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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Stochastic density functional theory: Real- and energy-space fragmentation for noise reduction

Stochastic density functional theory (sDFT) is becoming a valuable tool for studying ground-state properties of extended materials. The computational complexity of describing the Kohn-Sham orbitals is replaced by introducing a set of random (stochastic) orbitals leading to linear and often sub-linear scaling of certain ground-state observables at the account of introducing a statistical error. Schemes to reduce the noise are essential, for example, for determining the structure using the forces obtained from sDFT. Recently, we have introduced two embedding schemes to mitigate the statistical fluctuations in the electron density and resultant forces on the nuclei. Both techniques were based on fragmenting the system either in real space or slicing the occupied space into energy windows, allowing for a significant reduction in the statistical fluctuations. For chemical accuracy, further reduction of the noise is required, which could be achieved by increasing the number of stochastic orbitals. However, the convergence is relatively slow as the statistical error scales as 1 / N χ according to the central limit theorem, where N χ is the number of random orbitals. In this paper, we combined the embedding schemes mentioned above and introduced a new approach that builds on overlapped fragments and energy windows. The new approach significantly lowers the noise for ground-state properties, such as the electron density, total energy, and forces on the nuclei, as demonstrated for a G-center in bulk silicon.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Effects of surface diffusion in electrocatalytic CO 2 reduction on Cu revealed by kinetic Monte Carlo simulations

We report Kinetic Monte Carlo (KMC) methods are frequently used for mechanistic studies of thermally driven heterogeneous catalysis systems but are underused for electrocatalysis. Here, we develop a lattice KMC approach for electrocatalytic CO 2 reduction. The work is motivated by a prior experimental report that performed electroreduction of a mixed feed of 12 CO 2 and 13 CO on Cu; differences in the 13 C content of C2 products ethylene and ethanol (Δ 13 C) were interpreted as evidence of site selectivity. The lattice KMC model considers the effect of surface diffusion on this system. In the limit of infinitely fast diffusion (mean-field approximation), the key intermediates 12 CO* and 13 CO* would be well mixed on the surface and no evidence of site selectivity could have been observed. Using a simple two-site model and adapting a previously reported microkinetic model, we assess the effects of diffusion on the relative isotope fractions in the products using the estimated surface diffusion rate of CO* from literature reports. We find that the size of the active sites and the total surface adsorbate coverage can have a large influence on the values of Δ 13 C that can be observed. Δ 13 C is less sensitive to the CO* diffusion rate as long as it is within the estimated range. We further offer possible methods to estimate surface distribution of intermediates and to predict intrinsic selectivity of active sites based on experimental observations. This work illustrates the importance of considering surface diffusion in the study of electrochemical CO 2 reduction to multi-carbon products. Our approach is entirely based on a freely available open-source code, so will be readily adaptable to other electrocatalytic systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Reduction of efficiency droop in c -plane InGaN/GaN light-emitting diodes using a thick single quantum well with doped barriers

We report on c-plane InGaN/GaN single quantum well (QW) light-emitting diodes (LEDs) of different well widths (3 or 9 nm) with and without doped barriers. QW barriers were doped with the aim of reducing the internal electric field (F QW ) in the QW to increase the electron-hole overlap, therefore increasing the recombination rates and resulting in the reduction of the efficiency droop. We, indeed, observed, through biased photocurrent spectroscopy, a reduction in F QW with doped barriers, with F QW being in the same direction of the p-n junction field at zero bias as opposed to the junction field for LEDs without doped barriers. Even with the improvement in the ground state wavefunction overlap, the ground state transition rate remains low for thick QWs. Transitions through excited states were observed for both thick QW LEDs with and without doped barriers. Here, the thick QW LED without doped barriers displayed low external quantum efficiency (EQE), likely as a result of the carrier overflow due to the poor confinement of carriers in the excited states. On the other hand, for LEDs with doped barriers, the flatter band in the QW resulting from the lower F QW reduces the energy separation between the eigenstates, leading to better confinement of carriers in the excited states. With doped barriers, we demonstrated a low efficiency droop 9-nm-thick single QW LED with a peak EQE of 42% at 40 A/cm 2 and an EQE of 36% at 400 A/cm 2 .

