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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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NASA NTRS2018

Rapid Analysis and Manufacturing Propulsion Technology (RAMPT)

NASA's strategic plan calls for the development of enabling technologies, improved production methods, and advanced design and analysis tools related to the agency's objectives to expand human presence in the solar system. NASA seeks to advance exploration, science, innovation, benefits to humanity, and international collaboration, as well as facilitate and utilize U.S. commercial capabilities to deliver cargo and crew to space.

factsheet↗
NASA NTRS2020

LUNAR SURFACE GEOSCIENCE TRAINING FOR ASTRONAUTS

Geoscientists have been training and preparing astronauts to observe the Earth from space and explore other planetary surfaces with a legacy that reaches back to the early days of the space program. Continuing this legacy and critical function, a core NASA team has been closely coordinating with the Johnson Space Center (JSC) Flight Operations Directorate (FOD) as well as academic, research institutions, and other governmental partners to conduct a comprehensive geoscience training program that ranges from initial astronaut candidate training to preparing assigned crew for future lunar surface missions. Described below are the three program training phases (summarized in Figure 1), along with recent program highlights and forward planning. Given NASA’s increased planning and development efforts regarding near-term lunar surface science activities, we welcome ideas, concepts, capabilities, collaborations, and innovations to this astronaut training program.

T. G. Graff↗
NASA NTRSDate not supplied

Early Assessments of Crew Timelines for the Lunar Surface Habitat

As NASA progresses towards sustained crewed space missions, crew timelines will become increasingly important to achieving mission goals. While it is desirable to spend as much time as possible during crewed space missions on science activities and experiments, there are a large number of activities that crew members must perform each day in order to maintain both crew and vehicle health and safety. The time available for science activities in space is highly dependent on mandatory tasks required for crew and vehicle health and safety. The different crewed activities need to be planned accordingly long before the start of a mission in order to optimize crew time for science. To begin assessing the potential crew time for available for science, an understanding of the requirements to maintain crew and vehicle health and safety is needed. These additional activities may include sleep, exercise, vehicle maintenance, logistics handling, crew personal time, as well as many other tasks. The remaining time outside of these required tasks, within a reasonable crew work schedule, can be dedicated to science operations. This paper will detail a collaborative effort to analyzing crew times for sustained spaceflight missions and how the results of that analysis are applied to the crew timeline for the proposed Lunar Surface Habitat (SH).To determine the crew time for all of these required activities, an analysis was conducted utilizing defined agency requirements and historical crewed mission data. Predicted crew activity times were integrated into a daily schedule in order to optimize the crew’s time during the mission. This methodology was utilized to produce expected crew timelines for NASA’s proposed Artemis Base Camp (ABC) missions. The current plans for the ABC contain two different sustained habitats, the Pressurized Rover (PR) and the Surface Habitat (SH). While the crew are separated between the two habitats, the timelines for each element are dependent on the other element’s operations, so the two element timelines are formed in conjunction with one another. The results described in this paper, however, will focus solely on the crew timeline in the SH. This paper will explain the methodology behind predicting the required crew time spent in the SH for each activity, and the process of incorporating these predicted crew times into a coherent schedule.

Crew Time↗
NASA NTRSDate not supplied

An LES Survey of Cold-Air Outbreaks Spanning ACTIVATE and COMBLE

Two recent field campaigns offer unique new opportunities to survey coupled aerosol and cloud processes operating within cold-air outbreaks (CAOs). NASA's 2019-2023 Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project provided airborne in situ and remote sensing observations in multiple CAOs off the central US Eastern Seaboard. Contemporaneously, DOE's 2019-2020 Cold-air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) provided continuous ground-based in situ and remote sensing measurements during hundreds of hours of CAO conditions at two sites flanking the Norwegian Sea. Here we highlight lessons learned from contrasting sets of aerosol-aware Lagrangian large-eddy simulation case studies derived from each campaign, as well as outstanding uncertainties. Cases simulated include all of the most extensively sampled CAO flights during ACTIVATE, and a wide range of CAO strengths and mesoscale structures during COMBLE. Each case study is also suitable for simulation with large-scale models in single-column model mode or with a limited-domain approach using periodic boundary conditions. We seek to address the central question: what microphysical process pathways control the Lagrangian evolution of surface and top-of-atmosphere radiative fluxes under ACTIVATE and COMBLE conditions? Many science team members, co-investigators, and collaborators will be gratefully acknowledged in association with their contributions to making this work possible.

cold-air outbreaks↗
NASA NTRSDate not supplied

Coronal Diagnostic Experiment (CODEX)

