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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 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

DONKEY: A Flexible and Accurate Algorithm for Clustering

We propose an accurate clustering algorithm suitable for the varied and multidimensional data sets that correspond to temporal snapshots from on-the-fly nonadiabatic trajectory-based simulations of photoexcited dynamics. The algorithm approximates the underlying probability density function using variable kernel density estimation, with local maxima corresponding to cluster centers. Each data point is then assigned to one of the maxima by employing a maximization procedure. Finally, clusters artificially separated by minor fluctuations in the probability density are merged. The algorithm does not require parameter tuning, which ensures flexibility and reduces the risk of bias. It is tested on several synthetic data sets, where it consistently outperforms conventional clustering algorithms. As a final example, the algorithm is applied to the excited dynamics of the norbornadiene ⇌ quadricyclane (C 7 H 8 ) molecular photoswitch, demonstrating how distinct reaction pathways can be identified.

algorithms↗
DOE OSTI2017

Multireference Density Functional Theory with Generalized Auxiliary Systems for Ground and Excited States

To describe static correlation, we create a new approach to density functional theory (DFT), which uses a generalized auxiliary system that is of a different symmetry, such as particle number or spin, from that of the physical system. The total energy of the physical system consists of two parts: the energy of the auxiliary system, which is determined with a chosen density functional approximation (DFA), and the excitation energy from an approximate linear response theory that restores the symmetry to that of the physical system, thus rigorously leading to a multideterminant description of the physical system. The electron density of the physical system is different from that of the auxiliary system and is uniquely determined from the functional derivative of the total energy with respect to the external potential. Our energy functional is thus an implicit functional of the physical system density, but an explicit functional of the auxiliary system density. We demonstrate that the total energy minimum and stationary states, describing the ground and excited states of the physical system, can be obtained by a self-consistent optimization with respect to the explicit variable, the generalized Kohn–Sham noninteracting density matrix. We have developed the generalized optimized effective potential method for the self-consistent optimization. Among options of the auxiliary system and the associated linear response theory, reformulated versions of the particle–particle random phase approximation (pp-RPA) and the spin-flip time-dependent density functional theory (SF-TDDFT) are selected for illustration of principle. Numerical findings show that our multireference DFT successfully describes static correlation in bond dissociation and double bond rotation.

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

Generalized Optimized Effective Potential for Orbital Functionals and Self-Consistent Calculation of Random Phase Approximations

A new self-consistent procedure for calculating the total energy with an orbital-dependent density functional approximation (DFA), the generalized optimized effective potential (GOEP), is developed in the present work. The GOEP is a nonlocal Hermitian potential that delivers the sets of occupied and virtual orbitals and minimizes the total energy. The GOEP optimization leads to the same minimum as does the orbital optimization. The GOEP method is promising as an effective optimization approach for orbital-dependent functionals, as demonstrated for the self-consistent calculations of the random phase approximation (RPA) to the correlation functionals in the particle–hole (ph) and particle–particle (pp) channels. The results show that the accuracy in describing the weakly interacting van der Waals systems is significantly improved in the self-consistent calculations. In particular, the important single excitations contribution in non-self-consistent RPA calculations can be captured self-consistently through the GOEP optimization, leading to orbital renormalization, without using the single excitations in the energy functional.

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

Synergistic CuWO 4 /NiS 2 Binary Nanostructures for Efficient Photocatalytic Hydrogen Production

A viable and ecologically safe method for producing green hydrogen is photocatalytic hydrogen (H 2 ) production. However, the development of effective semiconductor materials with improved activity and long-term stability is still a major obstacle. Here, in this work, we report the development of a broadband-gap, UV light-responsive CuWO 4 /NiS 2 nanocomposite using simple hydrothermal and wet impregnation techniques. The hybrid systems, with photocatalytic presentation, were thoroughly assessed using spectroscopic, photophysical, and microscopic characterization methods. The optimized CuWO 4 /NiS 2 nanostructure demonstrated an impressive H 2 generation rate of 28.2 mmol h –1 g –1 under light irradiation, which is roughly 2.71 and 3.28 times greater than those of pristine CuWO 4 (10.4) and NiS 2 (8.6 mmol h -1 g (cat) -1 ), respectively. The enhanced presentation is ascribed to the synergetic interface between the coupled semiconductors, which facilitates the formation of a nanostructure. This arrangement inhibits electron–hole recombination and encourages effective charge carrier parting. Additionally, compared with pristine CuWO 4 and NiS 2 , respectively, the CuWO 4 /NiS 2 composite showed a noticeably greater photocurrent density. The developed nanostructured CuWO 4 /NiS 2 displayed a 28.6% quantum efficiency at 450 nm. These outcomes validate CuWO 4 /NiS 2 ’s significant aptitude as a non-noble-metal photocatalyst for the production of sustainable hydrogen.

