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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 OSTI2019

Clustering representative days for power systems generation expansion planning: Capturing the effects of variable renewables and energy storage

Decision makers rely on models to make important regulatory, policy, and investment decisions. For power systems, these models must capture (i) the future challenges introduced by the integration of large quantities of variable renewable energy sources and (ii) the role that energy storage technologies should play. In this paper, we explore several different approaches to selecting representative days for generation expansion planning models, focusing on capturing these dynamics. Further, we propose a new methodology for adjusting the outputs of clustering algorithms that provides three advantages: the targeted level of net demand is captured, the underlying net demand shapes that define ramping challenges are accurately represented, and the relationship between annual energy and peak demand is captured. This weighting methodology reduces the magnitude of the error in the representative day based generation expansion planning models estimation of costs by 61% on average. The results also demonstrate the importance of carefully performing the clustering of representative days for both system costs and technology mix. In most cases improvements to the total cost of different representative day based expansion plans are realised where conventional generation capacity is substituted for energy storage. Based on the energy storage technology selected we conclude this capacity is being used to address ramping challenges as opposed to shifting renewable generation from off to on peak periods, reinforcing the importance of capturing detailed intraday dynamics in the representative day selection process.

Clustering↗
DOE OSTI2026

Pre- and post-coppice production and biomass chemistry of eastern cottonwood and hybrid poplars in the southeastern US

Sustainably grown feedstocks for bioenergy and bioproducts are important tools to fight climate change, provide ecosystem services, and sequester carbon. Populus species including hybrid poplars have been utilized around the world, and in the southeastern US native eastern cottonwood (P. deltoides) is often favored due to its resistance to stem cankers. However, P. deltoides has historically been grown in single stem production and its relative performance in short rotation coppice production, prevalent in Europe, is not well documented. Therefore, this study's goals were to evaluate eastern cottonwood (D × D) and hybrid poplar (P. deltoides × P. maximowiczii (D × M) and P. deltoides × P. trichocarpa (D × T) taxa) productivity under a two-year establishment and two-year coppice cycle, analyze biomass chemistry properties, identify drivers of productivity, and select clones suited for marginal vs. optimal sites. We found that D × M and D × D grew best in both establishment and coppice rotation with production increasing after coppice. Taxa and clones exhibited few differences in select biomass chemistry properties despite differences in growth rate and stem size. Across years and sites, production was correlated with the soil's carbon to nitrogen ratio, percentage of sand, and shrink-swell potential of the soil. A D × M clone ‘9709’ tended to perform better than other tested clones on marginal sites and had the highest survival rate after four years. Overall, this research suggests that Populus species can be grown in coppice rotation in the southeastern US with the potential for hybrid poplars to be successful on certain sites in the region although longer term studies are necessary to confirm these results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Significance and complications of drilling fluid rheology in geothermal drilling: A review

The harsh downhole conditions of high pressure and high temperature (HPHT) encountered in geothermal wells make the drilling operation challenging. Drilling in such environments requires a special drilling mud formulation with high thermal stability and good rheological properties to fulfill the drilling fluid functions. Therefore, great efforts should be put into selecting the suitable drilling fluid, optimize and monitor the drilling fluid properties throughout drilling operations, and predicting its performance under downhole conditions. Rheological properties significantly impact many drilling parameters such as hole cleaning, fluid and wellbore stability, wellbore hydraulics, torque and drag, and other drilling issues. This paper discusses water-based drilling fluids' flow behavior under HPHT conditions and highlights the significance of fluid rheology in geothermal drilling. The common challenges and complications related to fluid rheology encountered in geothermal drilling are addressed in this paper, such as hole cleaning, wellbore hydraulics, and drilling fluid stability. This article also reviews the recent advances in drilling mud systems, rheology enhancement, and rheological properties measurements at surface and subsurface conditions. Furthermore, the rheology models of drilling fluid at elevated temperatures are reviewed to fully understand their flow behavior and establish a method for drilling engineers to optimize fluid formulations for geothermal drilling.

