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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 91 records · Page 5

Applications of “Tender” Energy (1-5 keV) X-ray Absorption Spectroscopy in Life Sciences

The “tender” energy range of 1 to 5 keV, between the energy ranges of most “hard” (>5 keV) and “soft” (<1 keV) synchrotron X-ray facilities, offers some unique opportunities for synchrotron-based X-ray absorption fine structure spectroscopy in life sciences. In particular the K absorption edges of Na through Ca offer opportunities to study local structure, speciation, and chemistry of many important biological compounds, structures and processes. This is an area of largely untapped science, in part due to a scarcity of optimized facilities. Such measurements also entail unique experimental challenges. Lastly, this brief review describes the technique, its experimental challenges, recent progress in development of microbeam measurement capabilities, and several highlights illustrating applications in life sciences.

36 MATERIALS SCIENCE↗

AiiDA: automated interactive infrastructure and database for computational science

Computational science has seen in the last decades a spectacular rise in the scope, breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still performed using the renaissance model of individual artisans gathered in a workshop, under the guidance of an established practitioner. Great benefits could follow instead from adopting concepts and tools coming from computer science to manage, preserve, and share these computational efforts. We illustrate here our paradigm sustaining such vision, based around the four pillars of Automation, Data, Environment, and Sharing. We then discuss its implementation in the open-source AiiDA platform (http://www.aiida.net), that has been tuned first to the demands of computational materials science. AiiDA’s design is based on directed acyclic graphs to track the provenance of data and calculations, and ensure preservation and searchability. Remote computational resources are managed transparently, and automation is coupled with data storage to ensure reproducibility. Last, complex sequences of calculations can be encoded into scientific workflows. We believe that AiiDA’s design and its sharing capabilities will encourage the creation of social ecosystems to disseminate codes, data, and scientific workflows.

Materials Science↗

Managing autonomous materials labs with multi-agent AI and its implications for the science of science

Self-driving lab systems (aka, autonomous experimentation) accelerate research - letting scientists learn faster, spend less resources, and fail smarter in well defined, narrow studies. The next-generation materials lab combines self-driving systems to tackle broader challenges - orchestrating complex research campaigns while optimizing lab resources. We propose that agent-based and agentic artificial intelligence will be an integral part of next-generation lab management and discuss potential implementation scenarios. Additionally, digital and physical sandboxes will allow scientists to evaluate diverse and dynamic research and lab management strategies. Beyond the immediate benefit to lab optimization, such sandboxes will enable realistic computational studies of the philosophy of science (i.e., science of science) to achieve higher level scientific efficiencies.

Computer science↗

The Open Science Cyber Risk Profile: The Rosetta Stone for Open Science and Cybersecurity

The Open Science Cyber Risk Profile (OSCRP) working group has created a document that motivates scientists by demonstrating how improving their security posture reduces the risks to their science. This effort aims to both bridge the communication gap between scientists and IT security professionals and allows for the effective management of risks to open science caused by IT security threats.

97 MATHEMATICS AND COMPUTING↗

AI for Science: Report on the Department of Energy (DOE) Town Halls on Artificial Intelligence (AI) for Science

The report documents the DOE Town Halls held during 2019 at Argonne National Laboratory, Oak Ridge National Laboratory, Lawrence Berkeley National Laboratory, and in Washington, DC. From July to October 2019, the Argonne, Oak Ridge, and Berkeley National Laboratories hosted a series of four town hall meetings attended by more than 1,000 U.S. scientists and engineers. The goal of the town hall series was to examine scientific opportunities in the areas of artificial intelligence (AI), Big Data, and high-performance computing (HPC) in the next decade, and to capture the big ideas, grand challenges, and next steps to realizing these opportunities. In this report and in the Department of Energy (DOE) laboratory community, we use the term “AI for Science” to broadly represent the next generation of methods and scientific opportunities in computing, including the development and application of AI methods (e.g., machine learning, deep learning, statistical methods, data analytics, automated control, and related areas) to build models from data and to use these models alone or in conjunction with simulation and scalable computing to advance scientific research. The AI for Science town hall discussions focused on capturing the transformational uses of AI that employ HPC and/or data analysis, leveraging data sets from HPC simulations or instruments and user facilities, and addressing scientific challenges unique to DOE user facilities and the agency’s wide-ranging fundamental and applied science enterprise.

