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The structural and magnetic properties of quasi-one-dimensional (1D) spin-chain compounds Ca 3 Co 2-x Fe x O 6 (x = 0, 0.1, 0.2 and 0.3) synthesized by a sol-gel method have been systematically studied by means of X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), neutron powder diffraction (NPD), dc and ac magnetization measurements. The samples adopt a rhombohedral crystal structure with the space group R-3c in a temperature range of 5–100 K. Fe ions are found to locate at the trigonal prism Co2 crystallographic sites. Fe doping converts some of low-spin Co 3+ ions at Co1 octahedral sites into Co 2+ ions. For the high-temperature paramagnetic phase, the Curie paramagnetic temperature θp changes its sign from positive to negative at x = 0.2, implying that the dominant magnetic interaction is driven from ferromagnetic (FM) to antiferromagnetic (AFM) upon Fe substitution. A partial low-spin to high-spin state crossover of the Co1 ions is observed at high Fe-doping level x = 0.3. All the samples exhibit a long-range spin-density wave (SDW) AFM ordering below TN, followed by a spin-glass-like transition at Tf. These low-temperature magnetic phases were significantly suppressed upon Fe doping. The Fe substitution in Ca 3 Co 2-x Fe x O 6 weakened both intrachain and interchain magnetic interactions, caused by enhanced magnetic disorder due to the different magnetic characters of Ising Co and Heisenberg Fe spins.
A new Vision for Space Exploration was announced earlier this year by U.S. President George W. Bush. NASA has evaluated on-going programs for strategic alignment with this vision. The evaluation proceeded at a rapid pace and is resulting in changes to the scope and focus of experimental research that will be conducted in support of the new vision. The existing network of researchers in the physical sciences - a highly capable, independent, and loosely knitted community - typically have shared conclusions derived from their work within appropriate discipline-specific peer reviewed journals and publications. The initial result of introducing this Vision for Space Exploration has been to shift research focus from a broad coverage of numerous, widely varying topics into a research program focused on a nearly-singular set of supporting research objectives to enable advances in space exploration. Two of these traditional physical science research disciplines, Combustion Science and Fluid Physics, are implementing a course adjustment from a portfolio dominated by "Fundamental Science Research" to one focused nearly exclusively on supporting the Exploration Vision. Underlying scientific and engineering competencies and infrastructure of the Microgravity Combustion Science and Fluid Physics disciplines do provide essential research capabilities to support the contemporary thrusts of human life support, radiation countermeasures, human health, low gravity research for propulsion and materials and, ultimately, research conducted on the Moon and Mars. A perspective on how these two research disciplines responded to the course change will be presented. The relevance to the new NASA direction is provided, while demonstrating through two examples how the prior investment in fundamental research is being brought to bear on solving the issues confronting the successful implementation of the exploration goals.
The liquid metal jetting droplet-on-demand technology provides many potential benefits to metal additive manufacturing. However, much is left to understand the operational margins to reliably create stable, satellite-free droplets. We elucidate these margins by experimentally probing the unexplored extremities of a recently proposed printability window based on the jet Weber (Wejet) and Ohnesorge (Z = 1/Oh) numbers. We generated stable droplets for 2 ≲ Wejet ≲ 4 for all tested Z = 339 to 2526—well beyond the bounds of any previously published printability window—suggesting that additional physics may be needed to understand droplet-on-demand in the high Z regime.
Abstract Ecosystem biomimicry is a promising pathway for sustainable development. However, while typical form- and process-level biomimicry is prevalent, system-level ecosystem biomimicry remains a nascent practice in numerous engineering fields. This critical review takes an interdisciplinary approach to synthesize trends across case studies, evaluate design methodologies, and identify future opportunities when applying ecosystem biomimicry to engineering practices, including cyber systems (CS), physical systems (PS), and cyber-physical systems (CPS). After systematically sourcing publications from major databases, the papers were first analyzed at a meta level for their bibliographic context and for statistical correlations among categorical variables. Then, we investigated deeper into the engineering applications and design methodologies. Results indicate that CPS most frequently mimic organisms and ecosystems, while CS and PS frequently mimic populations-communities and molecules-tissues-organ systems, respectively (statistically highly significant). An indirect approach is most often used for mimicry at organizational levels from populations to ecosystems, while a direct approach frequently suits levels from molecules to organisms (highly significant). Dominant themes across engineering applications include symbiotic organism search algorithms for CS and ecological network analysis for CPS, while PS are highly diverse. For design methodologies, this work summarizes and details ten well-documented biomimetic process models among literature, which addresses an outdated concern for a lack of systematic methods for ecosystem biomimicry. In addition to the Biomimetics Standard ISO 18458, these methods include the Natural Step and Techno-Ecological Synergy framework, among others. Further, the analyses revealed future opportunities from less utilized design methods (e.g. interdisciplinary teams tackling indirect, ecosystem-level projects) to well-established engineering concepts ready for technological advancement (e.g. implementing membrane computing for physical applications). For future studies, this review provides a comprehensive reference for ecosystem biomimetic design practices and application opportunities across multiple engineering domains.