42 ENGINEERING↗
DOE OSTI2023

An uncertainty-aware strategy for plasma mechanism reduction with directed weighted graphs

In this work, we present a framework for the analysis and reduction of plasma mechanisms by means of weighted directed graphs, in which reactions and species are both treated as nodes. The methodology consists of two distinct analyses. The first, which is qualitative, relies on graph spatializations via force-directed algorithms to discover the predominant global patterns in the chemical model. The second ranks the reactions based on their shortest paths' lengths from/to the species of interest and their relative contributions to the power balance. Further, this quantitative investigation enables a strategy for mechanism reduction that is fully automatized, as it does not require any expert knowledge, highly effective, as it generates reduced mechanisms that are highly accurate while relying on a small number of processes, and easily interpretable, as the algorithm justifies the importance of the retained reactions by outputting their related chemical pathways. Additionally, the work proposes a methodology extension that employs ensembles of graphs to improve the robustness of the reduced mechanism to reaction parameter uncertainties. The approach, here tested for steady-state predictions of a plasma system characterizing negative hydrogen ion sources, is general and can be used in a wide variety of applications outside the particular nuclear fusion context demonstrated in this work.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Noise reduction of stochastic density functional theory for metals

Density Functional Theory (DFT) has become a cornerstone in the modeling of metals. However, accurately simulating metals, particularly under extreme conditions, presents two significant challenges. First, simulating complex metallic systems at low electron temperatures is difficult due to their highly delocalized density matrix. Second, modeling metallic warm-dense materials at very high electron temperatures is challenging because it requires the computation of a large number of partially occupied orbitals. This study demonstrates that both challenges can be effectively addressed using the latest advances in linear-scaling stochastic DFT methodologies. Despite the inherent introduction of noise into all computed properties by stochastic DFT, this research evaluates the efficacy of various noise reduction techniques under different thermal conditions. Our observations indicate that the effectiveness of noise reduction strategies varies significantly with the electron temperature. Furthermore, we provide evidence that the computational cost of stochastic DFT methods scales linearly with system size for metal systems, regardless of the electron temperature regime.

Chemistry↗
DOE OSTI2024

A physics-informed deep learning description of Knudsen layer reactivity reduction

A physics-informed neural network (PINN) is used to evaluate the fast ion distribution in the hot spot of an inertial confinement fusion target. The use of tailored input and output layers to the neural network is shown to enable a PINN to learn the parametric solution to the Vlasov–Fokker–Planck equation in the absence of any synthetic or experimental data. As an explicit demonstration of the approach, the specific problem of Knudsen layer fusion yield reduction is treated. Here, the predictions from the Vlasov–Fokker–Planck PINN are used to provide a non-perturbative solution of the fast ion tail in the vicinity of the hot spot, thus allowing the spatial profile of the fusion reactivity to be evaluated for a range of collisionalities and hot spot conditions. Excellent agreement is found between the predictions of the Vlasov–Fokker–Planck PINN and the results from traditional numerical solvers with respect to both the energy and spatial distribution of fast ions and the fusion reactivity profile, demonstrating that the Vlasov–Fokker–Planck PINN provides an accurate and efficient means of determining the impact of Knudsen layer yield reduction across a broad range of plasma conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Finite-element-based simulations of electrodes for CO 2 cascade reduction reactions

The multielectron reduction of CO 2 to liquid fuels could be a path to scalable energy storage, but reaching this goal requires major advances in catalysis and systems engineering. Cascade catalysis, which couples sequential reactions without isolating intermediates, has emerged as a promising route to enhance selectivity and efficiency in CO 2 reduction (CO 2 R). In this review, we examine how finite-element-based simulations of continuum model [finite element method (FEM)] approaches are being used to analyze and guide CO 2 R cascade systems. We first outline the fundamentals of cascade catalysis and recent advances in catalytic materials (metallic, molecular, and hybrid architectures). We then focus on FEM developments at the electrode and device scales, emphasizing how these models capture transport phenomena, local microenvironments, and geometry-dependent effects. To clarify design principles, we present case studies of cascade electrodes organized in systems without and with integrated semiconductors. We further emphasize the integration of FEM with multiscale frameworks (density functional theory, molecular dynamics, kinetic Monte Carlo) and its role in bridging atomic-level insights with device-level performance. Finally, we identify current limitations and future prospects, including improved boundary conditions, coupling with operando experiments, and machine learning-accelerated model development. Together, these insights provide design principles for next-generation CO 2 R cascade systems for efficient solar fuel production.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