36 MATERIALS SCIENCE↗

Biological and Environmental Research, Earth and Environmental Systems Sciences Division (formerly Climate and Environmental Sciences Division) Strategic Plan: 2018–2023

The Climate and Environmental Sciences Division (CESD) of the Office of Biological and Environmental Research (BER) is managed within the U.S. Department of Energy’s (DOE) Office of Science. CESD is the intellectual home for fundamental research needed to address key uncertainties arising from the interactions and interdependencies of the atmospheric, terrestrial, subsurface, cryospheric, oceanic, and human-energy components of the Earth system. Using an approach to enhance system predictability, CESD-supported research strives to understand and anticipate how environmental stressors behave within a nonlinear system. These stressors, in turn, can influence the robustness and resilience of U.S. energy infrastructures. Particular emphasis also is placed on understanding how natural and human-derived factors contribute to variabilities and trends spanning local to global scales. By treating DOE environmental challenges as part of the Earth system, CESD also addresses DOE’s unique concerns regarding energy contaminants and wastes. The scope of CESD process research spans scales from molecular to global and durations from nanoseconds to many decades, in order for DOE to achieve its goals involving foundational science, environment, energy, economic, and security.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

The Stewardship Science Academic Alliance: A Model of Education for Fundamental and Applied Low-energy Nuclear Science

The Stewardship Science Academic Alliances (SSAA) were inaugurated in 2002 by the National Nuclear Security Administration of the U. S. Department of Energy. The purpose is to enhance connections between NNSA laboratories and the activities of university scientists and their students in research areas important to NNSA, including low-energy nuclear science. This paper highlights some of the ways that the SSAA fosters education and training of graduate students and postdoctoral scholars in low-energy nuclear science, preparing them for careers in fundamental and applied research and development.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Increasing the Reproducibility and Replicability of Supervised AI/ML in the Earth Systems Science by Leveraging Social Science Methods

Artificial intelligence (AI) and machine learning (ML) pose a challenge for achieving science that is both reproducible and replicable. The challenge is compounded in supervised models that depend on manually labeled training data, as they introduce additional decision-making and processes that require thorough documentation and reporting. We address these limitations by providing an approach to hand labeling training data for supervised ML that integrates quantitative content analysis (QCA)—a method from social science research. The QCA approach provides a rigorous and well-documented hand labeling procedure to improve the replicability and reproducibility of supervised ML applications in Earth systems science (ESS), as well as the ability to evaluate them. Specifically, the approach requires (a) the articulation and documentation of the exact decision-making process used for assigning hand labels in a “codebook” and (b) an empirical evaluation of the reliability” of the hand labelers. In this paper, we outline the contributions of QCA to the field, along with an overview of the general approach. We then provide a case study to further demonstrate how this framework has and can be applied when developing supervised ML models for applications in ESS. With this approach, we provide an actionable path forward for addressing ethical considerations and goals outlined by recent AGU work on ML ethics in ESS.

58 GEOSCIENCES↗

Cross section measurements at LANSCE for defense, science and applications

The Los Alamos Neutron Science Center (LANSCE) has three neutron sources that are used for nuclear science measurements. These sources are driven by an 800 MeV proton linear accelerator and cover an energy range from sub-thermal to hundreds of MeV. Research at the facilities is performed under the auspices of a US DOE user program under which research proposals are rated for merit by a program advisory committee and are scheduled based on merit and availability of beam time. A wide variety of instruments is operated at the neutron flight paths at LANSCE including neutron detector arrays, gamma-ray detector arrays, fission fragment detectors, and charged particle detectors. These instruments provide nuclear data for multiple uses that range from increasing knowledge in fundamental science to satisfying data needs for diverse applications such as nuclear energy, global security, and industrial applications. In addition, highlights of recent research related to cross sections measurements are presented, and future research initiatives are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Materials Design and Discovery: Catalysis and Energy Storage (Mira Early Science Program Final Technical Report): ALCF-2 Early Science Program Technical Report