I was invited to give a seminar to students and faculty of nuclear engineering and radiological sciences (NERS) at the university of Michigan, Ann Arbor. the presentation is about reactor physics capaibility at INL
The National Ignition Facility (NIF) is the world’s largest and most energetic laser facility. The NIF system is designed to produce high energy density (temperature and pressure) conditions through the application of its 192 laser beams. One of the users of NIF is the opacity platform developed to study the opacities at temperatures and densities relevant to the solar interior and stellar evolution. The platform was developed to study iron (Fe) opacity at temperatures relevant to the solar interior. The opacity campaign uses spectrometers to gather data. Spectrometers utilize crystals to produce x-ray spectra that are recorded on time-integrated and time-resolved detectors. The opacity spectrometer (OpSpec) currently fielded and in use at NIF uses a time integrated film channel to collect data. The opacity spectrometer time resolved (OpSpecTR) will utilize novel hCMOS detectors to capture time resolved images of spectra of interest. The key stakeholders identified for OpSpecTR included the physicists responsible for OpSpec and OpSpecTR, the Target Area Science and Engineering (TASE) department at NIF, the NIF and Photon Science (NIF & PS) Opacity program, the Nevada National Security Site (NNSS) Physics and Engineering program, the Sandia hCMOS manufacturing and testing program, and the Los Alamos National Laboratory (LANL) program sponsor. The Target and Experimental Operations (TEXOPS) was identified as a key stakeholder because the group includes the individuals that will physically interact with the OpSpecTR system as it participates in NIF experiments. The opacity platform collects data in a unique orientation relative to the existing diagnostics fielded at NIF. The existing infrastructure at NIF uses a diagnostic manipulator (DIM) to insert the diagnostic near the target chamber center to collect data during a NIF shot. Existing diagnostics collect data through the center line of the DIM axis and collect relevant data perpendicular to this axis. The opacity platform requires crystals mounted in a specific orientation which requires data collection parallel to the DIM axis. This deviation from standard NIF practices was a key factor in developing requirements.
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In the past year, the NASA s Office of Biological and Physical Research (OBPR) has formulated a long term plan to perform strategical and fundamental research bringing together physics, chemistry, biology, and engineering to solve problems needed for current and future agency mission goals. Materials Science is one of basic disciplines within the Enterprise s Division of Physical Sciences Research. The Materials Science Program participates to utilize effective use of International Space Station (ISS) and various world class ground laboratory facilities to solve new scientific and technology questions and transfer these results for public and agency benefits. The program has recently targeted new investigative research in strategic areas necessary to expand NASA knowledge base for exploration of the universe and some of these experiments will need access to the microgravity of space. The program is implementing a wide variety of traditional ground and flight based research related types of fundamental science related to materials crystallization, fundamental processing, and properties characterization in order to obtain basic understanding of various phenomena effects and relationships to the structures, processing, and properties of materials. , In addition new initiatives in radiation protection, materials for propulsion and In-space fabrication and repair focus on research helping the agency solve problems needed for future transportation into the solar system. A summary of the types and sources for this research is presented including those experiments planned for a low gravity environment. Areas to help expand the science basis for NASA future missions are described. An overview of the program is given including the scope of the current and future NASA Research Announcements with emphasis on new materials science initiatives. A description of the planned flight experiments to be conducted on the International Space Station program along with the planned facility class Materials Science Research Rack (MSRR) and Microgravity Glovebox (MSG) type investigations. Some initial results from the first three materials experiments are given.
The ultrafast excited state pathways and dynamics of NiII-bipyridine complexes influence the yield of photochemical processes involved in their catalytic cross-coupling reactions. Here we present ultrafast Ni K x-ray emission spectroscopy (XES) and x-ray solution scattering (XSS) of a NiII-bipyridine aryl halide complex, [Ni(t-Bubpy)(o-tol)Br], to quantify the excited state population dynamics and structural changes of the pre-catalyst. Due to the local spin-sensitivity of XES, the population dynamics of metal-to-ligand charge transfer (MLCT) and metal-centered (MC) excited states is established. A rapid ground state recovery pathway is newly identified, representing a significant deactivation pathway during photocatalysis. Furthermore, the pseudotetrahedral structure of the long-lived MC excited state is unambiguously identified and refined by XSS. The results advance our understanding of the ultrafast relaxation mechanisms that impact the photocatalytic mechanism and yield for NiII-bipyridine aryl halide cross-coupling catalysts.
Abstract Predicting complex dynamics in physical applications governed by partial differential equations in real-time is nearly impossible with traditional numerical simulations due to high computational cost. Neural operators offer a solution by approximating mappings between infinite-dimensional Banach spaces, yet their performance degrades with system size and complexity. We propose an approach for learning neural operators in latent spaces, facilitating real-time predictions for highly nonlinear and multiscale systems on high-dimensional domains. Our method utilizes the deep operator network architecture on a low-dimensional latent space to efficiently approximate underlying operators. Demonstrations on material fracture, fluid flow prediction, and climate modeling highlight superior prediction accuracy and computational efficiency compared to existing methods. Notably, our approach enables approximating large-scale atmospheric flows with millions of degrees, enhancing weather and climate forecasts. Here we show that the proposed approach enables real-time predictions that can facilitate decision-making for a wide range of applications in science and engineering.
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