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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Localizing Transformations of the Galaxy-Galaxy Lensing Observable

Modern cosmological analyses of galaxy-galaxy lensing face a theoretical systematic effect arising from the nonlocality of the observed galaxy-galaxy lensing signal. Because the predicted tangential shear signal at a given separation depends on the physical modeling on all scales internal to that separation, systematic uncertainties in the modeling of nonlinear small scales are propagated outward to larger scales. Even in the absence of other limiting factors, this systematic effect alone can necessitate conservative small-scale cuts, resulting in significant losses of information in the tangential shear data vector. We develop a simple linear transformation of the standard galaxy-galaxy observable that removes this nonlocality, which ensures that the cosmological signal contained within the transformed observable is exclusively drawn from well-understood physical scales. This new observable, through its robustness against nonlocality, also enables a significant extension in the range of usable scales in galaxy-galaxy lensing compared to the standard approach in current cosmological analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Cell-Free Gene Expression: Methods and Applications

Cell-free gene expression (CFE) systems empower synthetic biologists to build biological molecules and processes outside of living intact cells. The foundational principle is that precise, complex biomolecular transformations can be conducted in purified enzyme or crude cell lysate systems. This concept circumvents mechanisms that have evolved to facilitate species survival, bypasses limitations on molecular transport across the cell wall, and provides a significant departure from traditional, cell-based processes that rely on microscopic cellular “reactors.” In addition, cell-free systems are inherently distributable through freeze-drying, which allows simple distribution before rehydration at the point-of-use. Furthermore, as cell-free systems are nonliving, they provide built-in safeguards for biocontainment without the constraints attendant on genetically modified organisms. These features have led to a significant increase in the development and use of CFE systems over the past two decades. Here, we discuss recent advances in CFE systems and highlight how they are transforming efforts to build cells, control genetic networks, and manufacture biobased products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Quantification of High-Temperature Transition Al2O3 and Their Phase Transformations

High temperature exposure of ?-Al2O3 can lead to a series of polymorphic transformations, including the formation of ?-Al2O3 and ?-Al2O3. Quantification of the microstructure in the ?/?-Al2O3 formation range represents a formidable challenge as both phases accommodate a high degree of structural disorder. In this work, we explore the use of XRD recursive stacking formalism for quantification of high temperature transition aluminas. We formulate the recursive stacking methodology for modelling of disorder in ?-Al2O3 and twinning in ?-Al2O3 and show that explicitly accounting for the disorder is necessary to reliably model the XRD patterns of high temperature transition alumina. In the second part, we use the recursive stacking approach to study phase transformation during high temperature (1050 ºC) treatment. We show that the two different intergrowth modes of ?-Al2O3 have different transformation characteristics, and that a significant portion of ?-Al2O3 is stabilized with ?-Al2O3 even after prolonged high-temperature exposures. In discussions, we outline the limitation of the current XRD approach and discuss a possible multimodal XRD and NMR approach which can improve analysis of complex transition aluminas. This work was performed in the Wiley Environmental Molecular Sciences Laboratory (EMSL), a national scientific user facility sponsored by DOEs Office of Biological and Environmental Research and located at PNNL. The work was supported by the U.S. Department of Energy (DOE), Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences, and Biosciences.

Kovarik, Libor↗
DOE OSTI2020

Microbial processing of plant remains is co-limited by multiple nutrients in global grasslands

Microbial processing of aggregate-unprotected organic matter inputs is key for soil fertility, long-term ecosystem carbon and nutrient sequestration and sustainable agriculture. In this work, we investigated the effects of adding multiple nutrients (nitrogen, phosphorus and potassium plus nine essential macro- and micro-nutrients) on decomposition and biochemical transformation of standard plant materials buried in 21 grasslands from four continents. Addition of multiple nutrients weakly but consistently increased decomposition and biochemical transformation of plant remains during the peak-season, concurrent with changes in microbial exoenzymatic activity. Higher mean annual precipitation and lower mean annual temperature were the main climatic drivers of higher decomposition rates, while biochemical transformation of plant remains was negatively related to temperature of the wettest quarter. Nutrients enhanced decomposition most at cool, high rainfall sites, indicating that in a warmer and drier future fertilized grassland soils will have an even more limited potential for microbial processing of plant remains.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

