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At least 163 records · Page 9

All sources NASA NTRS DOE OSTI

DOE OSTI2017

The role of protected areas in land use/land cover change and the carbon cycle in the conterminous United States

Protected areas (PAs) cover about 22% of the conterminous United States. Understanding their role on historical land use and land cover change (LULCC) and on the carbon cycle is essential to provide guidance for environmental policies. In this study, we compiled historical LULCC and PAs data to explore these interactions within the terrestrial ecosystem model (TEM). We found that intensive LULCC occurred in the conterminous United States from 1700 to 2005. More than 3 million km2 of forest, grassland and shrublands were converted into agricultural lands, which caused 10,607 Tg C release from land ecosystems to atmosphere. PAs had experienced little LULCC as they were generally established in the 20th century after most of the agricultural expansion had occurred. PAs initially acted as a carbon source due to land use legacies, but their accumulated carbon budget switched to a carbon sink in the 1960s, sequestering an estimated 1,642 Tg C over 1700–2005, or 13.4% of carbon losses in non-PAs. We also find that PAs maintain larger carbon stocks and continue sequestering carbon in recent years (2001–2005), but at a lower rate due to increased heterotrophic respiration as well as lower productivity associated to aging ecosystems. It is essential to continue efforts to maintain resilient, biodiverse ecosystems and avoid large-scale disturbances that would release large amounts of carbon in PAs.

Lu, Xiaoliang↗

DOE OSTI2016

Ligand-induced dependence of charge transfer in nanotube–quantum dot heterostructures

As a model system to probe ligand-dependent charge transfer in complex composite heterostructures, we fabricated double-walled carbon nanotube (DWNT) – CdSe quantum dot (QD) composites. Whereas the average diameter of the QDs probed was kept fixed at ~4.1 nm and the nanotubes analyzed were similarly oxidatively processed, by contrast, the ligands used to mediate the covalent attachment between the QDs and DWNTs were systematically varied to include p-phenylenediamine (PPD), 2-aminoethanethiol (AET), and 4-aminothiophenol (ATP). Herein, we have put forth a unique compilation of complementary data from experiment and theory, including results from transmission electron microscopy (TEM), near-edge X-ray absorption fine structure (NEXAFS) spectroscopy, Raman spectroscopy, electrical transport measurements, and theoretical modeling studies, in order to fundamentally assess the nature of the charge transfer between CdSe QDs and DWNTs, as a function of the structure of various, intervening bridging ligand molecules. Specifically, we correlated evidence of charge transfer as manifested by changes and shifts associated with NEXAFS intensities, Raman peak positions, and threshold voltages both before and after CdSe QD deposition onto the underlying DWNT surface. Importantly, for the first time ever in these types of nanoscale composite systems, we have sought to use theoretical modeling to justify and account for our experimental results. Finally, our overall data suggest that (i) QD coverage density on the DWNTs varies, based upon the different ligand pendant groups used and that (ii) the presence of a π-conjugated carbon framework within the ligands themselves and the electron affinity of the pendant groups collectively play important roles in the resulting charge transfer from QDs to the underlying CNTs.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Materials characterization: Can artificial intelligence be used to address reproducibility challenges?

Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace.

Materials Science↗

DOE OSTI2025

How initial conditions-, structural-, and parameter-based model uncertainty interact and influence predictions in permafrost ecosystems: Modeling Archive

This dataset contains model output and input data, as well as source code examples for the Terrestrial Ecosystem Model with the Dynamic Vegetation Model and Dynamic Organic Soil (DVM-DOS-TEM) for the field sites Imnavait creek and the Bonanza creek Long Term Ecological Research Network (LTER). The data covers simulations from the last glacial maximum (LGM) until 2100 for a selection of paleo scenarios, setting the mean temperature of the LGM up to 10°C lower than pre-industrial conditions. The model structure was modulated to represent various model versions, and this dataset contains the relevant changes in the source code. The raw output data, the processed statistical data, the setup and processing scripts as well as parameter value distribution files from a parameter sensitivity analysis are included as well. Model outputs include active layer depth, organic soil carbon, soil layer depths, gross primary productivity (GPP) with and without nitrogen limitation, net primary productivity (NPP), soil liquid water content, heterotrophic, maintenance, and growth respiration, soil temperature, and vegetation carbon (*.nc files). The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research.Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Reducing uncertainty of high-latitude ecosystem models through identification of key parameters

