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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics

A general agreement in psycholinguistics claims that syntax and meaning are unified precisely and very quickly during online sentence processing. Although several theories have advanced arguments regarding the neurocomputational bases of this phenomenon, we argue that these theories could potentially benefit by including neurophysiological data concerning cortical dynamics constraints in brain tissue. In addition, some theories promote the integration of complex optimization methods in neural tissue. In this paper we attempt to fill these gaps introducing a computational model inspired in the dynamics of cortical tissue. In our modeling approach, proximal afferent dendrites produce stochastic cellular activations, while distal dendritic branches–on the other hand–contribute independently to somatic depolarization by means of dendritic spikes, and finally, prediction failures produce massive firing events preventing formation of sparse distributed representations. The model presented in this paper combines semantic and coarse-grained syntactic constraints for each word in a sentence context until grammatically related word function discrimination emerges spontaneously by the sole correlation of lexical information from different sources without applying complex optimization methods. By means of support vector machine techniques, we show that the sparse activation features returned by our approach are well suited—bootstrapping from the features returned by Word Embedding mechanisms—to accomplish grammatical function classification of individual words in a sentence. In this way we develop a biologically guided computational explanation for linguistically relevant unification processes in cortex which connects psycholinguistics to neurobiological accounts of language. We also claim that the computational hypotheses established in this research could foster future work on biologically-inspired learning algorithms for natural language processing applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A potent and durable malaria transmission-blocking vaccine designed from a single-component 60-copy Pfs230D1 nanoparticle

Malaria transmission-blocking vaccines (TBVs) reduce disease transmission by breaking the continuous cycle of infection between the human host and the mosquito vector. Domain 1 (D1) of Pfs230 is a leading TBV candidate and comprises the majority of transmission-reducing activity (TRA) elicited by Pfs230. Here we show that the fusion of Pfs230D1 to a 60-copy multimer of the catalytic domain of dihydrolipoyl acetyltransferase protein (E2p) results in a single-component nanoparticle composed of 60 copies of the fusion protein with high stability, homogeneity, and production yields. The nanoparticle presents a potent human transmission-blocking epitope within Pfs230D1, indicating the antigen is correctly oriented on the surface of the nanoparticle. Two vaccinations of New Zealand White rabbits with the Pfs230D1 nanoparticle elicited a potent and durable antibody response with high TRA when formulated in two distinct adjuvants suitable for translation to human use. This single-component nanoparticle vaccine may play a key role in malaria control and has the potential to improve production pipelines and the cost of manufacturing of a potent and durable TBV.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Electronic commensuration of a spin moiré superlattice in a layered magnetic semimetal

Spin moiré superlattices (SMSs) have been proposed as a magnetic analog of crystallographic moiré systems and a source of electron minibands offering vector-field moiré tunability and Berry curvature effects. However, it has proven challenging to realize an SMS in which a large exchange coupling J is transmitted between conduction electrons and localized spins. Furthermore, most systems have carrier mean free paths l mfp shorter than their spin moiré lattice constant a spin , inhibiting miniband formation. Here, we discover that the layered magnetic semimetal EuAg 4 Sb 2 overcomes these challenges by forming an interface with J ~ 100 milli–electron volts transferred between a Eu triangular lattice and anionic Ag 2 Sb bilayers hosting a two-dimensional electron band in the ballistic regime (l mfp >> a spin ). The system realizes an SMS with a spin commensurate with the Fermi momentum, leading to a marked quenching of the transport response from miniband formation. Our findings demonstrate an approach to magnetically engineering moiré superlattices and a potential route to an emergent spin-driven quantum Hall state.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Generating accurate density matrices on the tangent space of a Grassmann manifold

Interpolating a density matrix from a set of known density matrices is not a trivial task. This is because a linear combination of density matrices does not necessarily correspond to another density matrix. In this Communication, density matrices are examined as objects of a Grassmann manifold. Although this manifold is not a vector space, its tangent space is a vector space. As a result, one can map the density matrices on this manifold to their corresponding vectors in the tangent space and then perform interpolations on that tangent space. The resulting interpolated vector can be mapped back to the Grassmann manifold, which can then be utilized (1) as an optimal initial guess for a self-consistent field (SCF) calculation or (2) to derive energy directly without time-consuming SCF iterations. Such a promising approach is denoted as Grassmann interpolation (G-Int). The hydrogen molecule has been used to illustrate that the described interpolated method in this work preserves the essential attributes of a density matrix. For phosphorus mononitride and ferrocene, it was demonstrated numerically that reference points for the definition of the corresponding tangent spaces can be chosen arbitrarily. In addition, the interpolated density matrices provide a superior and essentially converged initial guess for an SCF calculation to make the SCF procedure itself unnecessary. Finally, this accurate, efficient, robust, and systematically improved G-Int strategy has been used for the first time to generate highly accurate potential energy surfaces with fine details for the difficult case, ferrocene.

