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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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This paper presents a modeling study conducted to evaluate tidal-stream energy extraction and its associated potential environmental impacts using a three-dimensional unstructured-grid coastal ocean model, which was coupled with a water-quality model and a tidal-turbine module.
Silver nanoparticles of many types are widely used in consumer and medical products. The surface chemistry of particles and the coatings that form during synthesis or use in many types of media can significantly impact the behaviors of particles including dissolution, transformation and biological or environmental impact. Consequently it is useful to be able to extract information about the thickness of surface coatings and other attributes of nanoparticles produced in a variety of ways. It has been demonstrated that X-ray Photoelectron Spectroscopy (XPS) can be reliably used to determine the thickness of organic and other nanoparticles coatings and shells. However, care is required to produce reliable and consistent information. Here we report the XPS spectra from gold/silver core-shell nanoparticles of nominal size 20 nm removed from a citrate saturated solution after one and two washing cycles. The Simulation of Electron Spectra for Surface Analysis (SESSA) program had been used to model peak amplitudes to obtain information on citrate coatings that remain after washing and demonstrate the presence of the gold core. This data is provided so that others can compare use of SESSA or other modeling approaches to quantify the nature of coatings to those already published and to explore the impacts particle non-uniformities on XPS signals from core-shell nanoparticles.
Chemical, biological, radiological, nuclear, and explosives incidents require rapid detection and characterization for appropriate response. For a nuclear detonation, visible-light cameras may be used to locate the cloud and characterize fallout deposition when coupled with numerical models. Films from the United States’ nuclear testing era compose the only sizeable collection of imagery depicting high-yield detonations. These films offer unique insights into characteristics of flows involving scales that are difficult to replicate experimentally, and they are a valuable source of data for the validation of models for nuclear fallout transport, either as part of emergency response or forensic activities. In this work, we implement modern computer vision and machine learning techniques to identify and track the cloud automatically and subsequently determine the time dependence of some of its features. We trained a ResNet-18 image classifier on hundreds of images to categorize nuclear cloud morphology. Each category or cloud regime is determined by early cloud evolution and is associated to constitutive properties of the flow, such as distribution of vorticity. Next, we identified keypoint features using the KAZE algorithm and tracked these keypoints in the images, allowing us to determine the dimensions and velocities of the cloud across film frames. These measurements converted to real-world units provide valuable experimental data that can be used in the development and validation of nuclear cloud models. We compared the results of this method against manual cloud rise measurements from two different films. In one, our automated method accelerated the feature extraction process without sacrificing measurement accuracy.
A reliable supply of lithium is required to meet the increased demand for batteries over the coming decades. In this work, we demonstrated the potential to effectively extract lithium from brines by coupling solar-powered evaporation, adsorption, and membrane technologies together. We first synthesized a three-dimensional adsorptive evaporator by coating a lithium manganese oxide material onto a cotton stick using an easily scalable, one-step method. An osmotic membrane was then installed at the root of the evaporator to enhance the lithium to magnesium selectivity, prevent scaling caused by divalent cations, and thus further increase the water evaporation flux. The operation of the dual membrane-adsorption evaporator is entirely driven by osmosis and capillary force, demanding no extra energy input. The integration of the osmotic membrane was found to increase the lithium to magnesium selectivity over 10-fold to higher than 40. The dual process also produced high lithium to calcium selectivity and proved to sustain long-term stability by self-managing its internal osmotic pressure. Last, a test was done in three simulated brine solutions with varied brine chemistry and the evaporation rate stayed high and consistent throughout the long-term test. This illustrates that this technology can be used in varied brine solutions throughout the world providing efficient lithium capture and separation from competing divalent cations.
Abstract We report on a comprehensive reinterpretation of the existing cross-section data for elastic electron-proton scattering obtained by the initial-state radiation technique, resulting in a significantly improved accuracy of the extracted proton charge radius. By refining the external energy corrections we have achieved an outstanding description of the radiative tail, essential for a detailed investigation of the proton finite-size effects on the measured cross sections. This development, together with a novel framework for determining the radius, based on a regression analysis of the cross sections employing a polynomial model for the form factor, led us to a new value for the charge radius, which is $$(0.878 \pm 0.011_\mathrm {stat.}\pm 0.031_\mathrm {sys.}\pm 0.002_\mathrm {mod.})\,\mathrm {fm}$$ ( 0.878 ± 0 . 011 stat . ± 0 . 031 sys . ± 0 . 002 mod . ) fm
A measurement of the jet mass distribution in hadronic decays of Lorentz-boosted top quarks is presented. The measurement is performed in the lepton + jets channel of top quark pair production ($\hbox {t}\overline{\hbox {t}}$) events, where the lepton is an electron or muon. The products of the hadronic top quark decay are reconstructed using a single large-radius jet with transverse momentum greater than 400 GeV. The data were collected with the CMS detector at the LHC in proton-proton collisions and correspond to an integrated luminosity of 138 fb -1 . The differential $\hbox {t}\overline{\hbox {t}}$ production cross section as a function of the jet mass is unfolded to the particle level and is used to extract the top quark mass. The jet mass scale is calibrated using the hadronic W boson decay within the large-radius jet. The uncertainties in the modelling of the final state radiation are reduced by studying angular correlations in the jet substructure. These developments lead to a significant increase in precision, and a top quark mass of 173.06 ± 0.84 GeV.