Abstract Climate change is having significant impacts on Earth’s ecosystems and carbon budgets, and in the Arctic may drive a shift from an historic carbon sink to a source. Large uncertainties in terrestrial biosphere models (TBMs) used to forecast Arctic changes demonstrate the challenges of determining the timing and extent of this possible switch. This spread in model predictions can limit the ability of TBMs to guide management and policy decisions. One of the most influential sources of model uncertainty is model parameterization. Parameter uncertainty results in part from a mismatch between available data in databases and model needs. We identify that mismatch for three TBMs, DVM-DOS-TEM, SIPNET and ED2, and four databases with information on Arctic and boreal above- and belowground traits that may be applied to model parametrization. However, focusing solely on such data gaps can introduce biases towards simple models and ignores structural model uncertainty, another main source for model uncertainty. Therefore, we develop a causal loop diagram (CLD) of the Arctic and boreal ecosystem that includes unquantified, and thus unmodeled, processes. We map model parameters to processes in the CLD and assess parameter vulnerability via the internal network structure. One important substructure, feed forward loops (FFLs), describe processes that are linked both directly and indirectly. When the model parameters are data-informed, these indirect processes might be implicitly included in the model, but if not, they have the potential to introduce significant model uncertainty. We find that the parameters describing the impact of local temperature on microbial activity are associated with a particularly high number of FFLs but are not constrained well by existing data. By employing ecological models of varying complexity, databases, and network methods, we identify the key parameters responsible for limited model accuracy. They should be prioritized for future data sampling to reduce model uncertainty.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Automated TEM Reveals Intercrystalline Correlations of Conjugated Polymers

Transmission electron microscopy (TEM) continues to transform polymer science by revealing key aspects of chain packing, phase separation and nanoscale structure. The development of instrumentation and data analyses tools is driving the field forward and enabling new experiments. Here, we use automated high-resolution TEM (HRTEM) and image processing to identify the structure of a conjugated polymer used in organic electronics. Analysis of more than 600 HRTEM images reveals lattice parameters and orientation correlations between crystals, including the preferred alignment of neighboring crystals along the same crystallographic direction that is likely the result of liquid crystalline order.

Fair, Ryan A. [Pennsylvania State University, Univ↗

DOE OSTI2022

Understanding the Influence of Receptive Field and Network Complexity in Neural Network-Guided TEM Image Analysis

Abstract Trained neural networks are promising tools to analyze the ever-increasing amount of scientific image data, but it is unclear how to best customize these networks for the unique features in transmission electron micrographs. Here, we systematically examine how neural network architecture choices affect how neural networks segment, or pixel-wise separate, crystalline nanoparticles from amorphous background in transmission electron microscopy (TEM) images. We focus on decoupling the influence of receptive field, or the area of the input image that contributes to the output decision, from network complexity, which dictates the number of trainable parameters. For low-resolution TEM images which rely on amplitude contrast to distinguish nanoparticles from background, we find that the receptive field does not significantly influence segmentation performance. On the other hand, for high-resolution TEM images which rely on both amplitude and phase-contrast changes to identify nanoparticles, receptive field is an important parameter for increased performance, especially in images with minimal amplitude contrast. Rather than depending on atom or nanoparticle size, the ideal receptive field seems to be inversely correlated to the degree of nanoparticle contrast in the image. Our results provide insight and guidance as to how to adapt neural networks for applications with TEM datasets.

42 ENGINEERING↗

DOE OSTI2013

TRANSMISSION ELECTRON MICROSCOPY OF Al-RICH SILICATE STARDUST FROM ASYMPTOTIC GIANT BRANCH STARS

We report on transmission electron microscopy (TEM) investigations of two mineralogically unusual stardust silicates to constrain their circumstellar condensation conditions. Both grains were identified by high spatial resolution nano secondary ion mass spectrometry (NanoSIMS) in the Acfer 094 meteorite, one of the most pristine carbonaceous chondrites available for study. One grain is a highly crystalline, highly refractory (Fe content < 0.5 at%), structurally undisturbed orthopyroxene (MgSiO{sub 3}) with an unusually high Al content (1.8 {+-} 0.5 at%). This is the first TEM documentation of a single crystal pyroxene within the complete stardust silicate data set. We interpret the microstructure and chemistry of this grain as being a direct condensate from a gas of locally non-solar composition (i.e., with a higher-than-solar Al content and most likely also a lower-than-solar Mg/Si ratio) at (near)-equilibrium conditions. From the overabundance of crystalline olivine (six reported grains to date) compared to crystalline pyroxene (only documented as a single crystal in this work) we infer that formation of olivine over pyroxene is favored in circumstellar environments, in agreement with expectations from condensation theory and experiments. The second stardust silicate consists of an amorphous Ca-Si rich material which lacks any crystallinity based on TEM observations in which tiny (<20 nm) hibonite nanocrystallites are embedded. This complex assemblage therefore attests to the fast cooling and rapidly changing chemical environments under which dust grains in circumstellar shells form.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2016

Enabling the High Level Synthesis of Data Analytics Accelerators

Conventional High Level Synthesis (HLS) tools mainly tar- get compute intensive kernels typical of digital signal pro- cessing applications. We are developing techniques and ar- chitectural templates to enable HLS of data analytics appli- cations. These applications are memory intensive, present fine-grained, unpredictable data accesses, and irregular, dy- namic task parallelism. We discuss an architectural tem- plate based around a distributed controller to efficiently ex- ploit thread level parallelism. We present a memory in- terface that supports parallel memory subsystems and en- ables implementing atomic memory operations. We intro- duce a dynamic task scheduling approach to efficiently ex- ecute heavily unbalanced workload. The templates are val- idated by synthesizing queries from the Lehigh University Benchmark (LUBM), a well know SPARQL benchmark.