CuWO4/NiS2↗
DOE OSTI2026

Ir–Ru Particles Enable Low-Loading Acidic Oxygen Evolution for Integrated Solar Devices

Integrated photoelectrochemical (PEC) devices for water splitting represent a compelling pathway for sustainable hydrogen production, directly converting solar energy into chemical fuels. While alkaline systems have achieved state-of-the-art solar-to-hydrogen (STH) efficiencies above 20% using earth-abundant catalysts, acidic PEC architectures provide unique advantages for compact device integration, fast proton transport, and stable operation under highly dynamic solar conditions. Proton-exchange membrane (PEM)-based configurations enable high current densities, low gas crossover, and rapid ionic response, making them especially well-suited for intermittent, bias-free PEC operation, despite alkaline electrolysis being more technologically mature. A critical limitation of acidic PEC systems remains, the oxygen evolution reaction (OER), which currently relies on scarce and costly iridium catalysts, restricting scalability. Here, in this study, we report a series of low-iridium mixed-metal oxide catalysts synthesized via a surfactant-assisted borohydride reduction method. An optimized Ir 0.5 Ru 0.5 O x catalyst exhibits exceptional intrinsic activity (>400 A g –1 Ir at 1.55 V vs RHE) in 0.1 M HClO 4 and maintains stable operation for over 10 days in an integrated PEC flow-cell. Sustained hydrogen production is achieved at 1.65 V with a total iridium loading of only 0.1 mg cm –2 , substantially below commercial PEM benchmarks. These results demonstrate a viable pathway toward scalable, high-performance acidic PEC hydrogen technologies.

Acidic electrolysis↗
DOE OSTI2025

Engineering Polyketide Stereocenters with Ketoreductase Domain Exchanges

Polyketide synthases (PKSs) are versatile biosynthetic megasynthases capable of producing a diverse range of natural products with many applications, including in pharmaceuticals. The stereochemical precision of PKSs makes them a powerful tool for engineering tailored, unnatural polyketides; however, modifying the stereocenters of a PKS product while maintaining production levels remains a significant challenge. In this study, we systematically tested and evaluated strategies for ketoreductase (KR) domain exchanges, the domain responsible for setting stereocenters of polyketide products. After first optimizing the method for KR exchanges, we then performed 44 KR domain exchanges on three different PKSs to obtain high production of all four stereoisomers in vivo. By testing both one- and two-module PKS systems, we investigated how downstream modules process intermediates with altered stereochemistry and found that the configuration of the α-substituents was critical for gatekeeping by the ketosynthase (KS). To overcome this constraint, we investigated two different strategies for altering the KS domain, including introducing targeted mutations in the downstream KS, and exploring boundaries in exchanging the entire functional unit from the donor PKS. Both strategies successfully modified the KS stereocontrol with distinct trade-offs; the functional unit exchange resulted in higher titer improvements, though it was more likely to break the entire PKS. This study demonstrates a comprehensive approach to successfully engineering all four stereochemical configurations in multiple PKS systems, advancing our understanding of and ability to rationally modify polyketide stereochemistry through multiple engineering strategies.