Chemistry↗
DOE OSTI2024

In-plane multi-q magnetic ground state of Na 3 Co 2 SbO 6

We report Na 3 Co 2 SbO 6 is a potential Kitaev magnet with a monoclinic layered crystal structure. Recent investigations of the C3-symmetric sister compound Na 2 Co 2 TeO 6 have uncovered a unique triple-q magnetic ground state, as opposed to a single-q (zigzag) one, prompting us to examine the influence of the reduced structural symmetry of Na 3 Co 2 SbO 6 on its ground state. Neutron diffraction data obtained on a twin-free crystal reveal that the ground state remains a multi-q state, despite the system's strong non-C 3 -symmetric anisotropy. This robustness of multi-q orders suggests that they are driven by a common mechanism in the honeycomb cobaltates, such as interactions beyond the bilinear order. Spin-polarized neutron diffraction results show that the ordered moments are entirely in plane, with each staggered component orthogonal to the propagating wave vector. The inferred ground state appears more compatible with the so-called XXZ easy-plane anisotropy than the Kitaev anisotropy, and features unequal ordered moments reduced by strong quantum fluctuations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Controlling Magnetization Vector Depth Profiles of La 0.7 Sr 0.3 CoO 3 /La 0.7 Sr 0.3 MnO 3 Exchange Spring Bilayers via Interface Reconstruction

The La 0.7 Sr 0.3 CoO 3–δ /La 0.7 Sr 0.3 MnO 3–δ (LSCO/LSMO) bilayer system is an ideal perovskite oxide platform for investigating interface reconstruction and its effect on their magnetic properties. Previous studies have shown that LSCO can separate into magnetic sublayers, which possess distinct trends as the total LSCO thickness increases. Here, we used polarized neutron reflectometry to quantify changes in the magnetic and chemical depth profiles, and it confirms the formation of ~12 Å-thick interfacial LSCO and LSMO layers, characterized by a decreased nuclear scattering length density compared to the bulk of the layers. This decrease is attributed to the combined effects of oxygen vacancy formation and interfacial charge transfer, which lead to magnetically active Co 2+ ions with ionic radii larger than the Co 3+ /Co 4+ ions typically found in bulk LSCO or single-layer films. The interfacial magnetization values, as well as Co 2+ ion and oxygen vacancy concentrations, depend strongly on the LSCO layer thickness. These results highlight the sensitive interplay of the cation valence states, oxygen vacancy concentration, and magnetization at interfaces in perovskite oxide multilayers, demonstrating the potential to tune their functional properties via careful design of their structure.

36 MATERIALS SCIENCE↗
DOE OSTI2022

High-Precision Characterization of Seismicity from the 2022 Hunga Tonga-Hunga Ha'apai Volcanic Eruption

The earthquake swarm accompanying the January 2022 Hunga Tonga-Hunga Ha'apai (HTHH) volcanic eruption includes a large number of posteruptive moderate-magnitude seismic events and presents a unique opportunity to use remote monitoring methods to characterize and compare seismic activity with other historical caldera-forming eruptions. We compute improved epicentroid locations, magnitudes, and regional moment tensors of seismic events from this earthquake swarm using regional to teleseismic surface-wave cross correlation and waveform modeling. Precise relative locations of 91 seismic events derived from 59,047 intermediate-period Rayleigh- and Love-wave cross-correlation measurements collapse into a small area surrounding the volcano and exhibit a southeastern time-dependent migration. Regional moment tensors and observed waveforms indicate that these events have a similar mechanism and exhibit a strong positive compensated linear vector dipole component. Precise relative magnitudes agree with regional moment tensor moment magnitude ($M_w$) estimates while also showing that event sizes and frequency increase during the days after the eruption followed by a period of several weeks of less frequent seismicity of a similar size. The combined information from visual observation and early geologic models indicate that the observed seismicity may be the result of a complex series of events that occurred after the explosive eruption on 15 January, possibly involving rapid resupply of the magma chamber shortly after the eruption and additional faulting and instability in the following weeks. In addition, we identify and characterize an $M_w$ 4.5 event five days before the paroxysmal explosion on 15 January, indicating that additional seismic events preceding the main eruption could have been identified with improved local monitoring. As a result, our analysis of the HTHH eruption sequence demonstrates the value of potentially utilizing teleseismic surface-wave cross correlation and waveform modeling methods to assist in the detailed analysis of remote volcanic eruption sequences.