Data Analytics↗

DOE OSTI2015

Crystal and electronic structures of two new iron selenides: Ba{sub 4}Fe{sub 3}Se{sub 10} and BaFe{sub 2}Se{sub 4}

The new ternary selenides, Ba{sub 4}Fe{sub 3}Se{sub 10} and BaFe{sub 2}Se{sub 4,} were synthesized from a reaction of appropriate amounts of elements at high temperature in a silica sealed tube, and their structures were resolved using X-ray single crystal diffraction. BaFe{sub 2}Se{sub 4} crystallizes in the tetragonal space group I4/m with a=8.008(9) Å and c=5.483(3) Å as cell parameters. It is a new compound with a structure isotypical to the sulfide BaFe{sub 2}S{sub 4} which belongs to the infinitely adaptive structures series Ba{sub 1+x}Fe{sub 2}S{sub 4}. The second compound, Ba{sub 4}Fe{sub 3}Se{sub 10}, crystallizes in the monoclinic space group P2{sub 1}/n with a=8.8593(1) Å, b=8.8073(1) Å, c=12.2724(1) Å and β=109.037(6)° as cell parameters. It exhibits an original structure with a new type of iron selenide polyhedra. These data were consistent with the powder X-ray diffraction and TEM analyses. Their electronic structures point towards metallicity and electronic correlations for both selenides. - Graphical abstract: Experimental [010] oriented ED pattern and corresponding HREM image of Ba{sub 4}Fe{sub 3}Se{sub 10}. Image calculated with a focus and thickness to 15nm and 8 nm respectively is inserted. Bright contrasts are correlated to Se rows belonging to FeSe{sub 3}(Se{sub 2}){sup 2−}–FeSe{sub 6}–FeSe{sub 3}(Se{sub 2}){sup 2−} trimers. The corresponding structure projection is also shown. - Highlights: • Two new barium iron selenide compounds. • An original structure type Ba4Fe3Se10. • Electronic structure calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Predicting the viability of beta-lactamase: How folding and binding free energies correlate with beta-lactamase fitness

One of the long-standing holy grails of molecular evolution has been the ability to predict an organism’s fitness directly from its genotype. With such predictive abilities in hand, researchers would be able to more accurately forecast how organisms will evolve and how proteins with novel functions could be engineered, leading to revolutionary advances in medicine and biotechnology. In this work, we assemble the largest reported set of experimental TEM-1 β-lactamase folding free energies and use this data in conjunction with previously acquired fitness data and computational free energy predictions to determine how much of the fitness of β-lactamase can be directly predicted by thermodynamic folding and binding free energies. We focus upon β-lactamase because of its long history as a model enzyme and its central role in antibiotic resistance. Based upon a set of 21 β-lactamase single and double mutants expressly designed to influence protein folding, we first demonstrate that modeling software designed to compute folding free energies such as FoldX and PyRosetta can meaningfully, although not perfectly, predict the experimental folding free energies of single mutants. Interestingly, while these techniques also yield sensible double mutant free energies, we show that they do so for the wrong physical reasons. We then go on to assess how well both experimental and computational folding free energies explain single mutant fitness. We find that folding free energies account for, at most, 24% of the variance in β-lactamase fitness values according to linear models and, somewhat surprisingly, complementing folding free energies with computationally-predicted binding free energies of residues near the active site only increases the folding-only figure by a few percent. This strongly suggests that the majority of β-lactamase’s fitness is controlled by factors other than free energies. Overall, our results shed a bright light on to what extent the community is justified in using thermodynamic measures to infer protein fitness as well as how applicable modern computational techniques for predicting free energies will be to the large data sets of multiply-mutated proteins forthcoming.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Toroid Signal Processing Container with Redis Integration

This report presents the research, design, and implementation of a Toroid Signal Processing Container with Redis Integration, conducted during my internship assignment. The project focused on developing a modular signal processing sys- tem capable of performing baseline correction, droop compensation, and real- time data communication for beam diagnostic signals. Using Redis as a message broker and configuration manager, the system ensures modularity, scalability, and efficient inter-process communication. Signal correction algorithms, such as Asymmetric Least Squares (ALS) baseline smoothing and a numerical droop correction approach, were implemented and tested using Linac beam data. The results confirm that this system improves signal fidelity and supports real-time analysis requirements, offering valuable contributions to the field of beam instrumentation and data acquisition systems.

