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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The phylogenetic roots of addiction: compulsive drug seeking, natural and drug-sensitive reward, and the acquisition of learned habits

Our rational faculties permit us humans to maximize the utility of our actions. We perform a fundamental type of cost-benefit analysis in which we frame a problem, assign values to the different paths, and then choose from among a set of available options, the course that promises the most favorable outcome. So why then does addiction appear to be so impervious to the associated costs, so unaffected by undesired consequences, and ultimately so resistant to cognitive oversight? The answer may likely be found in the fact that the drivers for compulsive drug seeking and drug taking are located in affective brain circuits, circuits that are structured and patterned by learning with repeated activation. These deep processes exhibit significant resistance to control by our cognitive faculties. The general consensus is that addiction arises from mechanisms that overvalue the magnitude of reward, discount the associated risks, and thereby bias individuals towards compulsive pursuit of addictive drugs. Behavioral disruption and dependence appear to arise at the intersection of a number of connected but separate phenomena: expectations for the occurrence of specific events, behaviors that seek encounters with them, the ability to notice and learn nonrandom, co-occurring conditions, the prediction and valuation of consequences, the forming of enduring memories, and the drivers of focused behavior through compulsion, habits, and acquired routines. It is important to recognize that each one of these individual faculties are present and well developed across the entire phylogenetic tree of bilateral metazoans. The goal of this special volume is dedicated to exploring the degree to which inherent elements can account for addiction and addiction-associated phenomena. In much of the literature on addiction, the underlying processes are often viewed as distinctly mammalian, arising, in part, from the strong cognitive capacities of this taxon. Some phenomena may even be regarded to exist only in primates, or even solely in humans. This supposition arises from the fact that studies are conducted almost exclusively in mammals and primates, while evolutionary antecedents of the behavior are rarely considered. A more comprehensive perspective that examines drug reward and reinforcement in a wider range of organisms demonstrates that many of the component traits are actually well developed across the greater metazoan lineage, and they may well predate the emergence of a mammalian clade by a wide margin. The collection of papers assembled here supports the notion that the capacity to associate cues and quences has not arisen in mammals de novo. Rather, the neural mechanisms for detecting contingencies and for predicting future outcomes are very deeply rooted across broad phylogenetic divisions. Our understanding of an ability to associate paired events has been enriched by work in invertebrate preparations in both classical and operant conditioning scenarios [Cook and Carew, 1986, 1989a, 1989b]. The ability to learn allows us to connect cues and behavioral actions to their associated consequences. Pavlovian conditioning enriches surrounding cues with predictive value. Outcomes with positive valence generate appetitive responses and approach to the associated cues, while those perceived as aversive bring cue avoidance and withdrawal. Humans are not the only life forms capable of such short- and long-term modulations of behavior

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Design Choices in Anomaly Detection for Industrial Control Systems: Insights from Gas Pipeline Data

Industrial control systems (ICS) remain vulnerable to increasingly sophisticated cyberattacks, yet evaluating anomaly detection models in these environments is challenging due to temporal dependencies, missing-not-at-random patterns, and extremely imbalanced datasets. These factors make common practices—especially random data splits and naïve imputation—prone to severe temporal leakage, which can inflate reported performance and obscure real-world limitations. In this work, we systematically examine classical machine learning models, temporal deep learning architecture, and tensor-decomposition–based methods on a gas-pipeline dataset using a fully temporally separated evaluation pipeline designed to mimic realistic deployment conditions. Our findings show that proper temporal handling and MNAR-aware preprocessing significantly alter the relative performance of popular anomaly-detection methods, providing practical guidance for designing reliable, leakage-resistant ICS intrusion-detection systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

High density of (pseudo) periodic twin-grain boundaries in molecular beam epitaxy-grown van der Waals heterostructure: MoTe{sub 2}/MoS{sub 2}