58 GEOSCIENCES↗
DOE OSTI2024

Field-resolved space–time characterization of few-cycle structured light pulses

Accompanied by the rapid development of ultrafast laser platforms in recent decades, the spatiotemporal manipulation of ultrashort laser pulses has attracted much attention due to the potential for cutting-edge applications of structured light, including optical tweezers, optical communications, super-resolution imaging, time-resolved spectroscopy in molecules and quantum materials, and strong-field physics. Today, techniques capable of characterizing the full spatial, temporal, and polarization state properties of structured light are strongly desired. Here, we demonstrate a technique, termed 3D TIPTOE, for characterizing structured mid-infrared waveforms, which uses only a two-dimensional silicon-based image sensor as both the detector and the nonlinear medium. By combining the advantages of the sub-cycle time resolution afforded by nonlinear excitation and the spatial resolution inherent to the two-dimensional sensor, the 3D TIPTOE technique allows full characterization of structured electric fields, significantly reducing the complexity of detection compared to other techniques. The validity of the technique is established by measuring both few-cycle Bessel–Gaussian pulses and radially polarized femtosecond vector beams.

Liu, Yangyang (ORCID:0000000335205332)↗
DOE OSTI2021

Paclitaxel loading in cationic liposome vectors is enhanced by replacement of oleoyl with linoleoyl tails with distinct lipid shapes

Abstract Lipid carriers of hydrophobic paclitaxel (PTX) are used in clinical trials for cancer chemotherapy. Improving their loading capacity requires enhanced PTX solubilization. We compared the time-dependence of PTX membrane solubility as a function of PTX content in cationic liposomes (CLs) with lipid tails containing one (oleoyl; DOPC/DOTAP) or two (linoleoyl; DLinPC/newly synthesized DLinTAP) cis double bonds by using microscopy to generate kinetic phase diagrams. The DLin lipids displayed significantly increased PTX membrane solubility over DO lipids. Remarkably, 8 mol% PTX in DLinTAP/DLinPC CLs remained soluble for approximately as long as 3 mol% PTX (the solubility limit, which has been the focus of most previous studies and clinical trials) in DOTAP/DOPC CLs. The increase in solubility is likely caused by enhanced molecular affinity between lipid tails and PTX, rather than by the transition in membrane structure from bilayers to inverse cylindrical micelles observed with small-angle X-ray scattering. Importantly, the efficacy of PTX-loaded CLs against prostate cancer cells (their IC50 of PTX cytotoxicity) was unaffected by changing the lipid tails, and toxicity of the CL carrier was negligible. Moreover, efficacy was approximately doubled against melanoma cells for PTX-loaded DLinTAP/DLinPC over DOTAP/DOPC CLs. Our findings demonstrate the potential of chemical modifications of the lipid tails to increase the PTX membrane loading while maintaining (and in some cases even increasing) the efficacy of CLs. The increased PTX solubility will aid the development of liposomal PTX carriers that require significantly less lipid to deliver a given amount of PTX, reducing side effects and costs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Climate change, malaria and neglected tropical diseases: a scoping review

Abstract To explore the effects of climate change on malaria and 20 neglected tropical diseases (NTDs), and potential effect amelioration through mitigation and adaptation, we searched for papers published from January 2010 to October 2023. We descriptively synthesised extracted data. We analysed numbers of papers meeting our inclusion criteria by country and national disease burden, healthcare access and quality index (HAQI), as well as by climate vulnerability score. From 42 693 retrieved records, 1543 full-text papers were assessed. Of 511 papers meeting the inclusion criteria, 185 studied malaria, 181 dengue and chikungunya and 53 leishmaniasis; other NTDs were relatively understudied. Mitigation was considered in 174 papers (34%) and adaption strategies in 24 (5%). Amplitude and direction of effects of climate change on malaria and NTDs are likely to vary by disease and location, be non-linear and evolve over time. Available analyses do not allow confident prediction of the overall global impact of climate change on these diseases. For dengue and chikungunya and the group of non-vector-borne NTDs, the literature privileged consideration of current low-burden countries with a high HAQI. No leishmaniasis papers considered outcomes in East Africa. Comprehensive, collaborative and standardised modelling efforts are needed to better understand how climate change will directly and indirectly affect malaria and NTDs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Using machine learning to predict the correlation of spectra using SDSS magnitudes as an improvement to the Locus Algorithm