Abstract $$B^\pm \rightarrow DK^\pm $$ B ± → D K ± transitions are known to provide theoretically clean information about the CKM angle $$\gamma $$ γ , with the most precise available methods exploiting the cascade decay of the neutral D into CP self-conjugate states. Such analyses currently require binning in the D decay Dalitz plot, while a recently proposed method replaces this binning with the truncation of a Fourier series expansion. In this paper, we present a proof of principle of a novel alternative to these two methods, in which no approximations at the level of the data representation are required. In particular, our new strategy makes no assumptions about the amplitude and strong phase variation over the Dalitz plot. This comes at the cost of a degree of ambiguity in the choice of test statistic quantifying the compatibility of the data with a given value of $$\gamma $$ γ , with improved choices of test statistic yielding higher sensitivity. While our current proof-of-principle implementation does not demonstrate optimal sensitivity to $$\gamma $$ γ , its conceptually novel approach opens the door to new strategies for $$\gamma $$ γ extraction. More studies are required to see if these can be competitive with the existing methods.
Spiking neuromorphic computers (SNCs) are promising as a post Moore's law technology partly because of their potential for very low power computation. SNCs have primarily been demonstrated on machine learning and neural network applications, but they can also be used for applications beyond machine learning that can leverage SNC properties such as massively parallel computation and collocated processing and memory. Here, we demonstrate two graph problems (shortest path and neighborhood subgraph extraction) that can be solved using SNCs. We discuss the approach for mapping these applications to an SNC. We also estimate the performance of a memristive SNC for these applications on three real-world graphs.
Neuromorphic computing is poised to become a promising computing paradigm in the post Moore's law era due to its extremely low power usage and inherent parallelism. Traditionally speaking, a majority of the use cases for neuromorphic systems have been in the field of machine learning. In order to expand their usability, it is imperative that neuromorphic systems be used for non-machine learning tasks as well. The structural aspects of neuromorphic systems (i.e., neurons and synapses) are similar to those of graphs (i.e., nodes and edges), However, it is not obvious how graph algorithms would translate to their neuromorphic counterparts. In this work, we propose a preprocessing technique that introduces fractional offsets on the synaptic delays of neuromorphic graphs in order to break ties. This technique, in turn, enables two graph algorithms: longest shortest path extraction and minimum spanning trees.
Transverse momentum ( p T ) and rapidity ( y ) spectra of Z bosons and quarkonium states (some charmonium c c ¯ mesons such as J / ψ and ψ ( 2 S ) and some bottomonium b b ¯ mesons such as Υ ( 1 S ) , Υ ( 2 S ) , and Υ ( 3 S ) ) produced in proton-proton (pp) and lead-lead (Pb-Pb) collisions at the large hadron collider (LHC) are uniformly described by a hybrid model of two-component Erlang distribution for p T spectrum and two-component Gaussian distribution for y spectrum. The former distribution results from a multisource thermal model, and the latter one results from the revised Landau hydrodynamic model. The modelling results are in agreement with the experimental data measured in pp collisions at center-of-mass energies s = 2.76 and 7 TeV and in Pb-Pb collisions at center-of-mass energy per nucleon pair s N N = 2.76 TeV. Based on the parameter values extracted from p T and y spectra, the event patterns (particle scatter plots) in two-dimensional p T - y space and in three-dimensional velocity space are obtained.
This algorithm processes high-rate 3-phase signals to identify the start time of each signal and estimate its envelope as data features. The start time and magnitude of each signal during the steady state is also extracted. The features can be used to detect abnormal signals. This algorithm is developed to analyze Exxeno's 3-phase voltage and current data recorded from refrigeration systems to detect device failure or degradation.
Real-time steering of time-resolved or in-situ X-ray tomography requires capturing changes in morphological descriptors in a sample (e.g., porosity, particle size, and crack width) during continuous data acquisition. Image segmentation (2D or3D) followed by quantitative measurement is the conventional method for tracking changes in these descriptors with respect to a previous time-step or a 3D search in a volume. However, image segmentation is expensive. As a faster and unsupervised alternative, a feature-extraction approach using a convolutional autoencoders was developed, where the latent space of the encoder responds to relative changes in morphology with-out prior knowledge of the morphological descriptors.
Code allows users to easily pull publications from PubMed, OSTI, and SCOPUS; identify biological terms and extract their contexts from these papers; and determine which terms are related to other terms using one of the eleven wrapped algorithms
A modular Python framework for information extraction using large language models with support for multiple backends and optional verification workflows.