58 GEOSCIENCES↗
DOE OSTI2025

Comparative life cycle assessment of a modular cross-laminated timber residential building designed for disassembly and reuse versus traditional wood frame construction

There is a need for affordable housing across the U.S., with high-performance modular and prefabricated buildings providing a logical avenue for meeting some of this demand. However, there is a need to balance high performance construction – including low emissions – with affordability. To provide a proof-of-concept in meeting these goals, the Circular Home is a cross-laminated timber (CLT)-based deconstructible and reconfigurable single-family residence that meets high performance targets in moisture, energy, design, economics, and life cycle assessment (LCA). This study focuses on the LCA, presenting a cradle-to-cradle whole-building life cycle assessment (WBLCA) for the Circular Home and a functionally equivalent Baseline Home constructed with traditional materials and methods. The functional unit is 1 m 2 of gross floor area across 60 years. Revit building information models (BIM) provided material quantities and Tally LCA was utilized for impact data (inclusive of biogenic carbon sequestration), supplemented with manufacturer environmental product declarations (EPDs). The Circular Home outperforms the baseline residence in most measured impact categories, including global warming potential (GWP), producing −2.73 kgCO 2 eq/m 2 in embodied emissions, whereas the modeled baseline has an embodied GWP of 428 kgCO 2 eq/m 2 . The careful material selection and advanced building design optimizes performance, with the Circular Home containing only −0.006 times the embodied emissions and −0.02 times the operational emissions of its traditional counterpart. Finally, the unique contribution of this work is in the environmental impact comparison of a high-performance modular CLT structure that can be affordably scaled and mass produced in a U.S. market, compared to typical single family home construction.

Circularity↗
DOE OSTI2021

An effective human uracil-DNA glycosylase inhibitor targets the open pre-catalytic active site conformation

Human uracil DNA-glycosylase (UDG) is the prototypic and first identified DNA glycosylase with a vital role in removing deaminated cytosine and incorporated uracil and 5-fluorouracil (5-FU) from DNA. UDG depletion sensitizes cells to high APOBEC3B deaminase and to pemetrexed (PEM) and floxuridine (5-FdU), which are toxic to tumor cells through incorporation of uracil and 5-FU into DNA. To identify small-molecule UDG inhibitors for pre-clinical evaluation, we optimized biochemical screening of a selected diversity collection of >3,000 small-molecules. We found aurintricarboxylic acid (ATA) as an inhibitor of purified UDG at an initial calculated IC 50 < 100 nM. Subsequent enzymatic assays confirmed effective ATA inhibition but with an IC50 of 700 nM and showed direct binding to the human UDG with a K D of <700 nM. ATA displays preferential, dose-dependent binding to purified human UDG compared to human 8-oxoguanine DNA glycosylase. ATA did not bind uracil-containing DNA at these concentrations. Yet, combined crystal structure and in silico docking results unveil ATA interactions with the DNA binding channel and uracil-binding pocket in an open, destabilized UDG conformation. Biologically relevant ATA inhibition of UDG was measured in cell lysates from human DLD1 colon cancer cells and in MCF-7 breast cancer cells using a host cell reactivation assay. Collective findings provide proof-of-principle for development of an ATA-based chemotype and “door stopper” strategy targeting inhibitor binding to a destabilized, open pre-catalytic glycosylase conformation that prevents active site closing for functional DNA binding and nucleotide flipping needed to excise altered bases in DNA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Elucidating the Role of Carbon Conductive Additive in the Processing and Electrochemical Behavior of Surface-Modified Si Anodes

In the present work, we explore the impact of conducting carbon on the processing of silicon (Si)-rich electrodes, their resulting cycling performance, and parasitic side reactions. We employed three different carbon additives, Super C45 (carbon black), Super C65 (carbon black), and TIMCAL C-NERGY KS 6L (graphite), with varying sizes and morphologies to determine how these parameters influence the structure and the electrochemical behavior of the cast electrodes. Raman mapping indicates that the improved performance achieved using C45 can be explained by the homogeneous distribution of Si and carbon, enabling the formation of continuous electrical pathways throughout the electrode. Further, this finding aligns with the results of ζ potential measurements, which indicate that C45 can maintain more stable dispersions in N-methyl-2-pyrrolidone (NMP) compared with the other carbon additives. However, electrodes with C45 as the conducting agent exhibited increased parasitic side reactions, as evidenced by the leakage current obtained from voltage hold experiments. These side reactions can have adverse effects on the calendar life of the electrode. Furthermore, the Raman maps of the electrodes reveal heterogeneities in Si and carbon distribution with lower carbon content, especially with only 2% carbon. This drastically impacts the cyclability of the electrodes, with portions of the low-carbon electrodes that remain unable to undergo cycling even at slow rates (0.2 Ah g –1 ). Additionally, we found that slower formation rates are favored in the context of parasitic side reactions, although slower formation rates can potentially create thicker passivating films that could hinder Li + -ion transport. Therefore, we emphasize the need for an intricate balance in materials selection and cycling protocol for optimal electrode processing and electrochemical behavior.