Growth of transition metal dichalcogenide heterostructures by molecular beam epitaxy (MBE) promises synthesis of artificial van der Waals materials with controllable layer compositions and separations. Here, we show that MBE growth of 2H-MoTe{sub 2} monolayers on MoS{sub 2} substrates results in a high density of mirror-twins within the films. The grain boundaries are tellurium deficient, suggesting that Te-deficiency during growth causes their formation. Scanning tunneling microscopy and spectroscopy reveal that the grain boundaries arrange in a pseudo periodic “wagon wheel” pattern with only ∼2.6 nm repetition length. Defect states from these domain boundaries fill the band gap and thus give the monolayer an almost metallic property. The band gap states pin the Fermi-level in MoTe{sub 2} and thus determine the band-alignment in the MoTe{sub 2}/MoS{sub 2} interface.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Real-space imaging of periodic nanotextures in thin films via phasing of diffraction data

New properties and exotic quantum phenomena can form due to periodic nanotextures, including Moire patterns, ferroic domains, and topologically protected magnetization and polarization textures. Despite the availability of powerful tools to characterize the atomic crystal structure, the visualization of nanoscale strain-modulated structural motifs remains challenging. Here, we develop nondestructive real-space imaging of periodic lattice distortions in thin epitaxial films and report an emergent periodic nanotexture in a Mott insulator. Specifically, we combine iterative phase retrieval with unsupervised machine learning to invert the diffuse scattering pattern from conventional X-ray reciprocal-space maps into real-space images of crystalline displacements. Our imaging in PbTiO 3 /SrTiO 3 superlattices exhibiting checkerboard strain modulation substantiates published phase-field model calculations. Furthermore, the imaging of biaxially strained Mott insulator Ca 2 RuO 4 reveals a strain-induced nanotexture comprised of nanometer-thin metallic-structure wires separated by nanometer-thin Mott-insulating-structure walls, as confirmed by cryogenic scanning transmission electron microscopy (cryo-STEM). The nanotexture in Ca 2 RuO 4 film is induced by the metal-to-insulator transition and has not been reported in bulk crystals. We expect the phasing of diffuse X-ray scattering from thin crystalline films in combination with cryo-STEM to open a powerful avenue for discovering, visualizing, and quantifying the periodic strain-modulated structures in quantum materials.

Science & Technology - Other Topics↗
DOE OSTI2020

Pattern of perturbations from a coherent quantum inflationary horizon

It is proposed that if quantum states of space-time are coherent on null surfaces, holographic Planck-scale fluctuations of inflationary horizons dominate the formation of primordial scalar curvature perturbations. It is shown that the reduction of quantum states on nearly-spherical emergent horizon surfaces around each observer creates a distinctive pattern whose correlations in the angular domain differ from the standard quantum theory of inflation. Causal constraints are used in a semiclassical model to formulate candidate directional symmetries. It is suggested that this hypothesis could provide a physical explanation for several well known anomalies measured in CMB anisotropy. New exact symmetries are predicted, such as a vanishing temperature correlation function at 90 degrees angular separation, that can be tested with current data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Manufacturing Technology Development for Visible Color Filters

Sandia National Laboratories (SNL) and Rockwell Collins Inc. (RCI) developed new metallic filters for separating red, green, and blue channels. These designs were not based on traditional absorptive dye layers. This technology is well-established, but difficult to change from pixel to pixel as pixels shrink in size and increase in number. These films are also quite lossy: throwing away needed light in low-light applications. The SNL-RCI technology investigated metal plasmonic filters. These patterns are a single thin layer and the metal pattern may be changed from one pixel to the next simply by varying the metal pattern shape or dimensions. This allows adjacent pixels to view different colors with an efficient processing method and without loss of as much loss as the dye technology.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Data for A Role for Differential Rubisco Activase Isoform Expression in C4 Bioenergy Grasses at High Temperature