BioTransformer 3.0 – A Web Server for Accurately Predicting Metabolic Transformation Products

BioTransformer 3.0 is a freely available web server that supports accurate, rapid and comprehensive in silico metabolism prediction. It combines machine learning approaches with a rule-based system to predict small-molecule metabolism in human tissues, the human gut as well as the external environment (soil and water microbiota). Simply stated, BioTransformer takes a molecular structure as input (SMILES or SDF) and outputs an interactively viewable/sortable table of the predicted metabolites or transformation products (SMILES, PNG images) along with the enzymes that are predicted to be responsible for those reactions and richly annotated downloadable files (CSV and JSON). The entire process typically takes a few seconds. Previous versions of BioTransformer focused exclusively on predicting the metabolism of xenobiotics (such as plant natural products, drugs, cosmetics and other synthetic compounds) using a limited number of pre-defined steps and somewhat limited rule-based methods. BioTransformer 3.0, uses much more sophisticated methods and incorporates new databases, new constraints and new prediction modules to not only more accurately predict the metabolic transformation products of exogenous xenobiotics but also the transformation products of endogenous metabolites, such as amino acids, peptides, carbohydrates, organic acids, and lipids. BioTransformer 3.0 can also support customized sequential combinations of these transformations along with multiple iterations to simulate multi-step human and/or environmental biotransformation events. Performance tests indicate that BioTransformer 3.0 is 40-50% more accurate, much less prone to combinatorial “explosions” and far more comprehensive in terms of metabolite coverage/capabilities than previous versions of BioTransformer.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Dynamic phase transformations in additively manufactured Ti-6Al-4V during thermo-mechanical gyrations

A complex interaction of process parameters, geometry and scan strategies in Additive Manufacturing (AM), can bring about spatial and temporal transients, i.e., Σ T ( x, y, z, time ), within a part. Published literature focusses on fluctuating thermal cycles on the microstructure evolution. However, the microstructural variations have not been correlated to dynamic flow behavior due to the macro- and micro-scale phenomena, i.e., accumulated plastic strains brought about by large thermal gradients, transformational strains and crystallographic misfit strains. Therefore, here we studied the mechanical response of Ti6Al4V alloys produced by AM under externally imposed controlled thermo-mechanical reversals in a Gleeble® thermo-mechanical simulator. The stress-strain behaviors were correlated to phase fractions, lattice strains, and also limited information on crystallographic texture using neutron diffraction techniques at the VULCAN Beamline at SNS, ORNL and also metallographic studies. The results are discussed and rationalized based on theories of static and dynamic phase transformations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Frequency-Selectable Laser Source (FLS) Calibrator for CMB Bandpass Characterization

One of the biggest challenges for Cosmic Microwave Background (CMB) experiments comes from our detector bandpass calibration. Uncertainties in bandpass can severely limit our measurements by limiting foreground removal and spectral fitting, which is particularly important for high-$\ell$ observations like cluster science using the Sunyaev-Zeldovich (SZ) effect. Currently, CMB experiments typically use a Fourier Transform Spectrometer (FTS) to measure the detector bandpasses. However, the resolution of the FTS is dependent on the length of the interferometer arms, leading to a need for increasingly large FTS instruments as CMB experiments require tighter constraints on detector bandpasses. Additionally, systematic effects like shifts in bandpass shape from uneven illumination from the FTS further limit the calibration uncertainties. As a complement to the FTS, we have developed a Frequency-selectable Laser Source (FLS) calibrator, which uses a laser with adjustable frequency housed in a calibrator that allows for varying degrees of laser power attenuation. We present several tests used to characterize the first prototype design of the FLS calibrator, as well as the improvements to the calibrator design currently underway.

Saunders, Lauren [Fermilab]↗
DOE OSTI2021

Ultradoping Boron on Si(100) via Solvothermal Chemistry

Ultradoping introduces unprecedented dopant levels into Si, which transforms its electronic behavior and enables its use as a next-generation electronic material. Commercialization of ultradoping is currently limited by gas-phase ultra-high vacuum requirements. Solvothermal chemistry is amenable to scale-up. However, an integral part of ultradoping is a direct chemical bond between dopants and Si, and solvothermal dopant-Si surface reactions are not well-developed. This work provides the first quantified demonstration of achieving ultradoping concentrations of boron (~1e14 cm 2 ) by using a solvothermal process. Surface characterizations indicate the catalyst cross-reacted, which led to multiple surface products and caused ambiguity in experimental confirmation of direct surface attachment. Density functional theory computations elucidate that the reaction results in direct B-Si surface bonds. Finally, this proof-of-principle work lays groundwork for emerging solvothermal ultradoping processes.