CO 2 reduction on pure Cu produces only H 2 after subsurface O is depleted: Theory and experiment

Significance Electrochemical reduction of CO 2 to fuels and valuable chemicals is a global imperative to address energy and climate challenges. Despite enormous efforts, there is not yet a commercial process. The best catalyst remains Cu metal, but a great deal of controversy revolves around the role of the presence of oxygen in the material. We combine theory and experiments to show that pure Cu without subsurface oxygen leads to no organic products. Thus, maintaining substantial oxygen at Cu surfaces is essential for long-term reduction of CO 2 to organics.

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

Shipping regulations lead to large reduction in cloud perturbations

Global shipping accounts for 13% of global emissions of SO2, which, once oxidized to sulfate aerosol, acts to cool the planet both directly by scattering sunlight and indirectly by increasing the albedo of clouds. This cooling due to sulfate aerosol offsets some of the warming effect of greenhouse gasses and is the largest uncertainty in determining the change in the Earth’s radiative balance by human activity. Ship tracks—the visible manifestation of the indirect of effect of ship emissions on clouds as quasi-linear features—have long provided an opportunity to quantify these effects. However, they have been arduous to catalog and typically studied only in particular regions for short periods of time. Using a machine-learning algorithm to automate their detection we catalog more than 1 million ship tracks to provide a global climatology. We use this to investigate the effect of stringent fuel regulations introduced by the International Maritime Organization in 2020 on their global prevalence since then, while accounting for the disruption in global commerce caused by COVID-19. We find a marked, but clearly nonlinear, decline in ship tracks globally: An 80% reduction in SO x emissions causes only a 25% reduction in the number of tracks detected.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Significant reduction in the low-field magnetization of Nb 3 Sn superconducting strands using the internal oxidation APC approach

Nb 3 Sn superconductors are promising for building accelerator magnets for future energy-frontier circular colliders. A critical factor for this application is the low-field persistent-current magnetization because it leads to several critical issues: e.g. low-field instability (including flux jumps), hysteresis loss, and field errors in magnet bores. Suppression of low-field magnetization requires reduction of low-field critical current density (J c ) or effective subelement size (d eff ). However, reduction of deff of state-of-the-art Nb 3 Sn conductors—the restacked-rod-process (RRP ® ) type—below 40–50 μm without a pronounced decrease in high-field J c is difficult. On the other hand, the internal oxidation method which forms artificial pinning centers (APC) in Nb 3 Sn offers an alternative approach to reducing the low-field magnetization. Compared with a conventional Nb 3 Sn conductor whose flux pinning force versus field (F p –B) curve peaks at ~20% of its irreversibility field (B irr ), the F p –B curve peaks of APC conductors shift to higher fields due to the point pinning effect, leading to flattening of the J c –B curves. The goal of this paper is to quantitatively study how much the APC approach can reduce the low-field magnetization. Here we measured the J c –B curves of an RRP ® conductor and two APC conductors (reacted at 700 °C) from zero field to B irr using a high-field vibrating sample magnetometer. Here the results showed that the APC conductors have higher non-Cu J c at high fields (e.g. 32%–41% higher at 16 T) and simultaneously lower non-Cu Jc at low fields (e.g. 28%–34% lower at 1 T) compared with the RRP ® . This effect is due to a competition between their Nb3Sn layer fraction ratios and layer F p ratios. Suppose they reach the same 16 T non-Cu J c , then the 1 T non-Cu J c and magnetization of the APC conductors are only half or even less compared with the RRP ® conductor.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Stimulation of Dissimilatory Sulfate Reduction in Response to Sulfate in Microcosm Incubations from Two Contrasting Temperate Peatlands near Ithaca, NY, USA