Rubisco activase (Rca) facilitates the release of sugar-phosphate inhibitors at Rubisco catalytic sites during CO2 fixation. Most plant species express two Rca isoforms, the larger Rca-α and the shorter Rca-β, either by alternative splicing from a single gene or expression from separate genes. The mechanism of Rubisco activation by Rca isoforms has been intensively studied in C3 plants. However, the functional role of Rca in C4 plants where Rubisco and Rca are located in a much higher [CO2] compartment is less clear. In this study, we selected four C4 bioenergy grasses and the model C4 grass setaria ( Setaria viridis ) to investigate the role of Rca in C4 photosynthesis. All five C4 grass species contained two Rca genes, one encoding Rca-α and the other Rca-β, which were positioned closely together in the genomes. A variety of abiotic stress-related motifs were identified in the Rca-α promoter of each grass, and while the Rca-β gene was constantly highly expressed at ambient temperature, Rca-α isoforms were expressed only at high temperature but never surpassed 30% of Rca-β content. The pattern of Rca-α induction on transition to high temperature and reduction on return to ambient temperature was the same in all five C4 grasses. In sorghum ( Sorghum bicolor ), sugarcane ( Saccharum officinarum ), and setaria, the induction rate of Rca-α was similar to the recovery rate of photosynthesis and Rubisco activation at high temperature. This association between Rca-α isoform expression and maintenance of Rubisco activation at high temperature suggests that Rca-α has a functional thermo-protective role in carbon fixation in C4 grasses by sustaining Rubisco activation at high temperature.

Genomics↗
DOE OSTI2016

Enamel ribbons, surface nodules, and octacalcium phosphate in C57BL/6 Amelx -/- mice and Amelx +/- lyonization

Amelogenin is required for normal enamel formation and is the most abundant protein in developing enamel. Methods. Amelx +/+ , Amelx +/- and Amelx -/- molars and incisors from C57BL/6 mice were characterized using RT-PCR, Western blotting, dissecting and light microscopy, immunohistochemistry (IHC), transmission electron microscopy (TEM), scanning electron microscopy (SEM), backscattered SEM (bSEM), nanohardness testing, and X-ray diffraction. No amelogenin protein was detected by Western blot analyses of enamel extracts from Amelx -/- mice. Amelx -/- incisor enamel averaged 20.3±3.3 μm in thickness, or only 1/6th that of the wild-type (122.3±7.9 μm). Amelx -/- incisor enamel nanohardness was 1.6 Gpa, less than half that of wild-type enamel (3.6 Gpa). Amelx +/- incisors and molars showed vertical banding patterns unique to each tooth. IHC detected no amelogenin in Amelx -/- enamel and varied levels of amelogenin in Amelx +/- incisors, which correlated positively with enamel thickness, strongly supporting lyonization as the cause of the variations in enamel thickness. TEM analyses showed characteristic mineral ribbons in Amelx +/+ and Amelx -/- enamel extending from mineralized dentin collagen to the ameloblast. The Amelx -/- enamel ribbons were not well-separated by matrix and appeared to fuse together, forming plates. Furthermore, x-ray diffraction determined that the predominant mineral in Amelx -/- enamel is octacalcium phosphate (not calcium hydroxyapatite). Amelx -/- ameloblasts were similar to wild-type ameloblasts except no Tomes’ processes extended into the thin enamel. Amelx -/- and Amelx +/- molars both showed calcified nodules on their occlusal surfaces. Histology of D5 and D11 developing molars showed nodules forming during the maturation stage. Amelogenin forms a resorbable matrix that separates and supports, but does not shape early secretory stage enamel ribbons. Amelogenin may facilitate the conversion of enamel ribbons into hydroxyapatite by inhibiting the formation of octacalcium phosphate. Finally, amelogenin is necessary for thickening the enamel layer, which helps maintain ribbon organization and development and maintenance of the Tomes process.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Adversarial Ensemble Modeling of Multi-modal Mechanical Properties for Iron-Based Alloys

Mechanical properties of alloys are controlled by their microstructure; and microstructure evolution is controlled by internal and external stressors. Chemical complexity during the alloy processing may result in heterogeneity and observation of multimodal performance patterns. Thus, to ensure the desired performance of stressed components it is important to understand the origin and mechanisms of such behavior. Adversarial ensemble modeling was introduced here to explain multimodal mechanical properties of the iron-based alloys. The modeling results showed that the areas of a single mechanism predominance were contiguous across the alloy compositions clustered by similarity, with sharp and persistent boundaries separating the single-mechanism domains. The marginal compositions resulted in increased competition between the adversarial models but only led to the multimodal behavior when the competing models diverged. Transparency of the clustering method allowed explicit interpretation of the chemistries leading to such competition. The adversarial ensemble was interpreted through transition from ductile to brittle microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Tethered from the Head and from the Tail: The Structure of Hydroxyl-Functionalized Ionic Liquids