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

Functional analysis of Salix purpurea genes support roles for ARR17 and GATA15 as master regulators of sex determination

Abstract The Salicaceae family is of growing interest in the study of dioecy in plants because the sex determination region (SDR) has been shown to be highly dynamic, with differing locations and heterogametic systems between species. Without the ability to transform and regenerate Salix in tissue culture, previous studies investigating the mechanisms regulating sex in the genus Salix have been limited to genome resequencing and differential gene expression, which are mostly descriptive in nature, and functional validation of candidate sex determination genes has not yet been conducted. Here, we used Arabidopsis to functionally characterize a suite of previously identified candidate genes involved in sex determination and sex dimorphism in the bioenergy shrub willow Salix purpurea . Six candidate master regulator genes for sex determination were heterologously expressed in Arabidopsis, followed by floral proteome analysis. In addition, 11 transcription factors with predicted roles in mediating sex dimorphism downstream of the SDR were tested using DAP‐Seq in both male and female S. purpurea DNA. The results of this study provide further evidence to support models for the roles of ARR17 and GATA15 as master regulator genes of sex determination in S. purpurea , contributing to a regulatory system that is notably different from that of its sister genus Populus . Evidence was also obtained for the roles of two transcription factors, an AP2 / ERF family gene and a homeodomain‐like transcription factor, in downstream regulation of sex dimorphism.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Search for heavy scalar resonances decaying to Lorentz-boosted Higgs and Higgs-like bosons in the $\mathrm{b}\overline{\mathrm{b}}4\mathrm{q}$ final state at $\sqrt{s}=13$ TeV

A search is performed for a heavy scalar resonance X decaying to a Higgs boson (H) and a Higgs-like scalar boson (Y) in the two bottom quark (H → $b\bar{b}$) and four quark (Y → VV → 4q) final state, where V denotes a W or Z boson. Masses of the X between 900 and 4000 GeV and the Y between 60 and 2800 GeV are considered. The search is performed in data collected by the CMS experiment at the CERN LHC from proton-proton collisions at 13 TeV center-of-mass energy, with a data set corresponding to a total integrated luminosity of 138 fb −1 . It targets the Lorentz-boosted regime, in which the products of the H → $b\bar{b}$ decay can be reconstructed as a single large-area jet, and those from the Y → VV → 4q decay as either one Y → 4q or two jets V → H → $q\bar{q}$. Jet identification and mass reconstruction exploit machine-learning tools, including a novel attention-based “particle transformer” for Y → 4q identification. No significant excess is observed in the data above the standard model background expectation. Upper limits on the product of production cross section and branching fraction as low as 0.2 fb are derived at 95% confidence level for various mass hypotheses. This is the first search at the LHC for scalar resonances in the all-hadronic H → $b\bar{b}$VV decay channel.

Beyond Standard Model↗
DOE OSTI2024

Continuous polyamorphic transition in high-entropy metallic glass

Polyamorphic transition (PT) is a compelling and pivotal physical phenomenon in the field of glass and materials science. Understanding this transition is of scientific and technological significance, as it offers an important pathway for effectively tuning the structure and property of glasses. In contrast to the PT observed in conventional metallic glasses (MGs), which typically exhibit a pronounced first-order nature, herein we report a continuous PT (CPT) without first-order characteristics in high-entropy MGs (HEMGs) upon heating. This CPT behavior is featured by the continuous structural evolution at the atomic level and an increasing chemical concentration gradient with temperature, but no abrupt reduction in volume and energy. The continuous transformation is associated with the absence of local favorable structures and chemical heterogeneity caused by the high configurational entropy, which limits the distance and frequency of atomic diffusion. As a result of the CPT, numerous glass states can be generated, which provides an opportunity to understand the nature, atomic packing, formability, and properties of MGs. Moreover, this discovery highlights the implication of configurational entropy in exploring polyamorphic glasses with an identical composition but highly tunable structures and properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Discovery of hybrid chemical synthesis pathways with DORAnet