Keiser, Leah S. [Joint BioEnergy Institute (JBEI),↗
DOE OSTI2021

Projecting Permafrost Thaw of Sub-Arctic Tundra With a Thermodynamic Model Calibrated to Site Measurements

Northern circumpolar permafrost thaw affects global carbon cycling, as large amounts of stored soil carbon becomes accessible to microbial breakdown under a warming climate. The magnitude of carbon release is linked to the extent of permafrost thaw, which is locally variable and controlled by soil thermodynamics. Soil thermodynamic properties, such as thermal diffusivity, govern the reactivity of the soil-atmosphere thermal gradient, and are controlled by soil composition and drainage. In order to project permafrost thaw for an Alaskan tundra experimental site, we used seven years of site data to calibrate a soil thermodynamic model using a data assimilation technique. The model reproduced seasonal and interannual temperature dynamics for shallow (5–40 cm) and deep soil layers (2–4 m), and simulations of seasonal thaw depth closely matched observed data. The model was then used to project permafrost thaw at the site to the year 2100 using climate forcing data for three future climate scenarios (RCP 4.5, 6.0, and 8.5). Minimal permafrost thawing occurred until mean annual air temperatures rose above the freezing point, after which we measured over a 1 m increase in thaw depth for every 1 °C rise in mean annual air temperature. Under no projected warming scenario was permafrost remaining in the upper 3 m of soil by 2100. We demonstrated an effective data assimilation method that optimizes parameterization of a soil thermodynamic model. The sensitivity of local permafrost to climate warming illustrates the vulnerability of sub-Arctic tundra ecosystems to significant and rapid soil thawing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Performance efficient macromolecular mechanics via sub-nanometer shape based coarse graining

Dimensionality reduction via coarse grain modeling is a valuable tool in biomolecular research. For large assemblies, ultra coarse models are often knowledge-based, relying on a priori information to parameterize models thus hindering general predictive capability. Here, we present substantial advances to the shape based coarse graining (SBCG) method, which we refer to as SBCG2. SBCG2 utilizes a revitalized formulation of the topology representing network which makes high-granularity modeling possible, preserving atomistic details that maintain assembly characteristics. Further, we present a method of granularity selection based on charge density Fourier Shell Correlation and have additionally developed a refinement method to optimize, adjust and validate high-granularity models. We demonstrate our approach with the conical HIV-1 capsid and heteromultimeric cofilin-2 bound actin filaments. Our approach is available in the Visual Molecular Dynamics (VMD) software suite, and employs a CHARMM-compatible Hamiltonian that enables high-performance simulation in the GPU-resident NAMD3 molecular dynamics engine.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Understanding important features of deep learning models for segmentation of high-resolution transmission electron microscopy images

Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy. However, application to problems in materials science is often difficult since these complex models may have difficultly learning meaningful image features that would enable extension to new datasets. In situ electron microscopy provides a clear platform for utilizing automated image analysis. In this work, we consider the case of studying coarsening dynamics in supported nanoparticles, which is important for understanding, for example, the degradation of industrial catalysts. By systematically studying dataset preparation, neural network architecture, and accuracy evaluation, we describe important considerations in applying deep learning to physical applications, where generalizable and convincing models are required. With a focus on unique challenges that arise in high-resolution images, we propose methods for optimizing performance of image segmentation using convolutional neural networks, critically examining the application of complex deep learning models in favor of motivating intentional process design.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Enhancing the efficiency of time-dependent density functional theory calculations of dynamic response properties

X-ray Thomson scattering (XRTS) constitutes an essential technique for diagnosing material properties under extreme conditions, such as high pressures and intense laser heating. Time-dependent density functional theory (TDDFT) is one of the most accurate available ab initio methods for modeling XRTS spectra, as well as a host of other dynamic material properties. However, strong thermal excitations, along with the need to account for variations in temperature and density as well as the finite size of the detector significantly increase the computational cost of TDDFT simulations compared to ambient conditions. In this work, we present a broadly applicable method for optimizing and enhancing the efficiency of TDDFT calculations. Our approach is based on a one-to-one mapping between the dynamic structure factor and the imaginary time density–density correlation function, which naturally emerges in Feynman’s path integral formulation of quantum many-body theory. Specifically, we combine rigorous convergence tests in the imaginary time domain with a constraints-based attenuation of narrow-band fluctuations to improve the efficiency of TDDFT modeling without the introduction of any significant bias. As a result, we can report a speed-up by up to an order of magnitude, thus substantially reducing the burden of computational cost required for XRTS analysis.