The investigation and design of new classes of materials for energy and catalysis requires a multi-facetted approach to simulation. Multiple methods are needed to study materials on the length scale 0.1 nm - 10 nm. For simulations where the atomic (and electronic) degrees of freedom are relevant, the methods of choice in the surface science, condensed matter physics, and material science communities are classical molecular dynamics (CMD), Density Functional Theory (DFT), and quantum Monte Carlo (QMC). The original scope for this Early Science Program (ESP) project was to perform fast-accurate DFT calculations on materials for energy and catalysis using the GPAW code on Blue Gene/Q. The types of calculations included significantly reduced time-to-solution on systems sizes accessible on Blue Gene/P (~10, 0000 valence electrons), but also systems which were were at least a factor of two larger (~20, 000 valence electrons). GPAW is a real-space DFT code using the projector augmented wave (PAW) method. DFT calculations on Blue Gene/P were executed on over >100, 000 cores using GPAW; thus it was consider a success on Mira’s predecessor system, Intrepid. One of the co-PIs (NAR), determined that the work necessary to allow the GPAW code for these aforementioned types of calculations could not be accomplish within the time frame of the ESP. This was not simply due to human time required to implement OpenMP parallelism, but also from intrinsic algorithmic limitations in supporting libraries, most notably ScaLAPACK. Additionally, the return-on-investment on $\mathcal{O}(N^3)$ DFT code has become some what tenable at best. NAR argues that what is really needed in preparation for exascale computing and to enable high-fidelity materials research is robust reduced scaling, $\mathcal{O}$(N) or $\mathcal{O}$(N)log(N), DFT approaches. Thus, in agreement with the other co-PIs, the decision was made to purse QMC as a complimentary method on Mira since it would be valuable for the scientific community and could easily leverage the massive parallelism that would be provided by the Blue Gene/Q. We note that the two other atomic-scale methods mentioned here, CMD and DFT, are being pursued by other ESP projects on Mira. The remainder of this report will focus on our progress on QMC.

Benali, Anouar↗

Fusion Energy Sciences Roundtable on Quantum Information Science, May 01-02, 2018

Quantum Information Science (QIS) is poised to revolutionize information technology, with significant impacts in many areas of science and technology. QIS includes general-purpose quantum computation, quantum simulation, quantum sensing and quantum communications. A fifth element is quantum materials, which can enable quantum computing (through physical embodiment of qubits) and which are a frontier of fundamental science in themselves.

97 MATHEMATICS AND COMPUTING↗

PCAST: Joint Statement to Leaders from the United States' President's Council of Advisors on Science and Technology and United Kingdom's Prime Minister's Council for Science and Technology

The President’s Council of Advisors on Science and Technology (PCAST) is the sole body of advisors from outside the federal government charged with making science, technology, and innovation policy recommendations to the President and the White House. Established by Executive Order, it is an independent Federal Advisory Committee comprised of distinguished individuals from industry, academia, and non-profit organizations with a range of perspectives and expertise. On March 14, 2024, PCAST and the UK Council for Science and Technology sent a joint letter to President Biden and Prime Minister Sunak outlining shared priorities for ongoing collaboration and the sharing of expertise between the two Councils.

99 GENERAL AND MISCELLANEOUS↗

Los Alamos Neutron Science Center (LANSCE) Nuclear Science Facilities

The Los Alamos Neutron Science Center (LANSCE) facilities for Nuclear Science consist of a high-energy "white" neutron source (Target 4) with 6 flight paths, three low-energy nuclear science flight paths at the Lujan Center, and a proton reaction area. The neutron beams produced at the Target 4 complement those produced at the Lujan Center because they are of much higher energy and have shorter pulse widths. The neutron sources are driven by the 800-MeV proton beam of the LANSCE linear accelerator. With these facilities, LANSCE is able to deliver neutrons with energies ranging from a milli-electron volt to several hundreds of MeV, as well as proton beams with a wide range of energy, time and intensity characteristics. The facilities, instruments and research programs are described briefly.