Ionic liquids with special functionalities are synthesized with the specific purpose of creating new patterns of interaction in the condensed phase. This Letter discusses the case of alcohol-functionalized ILs, the so-called HFILs, which are part of the larger cohort of task-specific ionic liquids. We find that this small chemical modification can cause massive changes in the liquid landscape when the cationic tails are longer. For prototypical ionic liquids, larger alkyl tails act as separators of charge networks, but in the case of HFILs these become physical charge network linkers. The OH functionality adds a large repertoire of interactions and correlations that were mostly unavailable to traditional ILs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Carrier density oscillation in the photoexcited semiconductor

Abstract The perturbation of a semiconductor from the thermodynamic equilibrium often leads to the display of non-linear dynamics and formation of spatiotemporal patterns due to the spontaneous generation of competing processes. Here, we use scanning ultrafast electron microscopy to show that the transport of hot carriers in the strongly excited semiconductor slows down by turning into an oscillatory process; this is evidenced by the expansions and contractions in the second moment of the distribution. We attribute such a response to the electric field generated by the spatial separation of photo-excited electrons and holes under intrinsic and photo-induced fields; we then introduce a transport model that mimics the experimental observation. Our finding provides a direct imaging evidence for the electrostatic oscillation of hot carriers in the highly excited semiconductor and offers new insights into their dynamics in space and time.

Physics↗
DOE OSTI2018

Centrality fluctuations in heavy-ion collisions

Volume or centrality fluctuations (CF) are one of the main uncertainties for interpreting the centrality dependence of many experimental observables. The CF is constrained by centrality selection based on particle multiplicity in a reference subevent and contributes to observables measured in another subevent. Using a Glauber-based independent source model, we study the influence of CF on several distributions of multiplicity N and eccentricities ε n : p(N), p(ε n ), p(ε n ,ε m ), and p(N,ε n ), where the effects of CF are quantified using multiparticle cumulants of these distributions. In midcentral collisions, a general relation is established between the multiplicity fluctuation and resulting CF in the reference subevent. In ultracentral collisions, where distribution of particle production sources is strongly distorted, we find these cumulants exhibit rich sign-change patterns, due to observable-dependent non-Gaussianity in the underlying distributions. The details of sign-change pattern change with the size of the collision systems. Simultaneous comparison of these different types of cumulants between model prediction and experimental data can be used to constrain the CF and particle production mechanism in heavy-ion collisions. Since the concepts of centrality and CF are expected to fluctuate in the longitudinal direction within a single event, we propose to use the pseudorapidity-separated subevent cumulant method to explore the nature of intraevent fluctuations of centrality and collective dynamics. The subevent method can be applied for any bulk observable that is sensitive to centrality and has the potential to separate different mechanisms for multiplicity and flow fluctuations happening at different timescales. As a result, the forward detector upgrades at the Relativistic Heavy Ion Collider and the Large Hadron Collider will greatly enhance such studies in the future.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Label-free cell-substrate adhesion imaging on plasmonic nanocup arrays

Cell adhesion is a crucial biological and biomedical parameter defining cell differentiation, cell migration, cell survival, and state of disease. Because of its importance in cellular function, several tools have been developed in order to monitor cell adhesion in response to various biochemical and mechanical cues. However, there remains a need to monitor cell adhesion and cell-substrate separation with a method that allows real-time measurements on accessible equipment. In this article, we present a method to monitor cell-substrate separation at the single cell level using a plasmonic extraordinary optical transmission substrate, which has a high sensitivity to refractive index changes at the metal-dielectric interface. We show how refractive index changes can be detected using intensity peaks in color channel histograms from RGB images taken of the device surface with a brightfield microscope. Furthermore, this allows mapping of the nonuniform refractive index pattern of a single cell cultured on the plasmonic substrate and therefore high-throughput detection of cell-substrate adhesion with observations in real time.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Simulation of Taylor flow evaporation for bubble-pump applications