Understanding solar wind sources and acceleration mechanisms is an overarching solar physics goal. Current models are highly under-constrained due to the limitations of the existing data, particularly in the ~3-10 Rs range. COronal Diagnostic Experiment (CODEX) is designed to deliver the first global, comprehensive data sets that will impose crucial constraints and answer targeted essential questions, including: Are there signatures of hot plasma released into the solar wind from previously closed fields? What are the velocities and temperatures of the density structures that are observed so ubiquitously within streamers and coronal holes? To provide these crucial measurements, NASA’s Goddard Space Flight Center, in collaboration with the Korea Astronomy and Space Science Institute, and Italian National Institute for Astrophysics (INAF) will develop a next-generation coronagraph for the International Space Station. This imaging coronagraph uses multiple filters to obtain simultaneous measurements of electron density, temperature, and velocity within a single instrument. This will be the first time all three have been measured simultaneously for this critical field-of-view, and CODEX achieves these measurements multiple times a day.

Jeff Newmark↗
NASA NTRSDate not supplied

ncompare: A Python Package for Comparing netCDF Structures

Earth science researchers and data engineers have a common problem: they often need to compare data files to see what is different between them. A lot of time is spent developing code to test differences. When it comes to comparing multidimensional data file formats like netCDFs (Network Common Data Form), this is particularly challenging and time-consuming, since there is frequently a need to evaluate the differences between dimension sizes, variable structures, and variable attributes, especially for regression testing. Since netCDFs are widely used in Earth science — with climate models, oceanographic or atmospheric reanalyses, and observational data — improved means of evaluating netCDF files can help enable a wide range of applications. We have developed a reusable open source approach through `ncompare`, which is a Python package for comparing netCDF structures [[https://github.com/nasa/ncompare]]. The `ncompare` tool compares the structure of two Network Common Data Form (NetCDF) files at the command line. It facilitates rapid comparisons by generating a formatted display of the matching and non-matching groups, variables, and associated metadata between two NetCDF datasets. The user has the option to colorize the terminal output for ease of viewing, and `ncompare` can optionally save comparison reports in text, comma-separated value (CSV), and/or Microsoft Excel formats. Despite the availability of tools (such as ncmpidiff or nccmp) that compare the values of variables, there was not previously a readily available, Python-based tool for rapid visual comparisons of group and variable structures, attributes, and chunking. `ncompare` was developed at NASA’s Atmospheric Science Data Center (ASDC) and is a collaboration with NASA Openscapes [[https://nasa-openscapes.github.io]] mentors across 11 of NASA’s data centers. Openscapes’ overarching vision is to support scientific researchers using NASA Earthdata as they migrate their workflows to the cloud. Relevant links: - https://github.com/nasa/ncompare - https://github.com/pyOpenSci/software-submission/issues/146 - https://nasa-openscapes.github.io

Daniel Kaufman↗
DOE OSTI2022

Science of the Van Allen Probes Science Operations Centers

The Van Allen Probes mission operations materialized through a distributed model in which operational responsibility was divided between the Mission Operations Center (MOC) and separate instrument specific SOCs. The sole MOC handled all aspects of telemetering and receiving tasks as well as certain scientifically relevant ancillary tasks. Each instrument science team developed individual instrument specific SOCs proficient in unique capabilities in support of science data acquisition, data processing, instrument performance, and tools for the instrument team scientists. In parallel activities, project scientists took on the task of providing a significant modeling tool base usable by the instrument science teams and the larger scientific community. With a mission as complex as Van Allen Probes, scientific inquiry occurred due to constant and significant collaboration between the SOCs and in concert with the project science team. Planned cross-instrument coordinated observations resulted in critical discoveries during the seven-year mission. Instrument cross-calibration activities elucidated a more seamless set of data products. Specific topics include post-launch changes and enhancements to the SOCs, discussion of coordination activities between the SOCs, SOC specific analysis software, modeling software provided by the Van Allen Probes project, and a section on lessons learned. One of the most significant lessons learned was the importance of the original decision to implement individual team SOCs providing timely and well-documented instrument data for the NASA Van Allen Probes Mission scientists and the larger magnetospheric and radiation belt scientific community.