43 PARTICLE ACCELERATORS↗

Development of a Freeze-Dried CRISPR-Cas12 Sensor for Detecting Wolbachia in the Secondary Science Classroom

Training the future synthetic biology workforce requires the opportunity for students to be exposed to biotechnology concepts and activities in secondary education. Detecting Wolbachia bacteria in arthropods using polymerase chain reaction (PCR) has become a common way for secondary students to investigate and apply recombinant DNA technology in the science classroom. Despite this important activity, cutting-edge biotechnologies such as clustered regularly interspaced short palindromic repeat (CRISPR)-based diagnostics have yet to be widely implemented in the classroom. To address this gap, we present a freeze-dried CRISPR-Cas12 sensing reaction to complement traditional recombinant DNA technology education and teach synthetic biology concepts. The reactions accurately detect Wolbachia from arthropod-derived PCR samples in under 2 h and can be stored at room temperature for over a month without appreciable degradation. Here, the reactions are easy-to-use and cost less than $40 to implement for a classroom of 22 students including the cost of reusable equipment. We see these freeze-dried CRISPR-Cas12 reactions as an accessible way to incorporate synthetic biology education into the existing biology curriculum, which will expand biology educational opportunities in science, technology, engineering, and mathematics.

59 BASIC BIOLOGICAL SCIENCES↗

Burning Plasma Science: Long Pulse-Materials and Fusion Nuclear Science (Final Project Report)

The series of International Conferences on Fusion Reactor Materials (ICFRM) has been established nearly forty years ago as the major international forum for exchanging and archiving a growing knowledge based on the development of materials for both near and long-term fusion energy systems. The critical role of materials science and engineering in overcoming the technological challenges to making fusion energy a practical reality has been an overarching theme of invited and contributed oral talks as well as the poster session presentations. ICFRM is the premier international forum connecting specialists in the area of development of materials for both near and longer-term fusion energy systems. A central theme is the sharing of new insights, cutting-edge research, and technology in materials science pertaining to the realization of fusion energy. The conference consists of daily overview plenary lectures, invited and contributed oral presentations, and a series of technical poster sessions. In addition to hosted satellite meetings, pre-conference technical tutorials, and stimulating discussions. The 19th International Conference on Fusion Reactor Materials (ICFRM-19) was held in La Jolla, California, U.S.A. during the period October 27 through November 1, 2019. We describe here the conference background, topics, structure, program, schedule and timeline, publications, venue, satellite meetings, short courses and seminars, and finally the budget and expenses.

36 MATERIALS SCIENCE↗

Basic Energy Sciences Roundtable: Foundational Science for Carbon Dioxide Removal Technologies (Brochure)

Basic research to identify and understand the fundamental principles governing carbon dioxide removal processes of capture, conversion, and storage is essential for achieving zero carbon emissions by 2050. In March 2022, the Office of Basic Energy Sciences (BES)—in coordination with the US Department of Energy (DOE) technology Offices of Energy Efficiency and Renewable Energy, Fossil Energy and Carbon Management, and Nuclear Energy—held a roundtable titled, “Foundational Science for Carbon Dioxide Removal Technologies,” to discuss the scientific and technical barriers for CO 2 capture, conversion, and storage. Five priority research opportunities were identified to address these scientific and technical challenges and accelerate progress toward the realization of zero carbon emissions. The PROs are as follows: 1) Master Interfacial Processes of CO2 Transport and Reactivity Across Multiple Length and Time Scales; 2) Create Materials that Simultaneously Exhibit Multiple Properties for CO 2 Capture and Release or Conversion; 3) Discover Unconventional Pathways for Energy-Efficient CO 2 Capture, Release, and Conversion; 4) Control Multiphase Interactions Required for CO 2 Conversion into Minerals and Materials; and 5) Achieve Predictive Understanding of Coupled Processes in Complex Subsurface Geologic Systems for Secure Carbon Storage.

36 MATERIALS SCIENCE↗