Abstract Peatlands are responsible for over half of wetland methane emissions, yet major uncertainties remain regarding carbon flow, especially when increased availability of electron acceptors stimulate competing physiologies. We used microcosm incubations to study the effects of sulfate on microorganisms in two temperate peatlands, one bog and one fen. Three different electron donor treatments were used (13C-acetate, 13C-formate, and a mixture of 12C short-chain fatty acids) to elucidate the responses of sulfate-reducing bacteria (SRB) and methanogens to sulfate stimulation. Methane production was measured and metagenomic sequencing was performed, with only the heavy DNA fraction sequenced from treatments receiving 13C electron donors. Our data demonstrate stimulation of dissimilatory sulfate reduction in both sites, with contrasting community responses. In McLean Bog (MB), hydrogenotrophic Deltaproteobacteria and acetotrophic Peptococcaceae lineages of SRB were stimulated, as were lineages with unclassified dissimilatory sulfite reductases. In Michigan Hollow Fen (MHF), there was little stimulation of Peptococcaceae populations, and a small stimulation of Deltaproteobacteria SRB populations only in the presence of formate as electron donor. Sulfate stimulated an increase in relative abundance of reads for both oxidative and reductive sulfite reductases, suggesting stimulation of an internal sulfur cycle. Together, these data indicate a stimulation of SRB activity in response to sulfate in both sites, with a stronger growth response in MB than MHF. This study provides valuable insights into microbial community responses to sulfate in temperate peatlands and is an important first step to understanding how SRB and methanogens compete to regulate carbon flow in these systems.

Microbiology↗
DOE OSTI2026

Variance reduction in lattice QCD observables via normalizing flows

Normalizing flows can be used to construct unbiased, reduced-variance estimators for lattice field theory observables that are defined by a derivative with respect to action parameters. This work implements the approach for observables involving gluonic operator insertions in the SU(3) Yang-Mills theory and two-flavor QCD in four space-time dimensions. Variance reduction by factors of 10–60 is achieved in glueball correlation functions and in gluonic matrix elements related to hadron structure, with demonstrated computational advantages. The observed variance reduction is found to be approximately independent of the lattice volume, so volume transfer can be utilized to minimize training costs.

Abbott, Ryan [Columbia U.; MIT, Cambridge, CTP; IA↗
DOE OSTI2025

Reduction in Nuclear Size and Quadrupole Deformation of High-Spin Isomers of 127,129 In

We employed laser spectroscopy of atomic transitions to measure the nuclear charge radii and electromagnetic properties of the high-spin isomeric states in neutron-rich indium isotopes (𝑍 =49) near the closed proton and neutron shells at 𝑍 = 50 and 𝑁 = 82. Our data reveal a reduction in the nuclear charge radius and intrinsic quadrupole moment when protons and neutrons are fully aligned in 129 In ⁢(𝑁 = 80), to form the high spin isomer. Such a reduction is not observed in 127 In ⁢(𝑁 = 78), where more complex configurations can be formed by the existence of four neutron holes. These observations are not consistently described by nuclear theory.

high spin states↗
DOE OSTI2025

Combining quantum noise reduction resources: A practical approach

Optomechanical sensors are capable of transducing external perturbations to resolvable optical signals. A particular regime of interest is that of high-bandwidth force detection, where an impulse is delivered to the system over a short period of time. Exceedingly sensitive impulse detection has been proposed to observe very weak signals like those due to long-range interactions with dark matter that require much higher sensitivities than current sensors can provide. Quantum resources to go beyond the traditional standard quantum limit of these sensors include squeezing of the light used to transduce the signal, backaction evasion by measuring the optimal quadrature, and quantum nondemolition (QND) measurements that reduce backaction directly. These methods have been developed in the context of gravitational wave detection for target frequencies in the audio band range. Here, we provide the theoretical limits to quantum noise reduction for higher and broader frequency targets, such as those from dark matter signals, while combining quantum-enhanced readout techniques based on squeezed light and QND measurements with optomechanical sensors. Here, we demonstrate that backaction evasion through QND techniques dramatically reduces the technical challenges presented when using squeezed light for broadband force detection, paving the way for combining multiple quantum noise reduction techniques for enhanced sensitivity in the context of impulse metrology.

