriptomes. The bash scripts in general control the overall workflow and are wrappers around 3rd party executables (not included in repo) that predict features or functions. Whereas the Python scripts do post-processing of raw output in terms of filtering or format transformation and in some cases contain some logic for picking the correct predictions or resolving overlaps. The pipeline is tailored to produce results required by IMG (https://img.jgi.doe.gov/) and is executed on every dataset submitted to IMG via https://img.jgi.doe.gov/submit. These consist of internal genomes, metagenomes and metatranscriptomes sequenced and assembled at the JGI, as well as datasets submitted by external users (non-lab/JGI affiliates).

Huntemann, Marcel↗
DOE OSTI2021

User Guide to the Advanced Dimensional Depletion for Engineering of Reactors (ADDER) Software (V.1.0)

The Advanced Dimensional Depletion for Engineering of Reactors (ADDER) software is being developed in the Research and Test Reactor (RTR) Program at Argonne National Laboratory to meet the reactor design and analysis needs of the Conversion Program. ADDER is a flexible tool that (1) provides a depletion capability through coupling external neutronics codes with a built-in CRAM solver or external depletion code and (2) provides a user-friendly interface to perform fuel management and criticality search operations. The ADDER software is a Python 3 application written using modern software development practices subject to a compliant implementation of NQA-1 and applicable Department of Energy software quality assurance standards. This report is the user guide for the initial software release as ADDER v1.0.0. The motivation for a software to have flexible capabilities that ADDER possesses is the need to support a wide variety of geometries that are commonly required in analysis of research and test reactors. These reactors can have complex fuel, experiment, or control material shuffling patterns that persist over several years with many fuel management and partial refueling intervals. The scale of fuel management analysis can require tracking of an inventory that is multiple times the core loading. Many reactors, both power and non-power reactors of various types, will find the features of ADDER useful to facilitate key tasks that a fuel or core design engineer must perform with the convenience of concise input and validated functionality.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Tailoring the Electronic Structures and Spectral Properties of ZnO with Irradiation Defects Generated Under Intense Electronic Excitation: A Combined Experimental and DFT Approach

Here, the relationship between the composition of the internal defect states, spectral properties, and correlated electronic structures of wurtzite zinc oxide (ZnO) crystals under 645 MeV Xe 35+ irradiation is systematically investigated, employing experimental characterizations combined with first-principle calculations. Based on the ion irradiation-induced thermal expansion and relaxation processes, the high concentration of vacancy/interstitial defects produced from the transient disordered phase in molten track states trigger photoelectric changes, as follows: i) the generation of internal defect states effectively reduces the intrinsic bandgap (3.25 eV → 2.66 eV); ii) a large number of defective active sites inhibits the recombination between electron–hole pairs, causing dark conductance and photoconductance to increase with increasing damage levels until optimal fluence is achieved. Based on the density functional theory (DFT) with the GGA + U (GGA = generalized gradient approximation) method, the defective models associated with the different electronic structures, density of states, formation energy, and the nature of the chemical bonding are established. The narrowing of the bandgap observed experimentally and the enhancement of carrier concentration originating from the internal electron defect states are qualitatively verified, therefore laying the foundation for designing future nanoscale photoelectronic devices and microelectronics applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Ab Initio Transport Calculations: From Normal to Superconducting Current

Applying the Bogoliubov-de Gennes equations with density-functional theory, it is possible to formulate first principles description of current-phase relationships in superconducting/normal (magnetic)/superconducting tri-layers. Such structures are the basis for the superconducting analog of Magnetoresistive random access memory devices (JMRAM). In a recent paper1 we presented results from the first attempt to formulate such a theory, applied to the Nb/Ni/Nb trilayers. In the present work we provide computational details, explaining how to construct key ingredient (scattering matrices SN ) in a framework of linear muffin-tin orbitals (LMTO).

CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS↗
DOE OSTI2021

Dispersion formulas in QFTs, CFTs and holography

We study momentum space dispersion formulas in general QFTs and their applications for CFT correlation functions. We show, using two independent methods, that QFT dispersion formulas can be written in terms of causal commutators. The first derivation uses analyticity properties of retarded correlators in momentum space. The second derivation uses the largest time equation and the defining properties of the time-ordered product. At four points we show that the momentum space QFT dispersion formula depends on the same causal double-commutators as the CFT dispersion formula. At n -points, the QFT dispersion formula depends on a sum of nested advanced commutators. For CFT four-point functions, we show that the momentum space dispersion formula is equivalent to the CFT dispersion formula, up to possible semi-local terms. We also show that the Polyakov-Regge expansions associated to the momentum space and CFT dispersion formulas are related by a Fourier transform. In the process, we prove that the momentum space conformal blocks of the causal double-commutator are equal to cut Witten diagrams. Finally, by combining the momentum space dispersion formulas with the AdS Cutkosky rules, we find a complete, bulk unitarity method for AdS/CFT correlators in momentum space.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Understanding the chemical bonding of ground and excited states of HfO and HfB with correlated wavefunction theory and density functional approximations

Knowledge of the chemical bonding of HfO and HfB ground and low-lying electronic states provides essential insights into a range of catalysts and materials that contain Hf–O or Hf–B moieties. Here, we carry out high-level multi-reference configuration interaction theory and coupled cluster quantum chemical calculations on these systems. We compute full potential energy curves, excitation energies, ionization energies, electronic configurations, and spectroscopic parameters with large quadruple-ζ and quintuple-ζ quality correlation consistent basis sets. We also investigate equilibrium chemical bonding patterns and effects of correlating core electrons on property predictions. Differences in the ground state electron configuration of HfB(X 4 Σ - ) and HfO(X 1 Σ + ) lead to a significantly stronger bond in HfO than HfB, as judged by both dissociation energies and equilibrium bond distances. We extend our analysis to the chemical bonding patterns of the isovalent HfX (X = O, S, Se, Te, and Po) series and observe similar trends. We also note a linear trend between the decreasing value of the dissociation energy (D e ) from HfO to HfPo and the singlet–triplet energy gap (ΔE S–T ) of the molecule. Finally, we compare these benchmark results to those obtained using density functional theory (DFT) with 23 exchange–correlation functionals spanning multiple rungs of “Jacob’s ladder.” When comparing DFT errors to coupled cluster reference values on dissociation energies, excitation energies, and ionization energies of HfB and HfO, we observe semi-local generalized gradient approximations to significantly outperform more complex and high-cost functionals.

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

Accelerating defect predictions in semiconductors using graph neural networks

First-principles computations reliably predict the energetics of point defects in semiconductors but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially ones that sufficiently encode defect coordination environments, can be used to accelerate defect predictions. Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of group IV, III–V, and II–VI zinc blende semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely crystal graph convolutional neural network, materials graph network, and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98% given the range of values within the dataset, improving significantly on the state-of-the-art. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. The current models are based on the semi-local generalized gradient approximation-Perdew–Burke–Ernzerhof (PBE) functional but are highly promising because of the correlation of computed energetics and defect levels with higher levels of theory and experimental data, the accuracy and necessity of discovering novel metastable and low energy defect structures at the PBE level of theory before advanced methods could be applied, and the ability to train multi-fidelity models in the future with new data from non-local functionals. The DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially optimized defective structures, helping identify electronically active defects in technologically important semiconductors.