Single-pressure absorption systems incorporate bubble-pump generators (BPGs) for refrigerant separation and passive fluid circulation. In conventional spot-heated BPGs, heat is transferred over a small area, requiring high source temperatures. Distributed-heated BPGs receive thermal input over most of the component surface, enabling low temperature operation. In this investigation, a Volume-of-Fluid phase-change simulation formulation is developed and validated. Here this approach is applied to the evaporating Taylor flow pattern in distributed-heated BPGs. A 2-D axisymmetric simulation is performed, which yields detailed information about the developing heat transfer and two-phase flow phenomena. Results are used to assess predicted trends and sub-models from a 1-D segmented BPG model. Close agreement is obtained between segmented model and simulation results for bubble rise velocity (5–7% deviation), bubble and slug lengths, void fraction (3%), and hydrodynamic pressure drop (18%). Specifying average Taylor bubble lengths from the simulation as an input to the segmented model reduces hydrodynamic pressure drop deviation to 6%. Simulated flow-evaporation heat transfer coefficients are significantly higher than those predicted using analytic models from the literature. A new flow evaporation heat transfer correlation that accounts for developing slug flow effects is proposed, and yields close agreement with simulation results for heat transfer coefficient (AAD = 11%) and overall heat transfer rate (2%). Overall, this investigation provides validation for a distributed-heated BPG modeling approach, which can enable passive refrigeration for diverse applications.

42 ENGINEERING↗
DOE OSTI2019

Spatial soft sweeps: Patterns of adaptation in populations with long-range dispersal

Adaptation in extended populations often occurs through multiple independent mutations responding in parallel to a common selection pressure. As the mutations spread concurrently through the population, they leave behind characteristic patterns of polymorphism near selected loci—so-called soft sweeps—which remain visible after adaptation is complete. These patterns are well-understood in two limits of the spreading dynamics of beneficial mutations: the panmictic case with complete absence of spatial structure, and spreading via short-ranged or diffusive dispersal events, which tessellates space into distinct compact regions each descended from a unique mutation. However, spreading behaviour in most natural populations is not exclusively panmictic or diffusive, but incorporates both short-range and long-range dispersal events. Here, we characterize the spatial patterns of soft sweeps driven by dispersal events whose jump distances are broadly distributed, using lattice-based simulations and scaling arguments. We find that mutant clones adopt a distinctive structure consisting of compact cores surrounded by fragmented “haloes” which mingle with haloes from other clones. As long-range dispersal becomes more prominent, the progression from diffusive to panmictic behaviour is marked by two transitions separating regimes with differing relative sizes of halo to core. We analyze the implications of the core-halo structure for the statistics of soft sweep detection in small genomic samples from the population, and find opposing effects of long-range dispersal on the expected diversity in global samples compared to local samples from geographic subregions of the range. We also discuss consequences of the standing genetic variation induced by the soft sweep on future adaptation and mixing.

Genetics & Heredity↗
DOE OSTI2017

Characterization and differentiation of rock varnish types from different environments by microanalytical techniques

