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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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Electrical parametric data are presented on BSF, BSR, textured 10 ohm cm, 50 micron advanced OAST cells in graphical and tabular form as functions of solar illumination intensity, temperature, and 1 MeV electron fluence.
We present developmentsforthe Sea-going Sky-Scanning Sun-tracking Advanced Research Ra-diometer(SeaSTAR), a ship-basedinstrument for aerosol characterizationover the ocean. Sun-tracking measurements of spectral solar irradiance are madein the conventional manner using filter-photodiode radiometers, and co-aligned polarizing filters are switched into the optical paths of the radiometersto measure polarized sky-radiance measurements. A robust high-performance robot with camera-feedback sun-tracking, and inertial measurement unit feed-forward motion-compensationis used for the sun-and sky-viewing modes respectively. An inverse-kinematics solver is used to point the instrument at the chosen position relative to the sunand ensure that the polarizer axis remains in the desired orientation, allowing the Stokes vector components of the sky radiance to be resolved by rotating the measurement barrel about a third robot axis. SeaSTAR aims to provide a resource for cost-effective validation of aerosol property measurements over the oceans in support of satellite missions such as PACE, SBG and GLIMR.
Multi-stage heat release (MSHR) is a unique phenomenon typically seen in lean/diluted and low- to intermediate-temperature combustion. Despite its relevance to advanced engine operation, the MSHR of multi-component fuels has barely been quantified. This study aims to characterize the MSHR of multi-component fuels in a rapid compression machine (RCM) at conditions representative of advanced combustion engines. New experimental data are first reported in the RCM at an equivalence ratio of 0.4, pressure of 60 bar and temperatures from 702 to 795 K for a research grade, multi-component gasoline surrogate, termed PACE-20. Here, experiments confirm the existence of MSHR for PACE-20 at all temperatures, and reveal the strong inhibiting effect of temperature on MSHR, where increasing temperature inhibits MSHR by suppressing first stage heat release and promoting second and third stage heat release. A detailed chemical kinetic model is also adopted to model the experiments, with good agreement observed. The response of MSHR characteristics to changes in different engine operating parameters (i.e., temperature, pressure, equivalence ratio and CO 2 dilution level) and fuel compositions (i.e., mole fraction of n-pentane, n-heptane, isooctane, cyclopentane, 1-hexene, ethanol and aromatics in PACE-20) is further evaluated via statistical analysis coupling quasi-random sampling, extensive computer experiments and high-dimensional model representation. The change in MSHR is characterized through 8 quantities of interest (QOIs), i.e., duration and extent of each heat release stage, and their standard deviations. The analysis highlights the dependence of the QOIs on the individual parameters as well as their interplays, with temperature exhibiting the strongest impact among all parameters. Furthermore, it is demonstrated that if MSHR is to be facilitated and combustion phasing is to be extended to enable engine operation at higher compression ratios, it is recommended to operate the engine at low intake temperatures, boosted intake pressures and high CO 2 dilutions (i.e., high EGR levels) with gasoline fuels containing more n-pentane and less aromatics.
Conference presentation at Carbon Capture, Utilization, and Storage (CCUS) Conference 2024, Houston, Texas, March 11–13, 2024. Petrophysics and core analysis are pivotal in carbon capture and storage (CCS). An integrated workflow including conventional and advanced well logs and core analysis (CCAL and SCAL) was developed to characterize the Broom Creek Formation as the target reservoir to store CO 2 in a CCS project in North Dakota.
The intersection of accelerator and quantum information science (QIS) offers a unique platform to advance both fields through shared technology and infrastructure. This talk will discuss the synergies which exist between these two vastly different but complementary domains. We demonstrate how we leverage pre-existing infrastructure and knowledge to perform research and development which helps to realize dramatic improvements in both 10 km long accelerators and 10 cm large quantum processors. We will explore niobium superconducting radio-frequency (SRF) cavities, a highly advanced technology that excels in efficiently storing electromagnetic energy, enabling ultra-long photon lifetimes critical for quantum processors and facilitating the characterization of quantum materials with parts-per-billion precision. We will also discuss how advancements in superconducting materials, cryogenic systems, and control techniques help to reduce cost and improve performance for both quantum systems and particle accelerators. Moreover, we will discuss cross-disciplinary applications such as dark-matter searches and demonstrate the convergence of these fields in addressing fundamental scientific questions.