25 ENERGY STORAGE↗
DOE OSTI2025

Dynamical Ion Correlations in Polymer and Membrane Electrolytes: Recent Advances and Open Questions

Ion transport in polymer and membrane electrolytes is often described using uncorrelated models like the Nernst–Einstein (NE) approximation, which link conductivity, transference number, and salt diffusivity to individual ion mobilities. However, growing evidence shows that dynamical ion correlations─arising from ion pairing, polymer-mediated coupling, confinement, and electrostatic interactions─can substantially alter macroscopic transport properties. The Onsager transport formalism offers a rigorous framework to quantify these effects via cross-correlated ionic displacements. This Perspective highlights how such correlations manifest across dry and hydrated systems, including salt-doped polymers, block copolymers, hydrogels, and ion-exchange membranes. Here, we examine key physical origins, the influence of salt chemistry and polymer structure, and open questions in measuring and controlling correlated motion. Embracing correlation-aware design enables new pathways to optimize conductivity, transference number, and selectivity─shifting the focus from individual ion mobility to engineered collective dynamics for next-generation electrolytes in energy storage and separations.

Charge transport↗
DOE OSTI2020

Interactions of Oxide Surfaces with Water Revealed with Solid-State NMR Spectroscopy

Hydrous materials are ubiquitous in the natural environment and efforts have previously been made to investigate the structures and dynamics of hydrated surfaces for their key roles in various chemical and physical applications, with the help of theoretical modeling and microscopy techniques. However, an overall atomic-scale understanding of the water–solid interface, including the effect of water on surface ions, is still lacking. Herein, we employ ceria nanorods with different amounts of water as an example and demonstrate a new approach to explore the water–surface interactions by using solid-state NMR in combination with density functional theory. NMR shifts and relaxation time analysis provide detailed information on the local structure of oxygen ions and the nature of water motion on the surface: the amount of molecularly adsorbed water decreases rapidly with increasing temperature (from room temperature to 150 °C), whereas hydroxyl groups are stable up to 150 °C, and dynamic water molecules are found to instantaneously coordinate to the surface oxygen ions. Additionally, the applicability of dynamic nuclear polarization for selective detection of surface oxygen species is also compared to conventional NMR with surface selective isotopic-labeling: the optimal method depends on the feasibility of enrichment and the concentration of protons in the sample. These results provide new insight into the interfacial structure of hydrated oxide nanostructures, which is important to improve performance for various applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

What Makes Au Nanospheres Superior to Octahedral and Cubic Counterparts for the Deposition of a Pt Monolayer Shell?

This study demonstrates that Au nanospheres are advantageous over their octahedral and cubic counterparts as seeds in the synthesis of Au@Pt core−shell nanocrystals with a monolayer shell. In combination with experimental characterization, we show through training a machine-learned interatomic potential that the Au nanospheres exhibit a large fraction of lowcoordination atoms which are uniformly distributed over the surface. The corresponding high-index facets, including {211}, {311}, {331}, {210}, and {310}, on a spherical seed promote nucleation while greatly shortening the diffusion distance for adatoms. In addition, the high-index facets are instrumental in retaining the deposited Pt atoms on the outermost surface by retarding their inter-diffusional exchange with the underlying Au atoms. By switching from a monolayer made of pure Pt to those made of Pt−Au alloys, we can optimize both the activity and selectivity of the nanocrystals toward the two-electron oxygen reduction reaction for the electrochemical synthesis of H 2 O 2 . This method should be extendible to the fabrication of other core−shell nanocatalysts with desired monolayer shells for various catalytic reactions.

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

Semiconductor-based Multilayer Selective Solar Absorber for Unconcentrated Solar Thermal Energy Conversion

Solar thermal energy conversion has attracted substantial renewed interest due to its applications in industrial heating, air conditioning, and electricity generation. Achieving stagnation temperatures exceeding 200 °C, pertinent to these technologies, with unconcentrated sunlight requires spectrally selective absorbers with exceptionally low emissivity in the thermal wavelength range and high visible absorptivity for the solar spectrum. In this Communication, we then report a semiconductor-based multilayer selective absorber that exploits the sharp drop in optical absorption at the bandgap energy to achieve a measured absorptance of 76% at solar wavelengths and a low emittance of approximately 5% at thermal wavelengths. In field tests, we obtain a peak temperature of 225 °C, comparable to that achieved with state-of-the-art selective surfaces. Furthemore, with straightforward optimization to improve solar absorption, our work shows the potential for unconcentrated solar thermal systems to reach stagnation temperatures exceeding 300 °C, thereby eliminating the need for solar concentrators for mid-temperature solar applications such as supplying process heat