Rahman, Md Habibur (ORCID:000000027705984X)↗
DOE OSTI2020

Elucidating the Influence of Residual Polymer and Gas Environment on the Electronic Structure of a Graphene Layer Using in Situ APXPS

We use in situ ambient pressure X-ray photoelectron spectroscopy (APXPS) and ultraviolet photoelectron spectroscopy (UPS) to develop an effective method for studying changes in the graphene (Gr) electronic structure according to certain circumstance. The amount of polymethyl methacrylate (PMMA) residual polymer (RP), inevitably generated during the Gr transfer process, is significantly reduced from Gr surface by thermal annealing. This processed Gr is then sequentially exposed to specific gas environments (Ar, N 2 , O 2 , and CO 2 ), and APXPS or UPS is carried out to investigate the variations in the Gr electronic structure including the work function. When the amount of PMMA RP on Gr is reduced, the position of the main carbon peak shifts by more than 0.4 eV to a higher binding energy (in XPS spectra), and the secondary electron cutoff moves by about 0.2 eV to a lower binding energy (in UPS spectra). These changes are generally caused by a decrease in the Gr work function. On the other hand, exposure to the gas environments at different temperatures that we investigated did not produce significant changes in the work function and chemical states of Gr. These results confirm that the material in contact with Gr should be considered to achieve the desired Gr performance in electronics.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Semiclassical model for calculating exciton and polaron pair energetics at interfaces

Exciton and polaron pair dissociation is a key functional aspect of photovoltaic devices. To improve upon the current state of interfacial transport models, we augment the existing classical models of dielectric interfaces by incorporating results from ab initio calculations, allowing us to calculate exciton and polaron binding energies more accurately. We demonstrate the predictive capabilities of this new model using two interfaces: (i) the boron subphthalocyanine chloride (SubPc) and C60 interface, which is an archetype for many organic photovoltaic devices; and (ii) pentacene and silicon (1 0 0), which represents a hybrid between organic and inorganic semiconductors. Overall, our calculations predict that the insertion of molecular dipoles at interfaces can be used for improving polaron pair dissociation and that sharp transitions in dielectric permittivity can have a stronger effect on the polaron pair dissociation than even the electron-hole Coulomb interaction.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Balanced stochastic versus deterministic assembly processes benefit diverse yet uneven ecosystem functions in representative agroecosystems

Ecological assembly processes, by influencing community composition, determine ecosystem functions of microbiomes. However, debate remains on how stochastic versus deterministic assembly processes influence ecosystem functions such as carbon and nutrient cycling. Towards a better understanding, we investigated three types of agroecosystems (the upland, paddy, and flooded) that represent a gradient of stochastic versus deterministic assembly processes. Carbon and nutrient cycling multifunctionality, characterized by nine enzymes associated with soil carbon, nitrogen, phosphorous and sulfur cycling, was evaluated and then associated with microbial assembly processes and cooccurrence patterns of vital ecological groups. Our results suggest that strong deterministic processes favour microorganisms with convergent functions (as in the upland agroecosystem), while stochasticity-dominated processes lead to divergent functions (as in the flooded agroecosystem). To benefit agroecosystems services, we speculate that it is critical for a system to maintain balance between its stochastic and deterministic assembly processes (as in the paddy agroecosystem). By doing so, the system can preserve a diverse array of functional traits and also allow for particular traits to flourish. To further confirm this speculation, it is necessary to develop a systematic knowledge beyond merely characterizing general patterns towards the associations among community assembly, composition, and ecosystem functions.

Liu, Wenjing↗
DOE OSTI2020

Leaf traits and canopy structure together explain canopy functional diversity: an airborne remote sensing approach