Developing efficient tools for discovering novel synthesis pathways is essential to advance chemical production methods that maximize the use of resources and energy. We introduce DORAnet (Designing Optimal Reaction Avenues Network Enumeration Tool), an open-source computational framework that addresses key limitations in current computer-aided synthesis planning (CASP) tools. DORAnet integrates both chemical/chemocatalytic (i.e., non-enzymatic) and enzymatic transformations, enabling the discovery of hybrid synthesis pathways. With 390 expert-curated chemical/chemocatalytic reaction rules and 3606 enzymatic rules derived from MetaCyc, it provides extensive flexibility for synthetic chemists and biotechnologists. The framework features customizable network expansion strategies, advanced filtering, and pathway search, ranking, and visualization tools. Validated against known reaction data, DORAnet successfully identified both established and novel synthesis routes for key industrial chemicals. In a case study involving 51 high-volume targets, DORAnet frequently ranked known commercial pathways among the top three results, demonstrating its practical relevance and ranking accuracy, while also uncovering numerous alternative (hybrid) synthesis pathways that were highly ranked.

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

Upcycling of polyethylene terephthalate to high-value chemicals by carbonate-interchange deconstruction

Condensation thermoplastics have become ubiquitous. The emergence of chemical upcycling could transform them into valuable feedstocks for chemical manufacturing at their end-of-life. However, current solvolysis processes suffer equilibrium limitations due to the liberation of reactive byproducts. We report a carbonate interchange deconstruction (CID) methodology for poly(ethylene terephthalate) (PET), where carbonates act as both latent nucleophiles and byproduct sequestering agents. High product selectivity (>95%) is achieved regardless of the targeted terephthalate product, originating from removal of ethylene glycol from the reaction equilibrium via its conversion into various oligoethers. CID is robust to the impurities present in post-consumer waste plastics and significantly reduces solvent demand, with just 10 mL of dimethyl carbonate successfully converting ca. 5 g of mixed PET waste into highly pure dimethyl terephthalate in excellent isolated yield (92%). CID opens a new upcycling paradigm wherein CO2, embedded in carbonates, is leveraged to choreograph the selectivity of an otherwise equilibrium-controlled polymer upcycling process.

Galan, Nick [ORNL]↗
DOE OSTI2024

Deep learning-driven super-resolution in Raman hyperspectral imaging: Efficient high-resolution reconstruction from low-resolution data

Deep learning (DL) has become an indispensable tool in hyperspectral data analysis, automatically extracting valuable features from complex, high-dimensional datasets. Super-resolution reconstruction, an essential aspect of hyperspectral data, involves enhancing spatial resolution, particularly relevant to low-resolution hyperspectral data. Yet, the pursuit of super-resolution in hyperspectral analysis is fraught with challenges, including acquiring ground truth high-resolution data for training, generalization, and scalability. The pressing issue of extended spectral acquisition times, notably for high-resolution scans, is a significant roadblock in hyperspectral imaging. Super-resolution methods offer a promising solution by providing higher spatial resolution data to expedite data collection and yield more efficient outcomes. This paper delves into a practical application of these concepts using Raman imaging, where spectral acquisition times can be prohibitively long. In this context, DL-based super-resolution models demonstrate their efficacy by predicting and reconstructing high-resolution Raman data from low-resolution input, eliminating the need for resource-intensive high-resolution scans. While previous work often relied on substantial high-resolution datasets, this study showcases the ability to achieve similar outcomes even with limited data, presenting a more practical and cost-effective approach. In conclusion, the results offer a glimpse into the transformative potential of this technology to streamline hyperspectral imaging applications by saving valuable time and resources through the successful generation of high-resolution data from low-resolution inputs.

42 ENGINEERING↗
DOE OSTI2025

On two nucleons near unitarity with perturbative pions

We explore the impact of perturbative pions on the Unitarity Expansion in the two-nucleon S-waves of Chiral Effective Field Theory at next-to-next-to leading order (N 2 LO). Pion exchange explicitly breaks the nontrivial fixed point’s universality, i.e. invariance of S waves under both conformal and Wigner’s combined SU(4) spin–isospin transformations. On the other hand, Unitarity explicitly breaks chiral symmetry. The two seem incompatible in their respective exact-symmetry limits. χEFT with Perturbative Pions in the Unitarity Expansion resolves the apparent conflict in the Unitarity Window (phase shifts 45° ≲ δ(k) ≲ 135°), i.e. around momenta most relevant for low-energy nuclear systems. Its only LO scale is the scattering momentum; NLO adds only scattering length, effective range and non-iterated one-pion exchange (OPE); and N 2 LO only once-iterated OPE. Agreement in the 1 S 0 channel is very good.