14 SOLAR ENERGY↗
DOE OSTI2019

Understanding cation effects in electrochemical CO 2 reduction

Solid–liquid interface engineering has recently emerged as a promising technique to optimize the activity and product selectivity of the electrochemical reduction of CO 2 . In particular, the cation identity and the interfacial electric field have been shown to have a particularly significant impact on the activity of desired products. Using a combination of theoretical and experimental investigations, we show the cation size and its resultant impact on the interfacial electric field to be the critical factor behind the ion specificity of electrochemical CO 2 reduction. We present a multi-scale modeling approach that combines size-modified Poisson–Boltzmann theory with ab initio simulations of field effects on critical reaction intermediates. The model shows an unprecedented quantitative agreement with experimental trends in cation effects on CO production on Ag, C 2 production on Cu, CO vibrational signatures on Pt and Cu as well as Au(111) single crystal experimental double layer capacitances. The insights obtained represent quantitative evidence for the impact of cations on the interfacial electric field. Lastly, we present design principles to increase the activity and selectivity of any field-sensitive electrochemical process based on the surface charging properties: the potential of zero charge, the ion size, and the double layer capacitance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

The constrained architecture of mammalian Hox gene clusters

In many animal species with a bilateral symmetry, Hox genes are clustered either at one or at several genomic loci. This organization has a functional relevance, as the transcriptional control applied to each gene depends upon its relative position within the gene cluster. It was previously noted that vertebrate Hox clusters display a much higher level of genomic organization than their invertebrate counterparts. The former are always more compact than the latter, they are generally devoid of repeats and of interspersed genes, and all genes are transcribed by the same DNA strand, suggesting that particular factors constrained these clusters toward a tighter structure during the evolution of the vertebrate lineage. Here, we investigate the importance of uniform transcriptional orientation by engineering several alleles within the HoxD cluster, such as to invert one or several transcription units, with or without a neighboring CTCF site. We observe that the association between the tight structure of mammalian Hox clusters and their regulation makes inversions likely detrimental to the proper implementation of this complex genetic system. We propose that the consolidation of Hox clusters in vertebrates, including transcriptional polarity, evolved in conjunction with the emergence of global gene regulation via the flanking regulatory landscapes, to optimize a coordinated response of selected subsets of target genes in cis .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Mechanical characterization of Bi-2212 composite winding pack samples for high-field superconducting magnet design

Bi₂Sr₂CaCu₂O8−x (Bi-2212) multi-filament round wire is a high-temperature superconductor (HTS) capable of carrying high transport currents, which makes it suitable for high-field magnet applications. However, its weak Ag–Mg sheath leaves it vulnerable to mechanical stress, posing challenges for high-field magnet design. To better understand and improve mechanical stress management in Bi-2212 winding packs, we conducted an experimental study evaluating the axial stress–strain behavior of five winding pack configurations with varying insulation materials, reinforcement strategies, and construction quality. Using uniaxial tensile testing at 77 K, we measured Young’s modulus and Poisson’s ratio for each composition. Our results show that pure alumina braid insulation and co-wind reinforcements significantly enhance stiffness compared to aluminosilicate braids, with more than 2.5 times increased winding pack Young’s modulus. Rule of mixtures analysis further quantified the contribution of non-wire composite components to overall stiffness. These findings highlight the critical role of insulation material selection and reinforcement design in optimizing Bi-2212 coil performance under stress, providing a foundation for improved mechanical models and more reliable high-field HTS magnet designs.

Bi-2212 magnet↗
DOE OSTI2020

Machine Learning for Searching the Dark Energy Survey for Trans-Neptunian Objects

In this paper we investigate how implementing machine learning could improve the efficiency of the search for Trans-Neptunian Objects (TNOs) within Dark Energy Survey (DES) data when used alongside orbit fitting. The discovery of multiple TNOs that appear to show a similarity in their orbital parameters has led to the suggestion that one or more undetected planets, an as yet undiscovered “Planet 9”, may be present in the outer solar system. DES is well placed to detect such a planet and has already been used to discover many other TNOs. Here, we perform tests on eight different supervised machine learning algorithms, using a data set consisting of simulated TNOs buried within real DES noise data. We found that the best performing classifier was the Random Forest which, when optimized, performed well at detecting the rare objects. We achieve an area under the receiver operating characteristic (ROC) curve, (AUC) = 0.996 ± 0.001. After optimizing the decision threshold of the Random Forest, we achieve a recall of 0.96 while maintaining a precision of 0.80. Finally, by using the optimized classifier to pre-select objects, we are able to run the orbit-fitting stage of our detection pipeline five times faster.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