For near-surface contaminant characterization, the accurate prediction of hydrogeological parameters in anisotropic and heterogeneous environments has been a challenge since the last decades. However, recent advances in near-surface geophysics have facilitated the use of geophysical data for hydrogeological characterization in the last few years. A pseudo 3-D high resolution P-wave shallow seismic reflection survey was performed at the P Reactor Area, Savannah River Site, South Carolina in order to delineate and predict migration pathways of a large contaminant plume including trichloroethylene. This contaminant plume originates from the northwest section of the reactor facility that is located within the Upper Atlantic Coastal Plain. The data were collected with 40 Hz geophones, an accelerated weight-drop as seismic source and 1 m receiver spacing with near- and far-offsets of 0.5 and 119.5 m, respectively. In such areas with near-surface contaminants, a detailed subsurface characterization of the vadose zone hydraulic parameters is very important. Indeed, an inexpensive method of deriving such parameters by the use of seismic reflection surveys is beneficial, and our approach uses the relationship between seismic velocity and hydrogeological parameters together with empirical observations relating porosity to permeability and hydraulic conductivity. Shear wave velocity ( V s ) profiles were estimated from surface wave dispersion analysis of the seismic reflection data and were subsequently used to derive hydraulic parameters such as porosity, permeability, and hydraulic conductivity. Additional geophysical data including core samples, vertical seismic profiling, surface electrical resistivity tomography, natural gamma and electrical resistivity logs allowed for a robust assessment of the validity and geological significance of the estimated V s and hydrogeological models. The results demonstrate the usefulness of this approach for the upper 15 m of shallow unconsolidated sediments even though the survey design parameters were not optimal for surface wave analysis due to the higher than desired frequency geophones.
With the advancement of high numerical aperture extreme ultraviolet (EUV) lithography, the new platform will enable chipmakers to achieve critical dimensions of 8 nm. However, resist materials face significant challenges in delivering increased sensitivity while managing rising stochastic variations. We aim to develop comprehensive techniques to characterize the chemical profile of latent images, stored in EUV resists after exposure and postexposure baking, which is essential for understanding the origin of stochastic effects. Infrared photo-induced force microscopy (IR PiFM) is a bimodal atomic force microscopy technique combined with an infrared light source, allowing for simultaneous sub-5 nm topographic and chemical characterization within a localized environment. Critical-dimension resonant soft X-ray scatterometry (CD-RSoXS) provides statistical data that reveal structural and chemical information for comparative analysis. For the first time, IR PiFM has been used to chemically map the latent images of EUV resists (after exposure and postexposure baking) at a record high resolution of 12 nm half-pitch, enabling nondestructive analysis of patterns prior to development. Furthermore, CD-RSoXS offers direct experimental observation and comparison of exposed, postexposure baked, and developed patterns, which align with the IR PiFM results. We demonstrate that the IR PiFM technique offers valuable insights into both high spatial resolution and local chemical information simultaneously. In addition, CD-RSoXS provides statistical results that support our main findings. This hybrid metrology approach leverages a multifaceted dataset by combining the most reliable information from each source, which is essential for a comprehensive understanding of the stochastic effects in EUV lithography processes.
Increasing our understanding of the Earth-atmosphere system has been a scientific and political priority for the last few decades. This system not only touches on environmental science, but it has applicability to our broader understanding of planetary atmospheres in general. While this issue focuses primarily on electromagnetics, other fundamental fields of science, including fluid and thermodynamics play major roles. In recent years, significant research efforts have led to advances in the fields of radiative transfer and electromagnetic scattering from irregularly shaped particles. Recently, several workshops and small conferences have taken place to promote the fusion of these efforts. Late in 2013, for instance, two such meetings took place. The Optical Characterization of Atmospheric Aerosols (OCAA) meeting took place in Smolenice, Slovakia to promote a better understanding of microphysical properties of aerosol particles, and the characterization of such atmospheric particles using optical techniques. A complementary conference was organized in Nagoya, Japan, the 3rd International Symposium on Atmospheric Light Scattering and Remote Sensing (ISALSaRS), whose goal is to fuse the advances achieved in particle characterization with remote-sensing techniques. While the focus of these meetings is slightly different, they represent the same aspects of this rapidly growing field. This Topical Issue is the first of two parts. Within this issue we analyze different aspects of the problem of atmospheric characterization and present a broad overview of the topical area. Research includes theory and experiment, ranging from fundamental microphysical properties of individual aerosol particles to broad characterizations of atmospheric properties. Since this is an active field, we also have encouraged the submission of ideas for new methodologies that may represent the future of the field.
The development and deployment of advanced cladding materials in advanced reactors requires the collection of relevant performance data for each cladding concept. Novel cladding concepts typically undergo significant innovation and iteration over the course of their development, and intention should be given to providing a snapshot of present performance. Thus, a modern heat of the nanostructured ferritic alloy (NFA) 14YWT is produced using state-of-the-art procedures for impurity minimization combined with thermomechanical processing methods designed to optimize fracture toughness, tensile strength, and ductility. This new heat of 14YWT, NFA-2, will provide the Advanced Fuels Campaign with adequate material for upcoming irradiation and mechanical testing campaigns. This report outlines procurement of new gas atomized 14YWT powder, production of four new bars of 14YWT, and subsequent mechanical and microstructural property characterization.