Aggregation, the process by which two or more ice particles attach to each other, is typically observed in clouds that span a range of temperatures and is influenced by the crystal shape (habit). In this study, the resulting characteristics of ice–ice two-monomer aggregation is investigated, which is expected to improve microphysical parameterizations through more precise aggregate characteristics and in turn better predict the rate of aggregation and snow development. A systematic way to determine the aspect ratio of the aggregate was developed, which takes into account the expected falling orientations, overlap of each monomer, and any contact angle that may form through so-called constrained randomization. Distributions were used to obtain the most frequent aspect ratio, major axis, and minor axis of aggregated particles with respect to the monomer aspect ratio. Simulations were completed using the Ice Particle and Aggregate Simulator (IPAS), a model that uses predefined three-dimensional geometries, (e.g., hexagonal prisms) to simulate ice crystal aggregation and allows for variation in crystal size, shape, number, and falling orientation. In this study, after collection in a theoretical grid space, detailed information is extracted from the particles to determine the properties of aggregates. It was found that almost all monomer aspect ratios aggregate to less extreme aggregate aspect ratios at nearly the same rate. Newly formed aggregate properties are amenable to implementation into more sophisticated bulk microphysical models designed to predict and evolve particle properties, which is crucial in realistically evolving cloud ice mass distribution and for representing the collection process.
Generalized equilibrium feedback assessment (GEFA) is a potentially valuable multivariate statistical tool for extracting vegetation feedbacks to the atmosphere in either observations or coupled Earth system models. The reliability of GEFA at capturing the terrestrial impacts on regional climate is demonstrated here using the National Center for Atmospheric Research Community Earth System Model (CESM), with focus on North Africa. The feedback is assessed statistically by applying GEFA to output from a fully coupled control run. To reduce the sampling error caused by short data records, the traditional or full GEFA is refined through stepwise GEFA by dropping unimportant forcings. Two ensembles of dynamical experiments are developed for the Sahel or West African monsoon region against which GEFA-based vegetation feedbacks are evaluated. In these dynamical experiments, regional leaf area index (LAI) is modified either alone or in conjunction with soil moisture, with the latter runs motivated by strong regional soil moisture–LAI coupling. Stepwise GEFA boasts higher consistency between statistically and dynamically assessed atmospheric responses to land surface anomalies than full GEFA, especially with short data records. GEFA-based atmospheric responses are more consistent with the coupled soil moisture–LAI experiments, indicating that GEFA is assessing the combined impacts of coupled vegetation and soil moisture. Both the statistical and dynamical assessments reveal a negative vegetation–rainfall feedback in the Sahel associated with an atmospheric stability mechanism in CESM versus a weaker positive feedback in the West African monsoon region associated with a moisture recycling mechanism in CESM.
In genotyping-by-sequencing (GBS) and restriction site-associated DNA sequencing (RAD-seq), read depth is important for assessing the quality of genotype calls and estimating allele dosage in polyploids. However, existing pipelines for GBS and RAD-seq do not provide read counts in formats that are both accurate and easy to access. Additionally, although existing pipelines allow previously-mined SNPs to be genotyped on new samples, they do not allow the user to manually specify a subset of loci to examine. Pipelines that do not use a reference genome assign arbitrary names to SNPs, making meta-analysis across projects difficult. We created the software TagDigger, which includes three programs for analyzing GBS and RAD-seq data. The first script, tagdigger_interactive.py, rapidly extracts read counts and genotypes from FASTQ files using user supplied sets of barcodes and tags. Input and output is in CSV format so that it can be opened by spreadsheet software. Tag sequences can also be imported from the Stacks, TASSEL-GBSv2, TASSEL-UNEAK, or pyRAD pipelines, and a separate file can be imported listing the names of markers to retain. A second script, tag_manager.py, consolidates marker names and sequences across multiple projects. A third script, barcode_splitter.py, assists with preparing FASTQ data for deposit in a public archive by splitting FASTQ files by barcode and generating MD5 checksums for the resulting files. TagDigger is open-source and freely available software written in Python 3. It uses a scalable, rapid search algorithm that can process over 100 million FASTQ reads per hour. TagDigger will run on a laptop with any operating system, does not consume hard drive space with intermediate files, and does not require programming skill to use.
Protein-only trajectories of the lowest-temperature window (310K) extracted from temperature replica-exchange molecular dynamics simulations of 23 different SARS CoV-2 systems, including S Protein ACE2-receptor binding-domain, MPro, PLPro, NSP3 ADRP (X-domain/macrodomain/phosphatase), NSP15 (endoribonuclease), NSP9, NSP10, NSP16, and N-protein N-terminus. Systems were prepared with charmm-gui and simulated using the GROMACS simulation software suite. Trajectories are provided in compressed dcd format with accompanying coordinate/topology files in pdb and psf formats. This data supplements the data release, DOI: 10.13139/OLCF/1650650 ('SARS-CoV2 Protein-Ligand Simulation Dataset: Layer 1 (Simulation Initial Conditions and Parameters)')