We investigated rock varnishes collected from several locations and environments worldwide by a wide range of microanalytical techniques. These techniques were selected to address the challenges posed by the chemical and structural complexity within the micrometer- to nanometer-sized structures in these geological materials. Femtosecond laser ablation-inductively coupled plasma-mass spectrometry (fs LA-ICP-MS), scanning transmission X-ray microscopy-near edge X-ray adsorption fine structure spectroscopy (STXM-NEXAFS) in combination with scanning electron microscopy (SEM) of focused ion beam (FIB) ultra-thin (100–200 nm) sections, conventional and polarization microscopy, as well as electron paramagnetic resonance (EPR) measurements were used to obtain information about these rock varnishes. Rock varnishes from different environments, which cannot readily be distinguished based on their macroscopic appearance, differ significantly in their constituent elemental mass fractions, e.g., of Mn, Fe, Ni, Co, Ba, and Pb, and their rare earth element (REE) patterns. Structural characteristics such as the particle sizes of embedded dust grains, internal structures such as layers of Mn-, Fe-, and Ca -rich material, and structures such as cavities varied between varnishes from different environments and regions in the world. The EPR spectra were consistent with aged biogenic Mn oxides in all samples, but showed subtle differences between samples of different origin. Our observations allow us to separate rock varnishes into different types, with differences that might be indicators of distinct geneses. Five different types of rock varnish could be distinguished, Type I–V, of which only Type I might be used as potential paleoclimate archive. Each varnish type has specific characteristics in terms of their elemental composition, element distribution, and structures. The combination of element ratios (Mn/Ba, Al/Ni, Mn/REY, Mn/Ce, Mn/Pb, La N /Yb N , and Ce/Ce*), total REE contents, and structures can be used to separate the different types of rock varnish from each other.

58 GEOSCIENCES↗
DOE OSTI2021

Selecting XFEL single-particle snapshots by geometric machine learning

A promising new route for structural biology is single-particle imaging with an X-ray Free-Electron Laser (XFEL). This method has the advantage that the samples do not require crystallization and can be examined at room temperature. However, high-resolution structures can only be obtained from a sufficiently large number of diffraction patterns of individual molecules, so-called single particles. Here, we present a method that allows for efficient identification of single particles in very large XFEL datasets, operates at low signal levels, and is tolerant to background. This method uses supervised Geometric Machine Learning (GML) to extract low-dimensional feature vectors from a training dataset, fuse test datasets into the feature space of training datasets, and separate the data into binary distributions of “single particles” and “non-single particles.” As a proof of principle, we tested simulated and experimental datasets of the Coliphage PR772 virus. We created a training dataset and classified three types of test datasets: First, a noise-free simulated test dataset, which gave near perfect separation. Second, simulated test datasets that were modified to reflect different levels of photon counts and background noise. These modified datasets were used to quantify the predictive limits of our approach. Third, an experimental dataset collected at the Stanford Linear Accelerator Center. The single-particle identification for this experimental dataset was compared with previously published results and it was found that GML covers a wide photon-count range, outperforming other single-particle identification methods. Moreover, a major advantage of GML is its ability to retrieve single particles in the presence of structural variability.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Artifact identification in X-ray diffraction data using machine learning methods

In situ synchrotron high-energy X-ray powder diffraction (XRD) is highly utilized by researchers to analyze the crystallographic structures of materials in functional devices ( e.g. battery materials) or in complex sample environments ( e.g. diamond anvil cells or syntheses reactors). An atomic structure of a material can be identified by its diffraction pattern along with a detailed analysis of the Rietveld refinement which yields rich information on the structure and the material, such as crystallite size, microstrain and defects. For in situ experiments, a series of XRD images is usually collected on the same sample under different conditions ( e.g. adiabatic conditions) yielding different states of matter, or is simply collected continuously as a function of time to track the change of a sample during a chemical or physical process. In situ experiments are usually performed with area detectors and collect images composed of diffraction patterns. For an ideal powder, the diffraction pattern should be a series of concentric Debye–Scherrer rings with evenly distributed intensities in each ring. For a realistic sample, one may observe different characteristics other than the typical ring pattern, such as textures or preferred orientations and single-crystal diffraction spots. Textures or preferred orientations usually have several parts of a ring that are more intense than the rest, whereas single-crystal diffraction spots are localized intense spots owing to diffraction of large crystals, typically >10 µm. In this work, an investigation of machine learning methods is presented for fast and reliable identification and separation of the single-crystal diffraction spots in XRD images. The exclusion of artifacts during an XRD image integration process allows a precise analysis of the powder diffraction rings of interest. When it is trained with small subsets of highly diverse datasets, the gradient boosting method can consistently produce high-accuracy results. The method dramatically decreases the amount of time spent identifying and separating single-crystal diffraction spots in comparison with the conventional method.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