EIC Detector Overview

The Electron Ion Collider will have two interaction regions that can be instrumented with detectors. Here, the first region will be instrumented as part of the project and needs to be capable of delivering the physics that has been outlined by the National Academy of Sciences and ready at the start of beam commissioning near the end of this decade. Plans for a second complementary detector to be located at a second interaction region are already in progress and will hopefully come to fruition just few years after the first detector comes online. While the basic parameters of these detectors are being selected using conventional approaches, the optimization of the detectors is already being enhanced by making use of advanced optimization techniques.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Strain-engineered optoelectronic properties of 2D transition metal dichalcogenide lateral heterostructures

Compared with their bulk counterparts, 2D materials can sustain much higher elastic strain at which optical quantities such as bandgaps and absorption spectra governing optoelectronic device performance can be modified with relative ease. Using first-principles density functional theory and quasiparticle GW calculations, we demonstrate how uniaxial tensile strain can be utilized to optimize the electronic and optical properties of transition metal dichalcogenide lateral (in-plane) heterostructures such as MoX 2 /WX 2 (X = S, Se, Te). We find that these lateral-type heterostructures may facilitate efficient electron–hole separation for light detection/harvesting and preserve their type II characteristic up to 12% of uniaxial strain. Based on the strain-dependent bandgap and band offset, we show that uniaxial tensile strain can significantly increase the power conversion efficiency of these lateral heterostructures. Our results suggest that these strain-engineered lateral heterostructures are promising for optimizing optoelectronic device performance by selectively tuning the energetics of the bandgap.

2D materials↗
DOE OSTI2026

Enhancing dimensionality prediction in hybrid metal halides via feature engineering and class-imbalance mitigation

We present a machine learning (ML) framework for predicting the structural dimensionality of hybrid metal halides (HMHs), including organic-inorganic perovskites, using a combination of chemically-informed feature engineering and advanced class-imbalance handling techniques. This study is motivated by the small and highly imbalanced nature of experimentally available HMH datasets, which limits the applicability and reliability of conventional ML approaches. The dataset, consisting of 494 HMH structures, is highly imbalanced across dimensionality classes (0D, 1D, 2D, 3D), posing significant challenges to predictive modeling. To mitigate this limitation, the dataset was augmented to 1336 samples using the synthetic minority oversampling technique, enabling improved learning of underrepresented dimensionality classes while preserving chemically meaningful feature relationships. We developed interaction-based descriptors designed to capture coupled steric and polarity effects relevant to dimensionality prediction, which are not readily captured by standard single-parameter or composition-only descriptors. These descriptors are integrated into a multi-stage workflow combining feature selection, ensemble stacking, and performance optimization. Our approach significantly improves F1-scores for underrepresented classes, achieving robust cross-validation performance across all dimensionalities. This work demonstrates a generalizable strategy for extracting reliable and interpretable structure–dimensionality relationships from limited experimental data, enabling pre-synthesis screening of organic cations and providing a practical blueprint for small-data ML in hybrid materials systems.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Evaluating performance and portability of high-level programming models: Julia, Python/Numba, and Kokkos on exascale nodes

We explore the performance and portability of the high-level programming models: the LLVM-based Julia and Python/Numba, and Kokkos on high-performance computing (HPC) nodes: AMD Epyc CPUs and MI250X graphical processing units (GPUs) on Frontier’s test bed Crusher system and Ampere’s Arm-based CPUs and NVIDIA’s A100 GPUs on the Wombat system at the Oak Ridge Leadership Computing Facilities. We compare the default performance of a hand-rolled dense matrix multiplication algorithm on CPUs against vendor-compiled C/OpenMP implementations, and on each GPU against CUDA and HIP. Rather than focusing on the kernel optimization per-se, we select this naive approach to resemble exploratory work in science and as a lower-bound for performance to isolate the effect of each programming model. Julia and Kokkos perform comparably with C/OpenMP on CPUs, while Julia implementations are competitive with CUDA and HIP on GPUs. Performance gaps are identified on NVIDIA A100 GPUs for Julia’s single precision and Kokkos, and for Python/Numba in all scenarios. We also comment on half-precision support, productivity, performance portability metrics, and platform readiness. We expect to contribute to the understanding and direction for high-level, high-productivity languages in HPC as the first-generation exascale systems are deployed.

Godoy, William↗