Optomechanics↗
DOE OSTI2026

Band-gap reduction and band alignments of dilute bismide III–V alloys

Adding a few atomic percent of Bi to III–V semiconductors leads to significant changes in their electronic structure and optical properties. Bismuth substitution on the pnictogen site leads to a large increase in spin-orbit splitting Δ SO at the top of the valence band (Γ 8⁢𝑣 −Γ 7⁢𝑣 ) and a large reduction in the band gap, creating unique opportunities in semiconductor device applications. Quantifying these changes is key to the design and simulation of electronic and optoelectronic devices. Using hybrid functional calculations, we predict the band gap of III–Vs (III = Al, Ga, In and V = As, Sb) with low concentrations of Bi (3.125% and 6.25%), the effects of adding Bi on the valence- and conduction-band edges, and the band offset between these dilute alloys and their III–V parent compounds. As expected, adding Bi raises the valence-band maximum (VBM). However, contrary to previous assumptions, the conduction-band minimum (CBM) is also significantly lowered, and both effects contribute to the sizable band-gap reduction. Changes in band gap and Δ SO are notably larger in the arsenides than in the antimonides. In conclusion, we also predict cases of band-gap inversion (Γ 6⁢𝑐 below Γ 8⁢𝑣 ), and Δ SO larger than the band gap, which are key parameters for designing topological materials and for minimizing losses due to Auger recombination in infrared lasers.

Saboor, Abdul [Univ. of Delaware, Newark, DE (Unit↗
DOE OSTI2024

BioXTAS RAW 2 : new developments for a free open-source program for small-angle scattering data reduction and analysis

BioXTAS RAW is a free open-source program for reduction, analysis and modelling of biological small-angle scattering data. Here, the new developments in RAW version 2 are described. These include improved data reduction using pyFAI ; updated automated Guinier fitting and D max finding algorithms; automated series ( e.g. size-exclusion chromatography coupled small-angle X-ray scattering or SEC-SAXS) buffer- and sample-region finding algorithms; linear and integral baseline correction for series; deconvolution of series data using regularized alternating least squares ( REGALS ); creation of electron-density reconstructions using electron density via solution scattering ( DENSS ); a comparison window showing residuals, ratios and statistical comparisons between profiles; and generation of PDF reports with summary plots and tables for all analysis. Furthermore, there is now a RAW API, which can be used without the graphical user interface (GUI), providing full access to all of the functionality found in the GUI. In addition to these new capabilities, RAW has undergone significant technical updates, such as adding Python 3 compatibility, and has entirely new documentation available both online and in the program.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Evaluating Nonuniform Reduction in HIP and SYCL on GPUs

Motivated by maturing programming models and portability for heterogeneous computing, we describe the challenges posed by hardware architectures and programming models when migrating an optimized implementation of nonuniform reduction from CUDA to HIP and SYCL. We explain the migration experience, evaluate the performance of the reduction on GPU -based computing platforms, and provide feedback on improving portability for the development of the SYCL programming model.

Jin, Zheming↗
DOE OSTI2021

Parallel String Graph Construction and Transitive Reduction for De Novo Genome Assembly

One of the most computationally intensive tasks in computational biology is de novo genome assembly, the decoding of the sequence of an unknown genome from redundant and erroneous short sequences. A common assembly paradigm identifies overlapping sequences, simplifies their layout, and creates consensus. Despite many algorithms developed in the literature, the efficient assembly of large genomes is still an open problem. In this work, we introduce new distributed-memory parallel algorithms for overlap detection and layout simplification steps of de novo genome assembly, and implement them in the diBELLA 2D pipeline. Our distributed memory algorithms for both overlap detection and layout simplification are based on linear-algebra operations over semirings using 2D distributed sparse matrices. Our layout step consists of performing a transitive reduction from the overlap graph to a string graph. We provide a detailed communication analysis of the main stages of our new algorithms. diBELLA 2D achieves near linear scaling with over 80% parallel efficiency for the human genome, reducing the runtime for overlap detection by 1.2-1.3× for the human genome and 1.5-1.9× for C.elegans compared to the state-of-the-art. Our transitive reduction algorithm outperforms an existing distributed-memory implementation by 10.5-13.3× for the human genome and 18-29× for the C. elegans. Our work paves the way for efficient de novo assembly of large genomes using long reads in distributed memory.

59 BASIC BIOLOGICAL SCIENCES↗