The Locus Algorithm is a new technique to improve the quality of differential photometry by optimising the choices of reference stars. At the heart of this algorithm is a routine to assess how good each potential reference star is by comparing its SDSS magnitude values to those of the target star. In this way, the difference in wavelength-dependent effects of the Earth’s atmospheric scattering between target and reference can be minimised. This paper sets out a new way to estimate the quality of each reference star using machine learning. A random subset of stars from SDSS with spectra was chosen. For each one, a suitable reference star, also with a spectrum, was chosen. The correlation between the two spectra in the SDSS r band (between 550 nm and 700 nm) was taken to be the gold-standard measure of how well they match up for differential photometry. The five SDSS magnitude values for each of these stars were used as predictors. A number of supervised machine learning models were constructed on a training set of the stars and were each evaluated on a testing set. The model using Support Vector Regression had the best performance of these models. It was then tested on a final, hold-out, validation set of stars to get an unbiased measure of its performance. With an R 2 of 0.62, the SVR model presents enhanced performance for the Locus Algorithm technique.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Universal Monte Carlo Event Generator

With the Jefferson Lab 12 GeV physics program underway and plans for the future Electron-Ion Collider (EIC), the nuclear physics community is entering a new era of exploration of QCD phenomena involving extensive data taking and event-level processing. This brings with it the potential for unprecedented access to multidimensional particle momentum distributions (PMDs) that can be connected to various theoretical frameworks by unfolding the emergent quantum mechanical properties of QCD using the PMDs. In practice, the PMDs are rendered as discretized histograms (typically one- or two-dimensional projections), and detector effects must be taken into account to unfold the pure detector effect-free PMDs that can be connected with theory. One of the challenges in this new era is obtaining faithful reconstructions of the multidimensional PMDs that preserve all of the inherent particle correlations. In this LDRD project we developed a novel approach using machine learning (ML) that solves this challenge, by avoiding entirely the need to use histograms as the main numerical technique to obtain the detector effect-free PMDs. The new approach is, moreover, scalable to higher dimensional PMDs. The central idea involves training neural networks (NNs) to generate synthetic event-level data (momenta 4-vectors of final state particles) to preserve all correlations among the particles. This is achieved by converting the trial synthetic vertex-level events to detector-level events using detector simulators. The NNs are then tuned using a specialized distance metric between the synthetic detector events and the real detector events.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Pushing the Limits of Micro battery Capacity and Energy

Micro battery with higher energy density will extend the service life of the miniature sensors so that the scientists and engineers could be able to collect more data along with the time in ecological and environmental vectors, such as animal tracking. Within this project, a completely new cell design (Gen2) for micro batteries to further increase their cell capacity and energy is developed. The new cell design directly packages the active material in a hard aluminum case, decreasing the percentage of non-active components. The manufacturing process to improve wetting ability is studied and the trial run is finally given to evaluate the feasibility of the proposed technology. It’s found that both the capacity and energy with same battery size could be increased by 30% comparing with Gen1 micro battery technology previously developed by PNNL. The yield of Gen2 micro batteries is still lower due to the high degree of manual operation, especially for drilling process where more precise position control is needed. More effort is still required to improve the capability of automatic manufacturing processes. The new battery technology will support the development of smaller and lighter miniature sensors at PNNL and could potentially contribute to the large-scale manufacturing capabilities of micro batteries in US with reliable performances and long-term shelf life.

25 ENERGY STORAGE↗
DOE OSTI2021

Dispersion relation for gauge-free electromagnetic drift kinetics

Recently, a new approach to gyrokinetics, invariant under electromagnetic gauge transformations, was developed. The gyrocenter equations of motion are now expressed in terms of the perturbed fields instead of the potentials, in a form suitable for numerical simulations and analytic studies. In this paper, we verify that the long-wavelength limit, i.e., the drift-kinetic limit of the new gyrokinetic theory, is in line with existing work, providing a solid foundation for simulations. Here, we compute the dispersion relation of the new drift-kinetic theory in slab geometry and find agreement with a long-wavelength limit of the full Vlasov–Maxwell model.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Cyclotron radiation emission spectroscopy signal classification with machine learning in project 8

The Cyclotron Radiation Emission Spectroscopy (CRES) technique pioneered by Project 8 measures cyclotron radiation from individual electrons in a background magnetic field to construct a highly precise energy spectrum for beta decay studies and other applications. The detector, magnetic trap geometry, and electron dynamics give rise to a multitude of complex electron signal structures which carry information about distinguishing physical traits. Here, we develop machine learning models to classify CRES signals with high accuracy based on these traits, improve the resultant frequency spectrum, and offer the potential for a sophisticated analysis which will help Project 8 achieve tritium endpoint measurement in the future.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Bare mass effects on the reheating process after inflation