Moldabekov, Zhandos A. [Helmholtz-Zentrum Dresden-↗
DOE OSTI2022

Programmable heating and quenching for efficient thermochemical synthesis

Conventional thermochemical syntheses by continuous heating under near-equilibrium conditions face critical challenges in improving the synthesis rate, selectivity, catalyst stability and energy efficiency, owing to the lack of temporal control over the reaction temperature and time, and thus the reaction pathways. As an alternative, we present a non-equilibrium, continuous synthesis technique that uses pulsed heating and quenching (for example, 0.02 s on, 1.08 s off) using a programmable electric current to rapidly switch the reaction between high (for example, up to 2,400 K) and low temperatures. The rapid quenching ensures high selectivity and good catalyst stability, as well as lowers the average temperature to reduce the energy cost. Using CH4 pyrolysis as a model reaction, our programmable heating and quenching technique leads to high selectivity to value-added C2 products (>75% versus <35% by the conventional non-catalytic method and versus <60% by most conventional methods using optimized catalysts). Our technique can be extended to a range of thermochemical reactions, such as NH 3 synthesis, for which we achieve a stable and high synthesis rate of about 6,000 μmol g Fe –1 h –1 at ambient pressure for >100 h using a non-optimized catalyst. Furthermore, this study establishes a new model towards highly efficient non-equilibrium thermochemical synthesis.

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

Heterogeneous Catalytic Oxidation of Amides to Imides by Manganese Oxides

Herein, we report a one-step peroxide mediated heterogeneous catalytic oxidation of amides to imides utilizing a series of manganese oxides. Among them, Cs/Mn 2 O 3 was found to be the most active catalyst for the selective partial oxidation of N-benzylbenzamide to diphenyl imide. We have been able to apply an optimized oxidation method to other aromatic substrates. The feasibility of using air as an oxidant, the heterogeneous nature, inexpensive catalytic materials, respectable turnover numbers, and chemoselectivity to imides make this methodology an attractive choice for functional group transformations of amides to imides.

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

Transverse oscillating bubble enhanced laser-driven betatron X-ray radiation generation

Abstract Ultrafast high-brightness X-ray pulses have proven invaluable for a broad range of research. Such pulses are typically generated via synchrotron emission from relativistic electron bunches using large-scale facilities. Recently, significantly more compact X-ray sources based on laser-wakefield accelerated (LWFA) electron beams have been demonstrated. In particular, laser-driven sources, where the radiation is generated by transverse oscillations of electrons within the plasma accelerator structure (so-called betatron oscillations) can generate highly-brilliant ultrashort X-ray pulses using a comparably simple setup. Here, we experimentally demonstrate a method to markedly enhance the parameters of LWFA-driven betatron X-ray emission in a proof-of-principle experiment. We show a significant increase in the number of generated photons by specifically manipulating the amplitude of the betatron oscillations by using our novel Transverse Oscillating Bubble Enhanced Betatron Radiation scheme. We realize this through an orchestrated evolution of the temporal laser pulse shape and the accelerating plasma structure. This leads to controlled off-axis injection of electrons that perform large-amplitude collective transverse betatron oscillations, resulting in increased radiation emission. Our concept holds the promise for a method to optimize the X-ray parameters for specific applications, such as time-resolved investigations with spatial and temporal atomic resolution or advanced high-resolution imaging modalities, and the generation of X-ray beams with even higher peak and average brightness.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Uncertainty-aware mixed-variable machine learning for materials design

Abstract Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods. Bayesian optimization (BO) employs uncertainty-aware machine learning models to select promising designs to evaluate, hence reducing the cost. However, BO with mixed numerical and categorical variables, which is of particular interest in materials design, has not been well studied. In this work, we survey frequentist and Bayesian approaches to uncertainty quantification of machine learning with mixed variables. We then conduct a systematic comparative study of their performances in BO using a popular representative model from each group, the random forest-based Lolo model (frequentist) and the latent variable Gaussian process model (Bayesian). We examine the efficacy of the two models in the optimization of mathematical functions, as well as properties of structural and functional materials, where we observe performance differences as related to problem dimensionality and complexity. By investigating the machine learning models’ predictive and uncertainty estimation capabilities, we provide interpretations of the observed performance differences. Our results provide practical guidance on choosing between frequentist and Bayesian uncertainty-aware machine learning models for mixed-variable BO in materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Enhanced Noble Gas Adsorption in Ag@MOF-74Ni