Chiral symmetry↗
DOE OSTI2023

Wetland Soil Characterization and Methane Production Impacted by Nickel Addition, Argonne and Tims Branch Wetlands, September and October 2020

Abstract:Freshwater wetland soils are foci of biogeochemical cycling as they serve as key sources of methane to the atmosphere. An array of metalloenzymes is essential to anaerobic microbial carbon transformations. Nickel is notably recognized as playing key roles in the enzymatic pathways of methanogenesis. Low availability of trace metals limits microbial element cycling in laboratory studies, but the occurrence of such limitations in natural subsurface aquatic systems is poorly understood. Microcosm incubation studies were carried out using two distinct wetland soils, one from a marsh wetland and the second from a riparian wetland, to explore the effect of dissolved Ni concentrations on methane production. Data are provided for wetland soil characterization and soil incubation experiments using materials from marsh wetlands at Argonne National Laboratory and riparian wetlands in the Tims Branch watershed at Savannah River National Laboratory. The characterization data consists soil carbon, nitrogen, sulfur, and iron contents plus as well as the solid-phase concentrations of copper, nickel, cobalt, and zinc, bioessential trace metals that may limits microbial metabolic process if they have low availability. The data for the soil incubation experiments include fluid pH, fluid dissolved trace metal concentrations, and cumulative methane production. Three soil incubations are reported: marsh wetland soil with increasing nickel addition, marsh wetland soil in sulfate-free water with increasing nickel addition, and riparian wetland soil with increasing nickel addition. All data are provided in text-based CSV format with header sections indicating the data contained in each file and the corresponding units. Note that "u" is used in place of Greek lower case mu to indicate the micro prefix on units. A Table of Contents file (Yan_Soil_Incubations_2020_TOC.txt) provides an index for the data contained in the individual files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning

Abstract The elementary excitations in metallic glasses (MGs), i.e., β processes that involve hopping between nearby sub-basins, underlie many unusual properties of the amorphous alloys. A high-efficacy prediction of the propensity for those activated processes from solely the atomic positions, however, has remained a daunting challenge. Recently, employing well-designed site environment descriptors and machine learning (ML), notable progress has been made in predicting the propensity for stress-activated β processes (i.e., shear transformations) from the static structure. However, the complex tensorial stress field and direction-dependent activation could induce non-trivial noises in the data, limiting the accuracy of the structure-property mapping learned. Here, we focus on the thermally activated elementary excitations and generate high-quality data in several Cu-Zr MGs, allowing quantitative mapping of the potential energy landscape. After fingerprinting the atomic environment with short- and medium-range interstice distribution, ML can identify the atoms with strong resistance or high compliance to thermal activation, at a high accuracy over ML models for stress-driven activation events. Interestingly, a quantitative “between-task” transferring test reveals that our learnt model can also generalize to predict the propensity of shear transformation. Our dataset is potentially useful for benchmarking future ML models on structure-property relationships in MGs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Automatic detection of methane emissions in multispectral satellite imagery using a vision transformer

Curbing methane emissions is among the most effective actions that can be taken to slow down global warming. However, monitoring emissions remains challenging, as detection methods have a limited quantification completeness due to trade-offs that have to be made between coverage, resolution and detection accuracy. Here we show that deep learning can overcome the trade-off in terms of spectral resolution that comes with multi-spectral satellite data, resulting in a methane detection tool with global coverage and high temporal and spatial resolution. We compare our detections with airborne methane measurement campaigns, which suggests that our method can detect methane point sources in Sentinel-2 data down to plumes of 0.01 km 2 , corresponding to 200 to 300 kg CH 4 /h -1 sources. Our model shows an order of magnitude improvement over the state-of-the-art, providing a significant step towards the automated, high resolution detection of methane emissions at a global scale, every few days.

54 ENVIRONMENTAL SCIENCES↗