Nitrogen-vacancy (NV) centers in diamond are among the most promising solid-state qubit candidates, owing to their exceptionally long spin coherence times, efficient spin-photon coupling, room-temperature operation, and steadily advancing fabrication and integration techniques. Despite significant progress in the field, atomic-scale characterization and control of individual NV centers have remained elusive. In this work, we utilize a conductive graphene capping layer to enable direct imaging and manipulation of NV - defects via scanning tunneling microscopy (STM). By investigating over 40 individual NV - centers, we identify their spectroscopic signatures and spatial configurations. Our dI/dV conductance spectra reveal the ground state resonance approximately 300 meV below the Fermi level and density-of-states maps uncover a two-lobed wavefunction aligned along the [111] crystallographic direction. Remarkably, we demonstrate the ability to manipulate the charge state of the NV centers from NV - to NV 0 through STM tip-induced gating. This work represents a significant advance in the atomic-scale imaging, spectroscopic characterization, and charge-state manipulation of NV centers, potentially paving the way for future quantum device development.
The labeled release concept was advanced to accommodate a post- Viking mission designed to extend the search, to confirm the presence of, and to characterize any Martian life found, and to obtain preliminary information on control of the life detected. The advanced labeled release concept utilizes four test chambers, each of which contains either an active or heat sterilized sample of the Martian soil. A variety of C-14 labeled organic substrates can be added sequentially to each soil sample and the resulting evolved radioactive gas monitored. The concept can also test effects of various inhibitors and environmental parameters on the experimental response. The current Viking '75 labeled release hardware is readily adaptable to the advanced labeled release concept.
The important 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) decomposition byproduct, 4,6-diamino-5,7-dinitro-benzo-furazan (F 1 ), was prepared in a four-step reaction sequence starting with commercially available materials through a key intermediate, 4,6-dichloro-5,7-dinitro-benzo-furazan. The introduction of azido to 4,6-dichloro-benzo-furazan, followed by nitration and amination gave F 1 in good yield. This new method showed a greatly improved yield and ease of purification than previous methods (the total yield was 30%–40% in seven steps). F 1 is a stable yellow solid with a melting point of 308.7°C and a thermal decomposing temperature of 310.3°C (differential scanning calorimetry at 10°C/min heating rate). The solid-state structure was solved by single-crystal X-ray analyses at low temperatures (100 K). The compound crystallizes in a P 2 1 c space group with the formula C 6 H 4 N 6 O 5 •H 2 O with 12 molecules of F 1 /H 2 O in the unit cell. This compound has eluded complete characterization for almost 50 years. With this information, more advanced decomposition models can be created to further improve critical safety margins in handling the energetic material, TATB.
Organic bilayers or amorphous silica films of a few nanometer thickness featuring embedded molecular wires offer opportunities for chemically separating while at the same time electronically connecting photo- or electrocatalytic components. Such ultrathin membranes enable the integration of components for which direct coupling is not sufficiently efficient or stable. Photoelectrocatalytic systems for the generation or utilization of renewable energy are among the most prominent ones for which ultrathin separation layers open up new approaches for component integration for improving efficiency. Recent advances in the assembly and spectroscopic, microscopic, and photoelectrochemical characterization have enabled the systematic optimization of the structure, energetics, and density of embedded molecular wires for maximum charge transfer efficiency. The progress enables interfacial designs for the nanoscale integration of the incompatible oxidation and reduction catalysis environments of artificial photosystems and of microbial (or biomolecular)-abiotic systems for renewable energy.
Corynebacterium glutamicum, a natural glutamate-producing bacterium adopted for industrial production of amino acids, has been extensively explored recently for high-level biosynthesis of amino acid derivatives, bulk chemicals such as organic acids and short-chain alcohols, aromatics, and natural products, including polyphenols and terpenoids. Here, we review the recent advances with a focus on biosystem design principles, metabolic characterization and modeling, omics analysis, utilization of nonmodel feedstock, emerging CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) tools for Corynebacterium strain engineering, biosensors, and novel strains of C. glutamicum. Further, future research directions for developing C. glutamicum cell factories are also discussed.