Plant functional diversity is strongly connected to photosynthetic carbon assimilation in terrestrial ecosystems. However, many of the plant functional traits that regulate photosynthetic capacity, including foliar nitrogen concentration and leaf mass per area, vary significantly between and within plant functional types and vertically through forest canopies, resulting in considerable landscape-scale heterogeneity in three dimensions. Hyperspectral imagery has been used extensively to quantify functional traits across a range of ecosystems but is generally limited to providing information for top-of-canopy leaves only. On the other hand, lidar data can be used to retrieve the vertical structure of forest canopies. Because these data are rarely collected at the same time, there are unanswered questions about the effect of forest structure on the 3-D spatial patterns of functional traits across ecosystems. In the United States, the National Ecological Observatory Network's Airborne Observation Platform (NEON AOP) provides an opportunity to address this structure-function relationship by collecting lidar and hyperspectral data together across a variety of ecoregions. With a fusion of hyperspectral and lidar data from the NEON AOP and field-collected foliar trait data, we assessed the impacts of forest structure on spatial patterns of N. In addition, we examine the influence of abiotic gradients and management regimes on top of canopy %N and total canopy N (i.e. the total amount of N (g/m 2 ) within a forest canopy) at a NEON site consisting of a mosaic of open longleaf pine and dense broadleaf deciduous forests. Here, our resulting maps suggest that in contrast with top-of-canopy values, total canopy N variation is dampened across this landscape resulting in relatively homogeneous spatial patterns. At the same time, we found that leaf functional diversity and canopy structural diversity showed distinct dendritic patterns related to the spatial distribution of plant functional types.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

On the minimum number of radiation field parameters to specify gas cooling and heating functions

Fast and accurate approximations of gas cooling and heating functions are needed for hydrodynamic galaxy simulations. We use machine learning to analyze atomic gas cooling and heating functions computed by Cloudy in the presence of a generalized incident local radiation field. We characterize the radiation field through binned radiation field intensities instead of the photoionization rates used in our previous work. We find a set of 6 energy bins whose intensities exhibit relatively low correlation. We use these bins as features to train machine learning models to predict Cloudy cooling and heating functions at fixed metallicity. We compare the relative SHapley Additive exPlanation (SHAP) value importance of the features. From the SHAP analysis, we identify a feature subset of 3 energy bins ($0.5-1, 1-4$, and $13-16 \, \mathrm{Ry}$) with the largest importance and train additional models on this subset. We compare the mean squared errors and distribution of errors on both the entire training data table and a randomly selected 20% test set withheld from model training. The machine learning models trained with 3 and 6 bins, as well as 3 and 4 photoionization rates, have comparable accuracy everywhere, with errors $\gtrsim 10$ times smaller than for the interpolation table of Gnedin and Hollon (2012). We conclude that 3 energy bins (or 3 analogous photoionization rates: molecular hydrogen photodissociation, neutral hydrogen HI, and fully ionized carbon CVI) are sufficient to characterize the dependence of the gas cooling and heating functions on our assumed incident radiation field model.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Photoelectron spectra of early 3 d -transition metal dioxide molecular anions from GW calculations

Photoelectron spectra of early 3 d -transition metal dioxide anions, Sc O 2 - , Ti O 2 - , V O 2 - , Cr O 2 - , and Mn O 2 - , are calculated using semilocal and hybrid density functional theory (DFT) and many-body perturbation theory within the GW approximation using one-shot perturbative and eigenvalue self-consistent formalisms. Different levels of theory are compared with each other and with available photoelectron spectra. We show that one-shot GW with a PBE0 starting point ( G 0 W 0 @PBE0) consistently provides very good agreement for all experimentally measured binding energies (within 0.1 eV–0.2 eV or less). We attribute this to the success of PBE0 in mitigating self-interaction error and providing good quasiparticle wave functions, which renders a first-order perturbative GW correction effective. One-shot GW calculations with a Perdew–Burke–Ernzerhof (PBE) starting point do poorly in predicting electron removal energies by underbinding orbitals with typical errors near 1.5 eV. A higher exact exchange amount of 50% in the DFT starting point of one-shot GW does not provide very good agreement with experiment by overbinding orbitals with typical errors near 0.5 eV. While not as accurate as G 0 W 0 @PBE0, the G -only eigenvalue self-consistent GW scheme with W fixed to the PBE level provides a reasonably predictive level of theory (typical errors near 0.3 eV) to describe photoelectron spectra of these 3 d -transition metal dioxide anions. Adding eigenvalue self-consistency also in W , on the other hand, worsens the agreement with experiment overall. Overall, our findings on the performance of various GW methods are discussed in the context of our previous studies on other transition metal oxide molecular systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