Various amounts of Ag nanoparticles were successfully deposited in porous MOF-74Ni (or Ni/DOBDC) with an auto-reduction method. An optimized silver-loaded MOF-74Ni was shown to have an improved Xe adsorption capacity (15% more) at STP compared to the MOF without silver nanoparticles. The silver-loaded sample also has a higher Xe/Kr selectivity. These results are explained by the stronger interactions between polarizable Xe molecules and the well-dispersed Ag nanoparticles.

Liu, Jian↗
DOE OSTI2017

Polythiophene coated aromatic polyimide enabled ultrafast and sustainable lithium ion batteries

Organic composite electrode materials based on aromatic polyimide (PI) and electron conductive polythiophene (PT) have been prepared by a facile in situ chemical oxidation polymerization method. The optimized composite electrode PI30PT delivers a remarkable high-rate cyclability, achieving a high capacity of 89.6 mA h g -1 at 20 C with capacity retention of 94% after 1000 cycles.

25 ENERGY STORAGE↗
DOE OSTI2022

Mapping the effects of physical and chemical reduction parameters on local atomic distributions within bimetallic nanoparticles

Bimetallic nanoparticles prove advantageous over their monometallic counterparts due to the tunable, hybrid properties that result from combining different atomic species in a controlled way. The favorable optical and catalytic properties resulting from AgAu nanoparticle formation have been widely attributed to the existence of Ag–Au bonds, the maximization of which assumes the formation of a homogeneous alloy. Despite the importance of atomic scale structure in these systems, synthetic studies are typically not paired with structural characterization at the atomic scale. Herein, a comprehensive synthetic exploration of physical and chemical reduction parameters of resulting nanoparticle products is complemented with thorough X-ray characterization to probe how these parameters affect atomic scale alloy distributions within AgAu nanoparticles. Presented evidence shows Ag is substantially underincorporated into nanoparticle constructs compared with solution Ag : Au ratios regardless of precursor :reductant ratio or volume of reductant added. Both Ag and Au exhibit significant local clustering, with Ag distributed preferentially towards the nanoparticle surface. Most significantly, the results of this investigation suggest that reduction parameters alone can affect the local alloy distributions and homogeneity within bimetallic nanoparticles, even when the ratio of metallic precursors remains constant. Overall, this investigation presents the ability to control alloy distributions using kinetics and provides new considerations for optimizing synthetic methods to produce functional bimetallic nanoparticles.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Chemoselective, osmium-free, dihydroxylation/oxidative cleavage of heteroaryl isoprenes by a contemporary Malaprade reaction

The methyl ketone is a central synthetic building block for the construction of advanced heteroaryl scaffolds and systems. Reactions, including oxidative cyclization strategies, are often predicated on efficient access to this ubiquitous moiety. In the context of arenes, standard approaches leveraging Markovnikov hydration/oxidation or oxidative cleavage of the C–C π bond often afford satisfactory performance. However, when the substrate contains an electron-deficient heteroaryl core, the traditional Malaprade reaction, and related oxidative-cleavage strategies, frequently result in diminished performance over carbon-based arenes. In this work we present the development and application of an oxidative cleavage reaction of various pyridinyl isoprenes towards accessing the downstream methyl ketone for utilization in advanced cyclizations for the preparation of soft-N-donor complexant scaffolds. Further, this efficient protocol parallels the principles of Green chemistry by exchanging KMnO 4 for the toxic OsO 4 and offers the end-user an efficient, more environmentally friendly option for accessing heteroaryl methyl ketones in one hour of reaction time using potassium permanganate and sodium paraperiodate as a synergistically potent oxidative cleavage system. The wide substrate scope defined access to simple, as well as advanced heteroaryl methyl ketones. Method development, optimization, substrate scope, preliminary mechanistic observations, and a scale up reaction are delineated herein.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