This study presents a framework to quantify changes in soil saturation in response to flooding caused by extreme hydrologic perturbation on coastal ecosystems at the interfaces and transition between terrestrial and aquatic systems. Subsurface heterogeneity limits the use of in situ measurements to quantify subsurface flow during flooding due to the spatial discontinuity in the measured data. While geophysical methods, including time-lapse electrical resistivity imaging (ERI), are increasingly used to monitor soil hydrological processes, their abilities to parameterize flow models have been underutilized. This study combines background ERI, ground penetrating radar (GPR), time-lapse ERI, soil characterization, and a numerical flow model developed using an Advanced Terrestrial Simulator (ATS) code to quantify the infiltration pathway and describe the hydrological dynamics during a simulated flooding experiment. We assessed the use of two conceptual models developed using [1] ERI and GPR data that described the stratigraphic distribution, and time-lapse ERI that mapped permeability contrast, and [2] information from a national soil database for capturing changes in saturation. Combining the ERI and GPR results with soil core data revealed the stratigraphic heterogeneity at the site with a silty clay layer from 1 to 2 m between an overlying loamy topsoil and an underlying saturated silty sand. This silty clay layer could restrict deep infiltration. The time-lapse ERI showed up to a 35% decrease in resistivity, which correlated with soil moisture data (R 2 value > 0.53) and revealed preferential infiltration zones used to inform the flow model. Numerical simulation results from both the geophysics- and soil database-informed models quantified changes in soil saturation with calculated soil moistures that agreed with field data. The geophysics-informed model captured more of the system’s variability, reflective of shallow subsurface heterogeneities. The framework presented will serve as a precursor for a robust ecohydrological model that can describe the impacts of extreme events induced by climate change on coastal ecosystems.
High-resolution investigation of cell wall materials has emerged as an important application of biomolecular solid-state NMR (ssNMR). Multidimensional correlation experiments have become a standard method for obtaining sufficient spectral resolution to determine the polymorphic structure of carbohydrates and address biochemical questions regarding the supramolecular organization of cell walls. Here, using plant cellulose and matrix polysaccharides as examples, we will review how the multifaceted complexity of polysaccharide structure is impeding the resonance assignment process and assess the available biochemical and spectroscopic approaches that could circumvent this barrier. We will emphasize the ineffectiveness of the current methods in reconciling the ever-growing dataset and deriving structural information. We will evaluate the protocols for achieving efficient and homogeneous hyperpolarization across the cell wall material using magic-angle spinning dynamic nuclear polarization (MAS-DNP). Critical questions regarding the line-broadening effects of cell wall molecules at cryogenic temperature and by paramagnetic biradicals will be considered. Finally, the MAS-DNP method will be placed into a broader context with other structural characterization techniques, such as cryo-electron microscopy, to advance ssNMR research in carbohydrate and cell wall biomaterials.
Reconstructing the internal properties of hadrons in terms of fundamental quark and gluon de- grees of freedom is a central goal in nuclear and particle physics. This effort lies at the core of major experimental programs, such as the Jefferson Lab 12 GeV program and the upcoming Electron-Ion Collider. A primary challenge is the inherent inverse problem: converting large-scale observational data from collision events into the fundamental QCD-defined densities that characterize the micro- scopic structure of hadronic systems. Recent advances in AI and machine learning have opened new avenues for addressing this challenge using deep learning techniques. A particularly promising direction is the integration of complex theoretical calculations and experimental simulations into a unified framework capable of reconstructing these densities directly from event-level information. In this document, we introduce a key algorithm called LOITS, which enables differentiable program- ming within such a framework, facilitating the use of AI/ML techniques to solve the inverse problem of QCF reconstruction at the event level.
Abstract The Additive Manufacturing (AM) landscape has significantly transformed in alignment with Industry 4.0 principles, primarily driven by the integration of Artificial Intelligence (AI) and Digital Twin (DT). However, current Intelligent Additive Manufacturing (IAM) systems face limitations such as fragmented AI tool usage and suboptimal human-machine interaction (HMI). This paper reviews existing IAM solutions, emphasizing control, monitoring, process autonomy, and end-to-end integration, and identifies key limitations, such as the absence of a high-level controller for global decision-making. To address these gaps, we propose a transition from IAM to Autonomous Additive Manufacturing (AAM), featuring a hierarchical framework with four integrated layers: knowledge, generative solution, operational, and cognitive. In the cognitive layer, AI agents notably enable machines to independently observe, analyze, plan, and execute operations that traditionally require human intervention. These capabilities streamline production processes and expand the possibilities for innovation, particularly in sectors like in-space manufacturing (ISM). Additionally, this paper discusses the role of AI in self-optimization and lifelong learning, positing that the future of AM will be characterized by a symbiotic relationship between human expertise and advanced autonomy, fostering a more adaptive, resilient manufacturing ecosystem.