Bowers, Elliot [Cabrillo Coll.]↗

NASA NTRS1977

Effects of mismatch on group delay of microwave transmission

Calculation method can be applied to transmission lines operating in TEM mode or to single-mode propagation in waveguides. Derived data are useful for estimating limits on variation of group delay with frequency or in determining how much discontinuity reduction is necessary to achieve given accuracy in predicting group delay.

Beatty, R. W.↗

NASA NTRS1992

Modeling of planar quasi-TEM superconducting transmission lines

An application of the phenomenological loss equivalence method (Lee and Itoh, 1989) in modeling the microwave behavior of planar quasi-TEM superconducting transmission lines is presented. For validation of the model, data are used from measurements of a YBCO superconducting thin-film coplanar-waveguide lowpass filter on a lanthanum aluminate substrate. Measured and modeled S-parameters of an existing superconducting coplanar waveguide lowpass filter agree to within 0.3 dB in magnitude and 0.5 radians in phase. Extracted values for penetration depth and real part of the conductivity of the superconducting film are within 10 percent of other researchers' findings.

Antsos, Dimitrios↗

NASA NTRS2002

Thermophysical ESEM Characterization of Carbon Fibers

Coefficients of thermal expansion (CTE) of carbon fibers create residual stresses in aggressive manufacturing and service environments. In this effort, environmental scanning electron microscope (ESEM) is used for in situ observations of a carbon fiber cross-section up to 1000 C in order to evaluate the much neglected transverse CTE. The perimeter of fiber cross-section is calculated with the Scion image processing program from images that were taken at every 100 C increments. CTE values are calculated by linear regression of the strain data based on the perimeter changes. Furthermore, through SEM, WDS and TEM observations, we are in the process of bringing an interactive rationale between CTE, crystallinity and surface roughness of carbon fibers.

Sue, Jiwoong↗

DOE OSTI2025

Toroid Signal Processing Container with Redis Integration

Bowers, Elliot [Cabrillo Coll.; Fermilab]↗

DOE OSTI2019

Structural, Morphological, Raman, and Mössbauer Studies on (La{sub 0.8}Ca{sub 0.2}){sub 1−x}Bi{sub x}FeO{sub 3} (x = 0.0, 0.1, and 0.2) Compounds

(La{sub 0.8}Ca{sub 0.2}){sub 1−x}Bi{sub x}FeO{sub 3} (x = 0.0, 0.1, and 0.2) compounds were prepared by the sol-gel method, using the citric acid route. Structural and microstructural characteristics of the samples were studied by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and Raman spectroscopy. Mössbauer spectroscopy was also used for magnetic characterization and structural confirmation of X-ray data. Refinement of XRD patterns indicated that the compounds crystallize in an orthorhombic structure, Pnma space group. The SEM measurements showed that the morphology of the samples is characterized by the existence of large agglomerates, while TEM confirmed the fine particle size. Raman spectra revealed that, when increasing temperature, the level of oxygen species adsorbed at the surface of the particles increases progressively. Room temperature Mössbauer spectroscopy data revealed the existence of two octahedral magnetic sites in the structure of the compounds, which is compatible with XRD data, and a small percentage of a paramagnetic phase in the samples, which increases with Bi-content.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

NASA NTRS2010

An Acoustical Comparison of Sub-Scale and Full-Scale Far-Field Measurements for the Reusable Solid Rocket Motor

Recently, members of the Marshall Space Flight Center (MSFC) Fluid Dynamics Branch and Wyle Labs measured far-field acoustic data during a series of three Reusable Solid Rocket Motor (RSRM) horizontal static tests conducted in Promontory, Utah. The test motors included the Technical Evaluation Motor 13 (TEM-13), Flight Verification Motor 2 (FVM-2), and the Flight Simulation Motor 15 (FSM-15). Similar far-field data were collected during horizontal static tests of sub-scale solid rocket motors at MSFC. Far-field acoustical measurements were taken at multiple angles within a circular array centered about the nozzle exit plane, each positioned at a radial distance of 80 nozzle-exit-diameters from the nozzle. This type of measurement configuration is useful for calculating rocket noise characteristics such as those outlined in the NASA SP-8072 "Acoustic Loads Generated by the Propulsion System." Acoustical scaling comparisons are made between the test motors, with particular interest in the Overall Sound Power, Acoustic Efficiency, Non-dimensional Relative Sound Power Spectrum, and Directivity. Since most empirical data in the NASA SP-8072 methodology is derived from small rockets, this investigation provides an opportunity to check the data collapse between a sub-scale and full-scale rocket motor.

Haynes, Jared↗