We consider the effects of a bare mass term for the inflaton, when the inflationary potential takes the form V(Φ)=λΦ k about its minimum with k ≥ 4. We concentrate on k =4, but discuss general cases as well. Further, we assume $λΦ$$^{2}_{end}$ >> $m$$^{2}_{Φ}$, where Φ end is the inflaton field value when the inflationary expansion ends. We show that the presence of a mass term (which may be present due to radiative corrections or supersymmetry breaking) can significantly alter the reheating process, as the equation of state of the inflaton condensate changes from w Φ = $\frac{1}{3}$ to w Φ = 0 when λ⁢Φ 2 drops below $m$$^{2}_{Φ}$. We show that, for a mass mΦ ≳ 3⁢λ $\frac{1}{4}$ T RH , the mass term will dominate at reheating. The value of λ is relatively model independent as it is normalized by the cosmic microwave background perturbation spectrum. For T models of inflation, this leads to m Φ ≳ T RH /250. We compute the effects on the reheating temperature for cases where reheating is due to inflaton decay (to fermions, scalars, or vectors) or to inflaton scattering (to scalars or vectors). For scattering to scalars and in the absence of a decay, there is always a residual inflaton background that acts as cold dark matter. In this case, we derive a strong upper limit to the inflaton bare mass which for T models is m Φ < 350 MeV⁢(T RH /10 10 GeV) 3/5 . We also consider the effect of the bare mass term on the fragmentation of the inflaton condensate.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Jacobian-scaled K-means clustering for physics-informed segmentation of reacting flows

This work introduces Jacobian-scaled K-means (JSK-means) clustering, which is a physicsinformed clustering strategy centered on the K-means framework. The method allows for the injection of underlying physical knowledge into the clustering procedure through a distance function modification: instead of leveraging conventional Euclidean distance vectors, the JSKmeans procedure operates on distance vectors scaled by matrices obtained from dynamical system Jacobians evaluated at the cluster centroids. The goal of this work is to show how the JSKmeans algorithm - without modifying the input dataset - produces clusters that capture regions of dynamical similarity, in that the clusters are redistributed towards high-sensitivity regions in phase space and are described by similarity in the source terms of samples instead of the samples themselves. The algorithm is demonstrated on a complex reacting flow simulation dataset (a channel detonation configuration), where the dynamics in the thermochemical composition space are known through the highly nonlinear and stiff Arrhenius-based chemical source terms. Interpretations of cluster partitions in both physical space and composition space reveal how JSK-means shifts clusters produced by standard K-means towards regions of high chemical sensitivity (e.g., towards regions of peak heat release rate near the detonation reaction zone). Furthermore, the findings presented here illustrate the benefits of utilizing Jacobian-scaled distances in clustering techniques, and the JSK-means method in particular displays promising potential for improving former partition-based modeling strategies in reacting flow (and other multi-physics) applications.

Clustering↗
DOE OSTI2022

Landscape Topography and Regional Drought Alters Dust Microbiomes in the Sierra Nevada of California

Dust provides an ecologically significant input of nutrients, especially in slowly eroding ecosystems where chemical weathering intensity limits nutrient inputs from underlying bedrock. In addition to nutrient inputs, incoming dust is a vector for dispersing dust-associated microorganisms. While little is known about dust-microbial dispersal, dust deposits may have transformative effects on ecosystems far from where the dust was emitted. Using molecular analyses, we examined spatiotemporal variation in incoming dust microbiomes along an elevational gradient within the Sierra Nevada of California. We sampled throughout two dry seasons and found that dust microbiomes differed by elevation across two summer dry seasons (2014 and 2015), which corresponded to competing droughts in dust source areas. Dust microbial taxa richness decreased with elevation and was inversely proportional to dust heterogeneity. Likewise, dust phosphorus content increased with elevation. At lower elevations, early season dust microbiomes were more diverse than those found later in the year. The relative abundances of microbial groups shifted during the summer dry season. Furthermore, mutualistic fungal diversity increased with elevation, which may have corresponded with the biogeography of their plant hosts. Although dust fungal pathogen diversity was equivalent across elevations, elevation and sampling month interactions for the relative abundance, diversity, and richness of fungal pathogens suggest that these pathogens differed temporally across elevations, with potential implications for humans and wildlife. This study shows that landscape topography and droughts in source locations may alter the composition and diversity of ecologically relevant dust-associated microorganisms.

59 BASIC BIOLOGICAL SCIENCES↗