47 OTHER INSTRUMENTATION↗
NASA NTRS2024

The NASA Facility for Astromaterials Research at the Johnson Space Center – A National Laboratory for Planetary Research

The Astromaterials Research and Exploration Science (ARES) Division at the NASA Johnson Space Center houses a unique combination of laboratories, instruments, infrastructure, technical expertise, and other assets for conducting broad-based world-class planetary research. These facilities have been accessed for decades by hundreds of external scientists, including faculty, post-docs, students, and interns, most at no-cost and on a collaborative basis. With funding through NASA’s Planetary Science Enabling Facilities (PSEF) program, we have established the NASA Facility for Astromaterials Research (NFAR) to expand access to and enhance these laboratories for a diverse and inclusive external user base, focusing on training of the next generation of scientists and, thus, maximizing the science return from research funded by R&A programs in NASA’s Planetary Science Division (PSD). NFAR enables new planetary sample analyses, making new scientific discoveries possible, including training the next generation of planetary scientists. NFAR laboratories are co-located with JSC Curation that houses the NASA-controlled astromaterials collections, thus enabling direct access to both research and curation expertise, to facilitate specialized sample handling and analysis of allocated samples to sample PIs, particularly those affiliated with institutions that historically have limited access to or lack in-house analytical or experimental facilities.

J. I. Simon↗
NASA NTRSDate not supplied

The NASA Facility for Astromaterials Research at the Johnson Space Center – A National Laboratory for Planetary Research

The Astromaterials Research and Exploration Science (ARES) Division at the NASA Johnson Space Center houses a unique combination of laboratories, instruments, infrastructure, technical ex-pertise, and other assets for conducting broad-based world-class planetary research. These facilities have been accessed for decades by hundreds of external scientists, including faculty, post-docs, students, and interns, most at no cost and on a collaborative basis. With funding through NASA’s Planetary Science Enabling Facilities (PSEF) program, we have estab-lished the NASA Facility for Astromaterials Research (NFAR) to expand access to and enhance these labora-tories for a diverse and inclusive external user base, thus maximizing the science return from research funded by R&A programs in NASA’s Planetary Sci-ence Division (PSD). NFAR enables cutting edge planetary sample analyses, making new scientific dis-coveries possible, in addition to training the next-generation of planetary scientists. NFAR laboratories are co-located with JSC Curation that houses all NASA-controlled astromaterials collections, thus ena-bling direct access to both research and curation exper-tise, to facilitate specialized sample handling and anal-ysis of allocated samples (from JSC and other sample collections) to PIs, particularly those affiliated with institutions that historically have limited or no access to in-house analytical or experimental facilities.

J Filiberto↗
DOE OSTI2022

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC Using Quantum Computer Simulators and Quantum Computer Hardware

Machine learning enjoys widespread success in High Energy Physics (HEP) analyses at LHC. However the ambitious HL-LHC program will require much more computing resources in the next two decades. Quantum computing may offer speed-up for HEP physics analyses at HL-LHC, and can be a new computational paradigm for big data analyses in High Energy Physics.We have successfully employed three methods (1) Variational Quantum Classifier (VQC) method, (2) Quantum Support Vector Machine Kernel (QSVM-kernel) method and (3) Quantum Neural Network (QNN) method for two LHC flagship analyses: ttH (Higgs production in association with two top quarks) and H->mumu (Higgs decay to two muons, the second generation fermions). We shall address the progressive improvements in performance from method (1) to method (3).We will present our experiences and results of a study on LHC High Energy Physics data analyses with IBM Quantum Simulator and Quantum Hardware (using IBM Qiskit framework), Google Quantum Simulator (using Google Cirq framework), and Amazon Quantum Simulator (using Amazon Braket cloud service). The work is in the context of a Qubit platform (a gate-model quantum computer). Taking into account the present limitation of hardware access, different quantum machine learning methods are studied on simulators and the results are compared with classical machine learning methods (BDT, classical Support Vector Machine and classical Neural Network). Furthermore, we do apply quantum machine learning on IBM quantum hardware to compare performance between quantum simulator and quantum hardware. The work is performed by an international and interdisciplinary collaboration with the Department of Physics and Department of Computer Sciences of University of Wisconsin, CERN Quantum Technology Initiative, IBM Research Zurich, IBM T.J. Watson Research Center, Fermilab Quantum Institute, BNL Computational Science Initiative, State University of New York at Stony Brook, and Quantum Computing and AI Research of Amazon Web Services. This work pioneers a close collaboration of academic institutions with industrial corporations in the High Energy Physics analyses effort. Though the size of event samples in future HL-LHC physics and the limited number of qubits pose some challenges to the Quantum Machine learning studies for High Energy Physics, more advanced quantum computers with larger number of qubits, reduced noise and improved running time (as envisioned by IBM and Google) may outperform classical machine learning in both classification power and in speed.Although the era of efficient quantum computing may still be years away, we have made promising progress and obtained preliminary results in applying quantum machine learning to High Energy Physics. A PROOF OF PRINCIPLE.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2014

Collaborative WorkBench for Researchers - Work Smarter, Not Harder

It is important to define some commonly used terminology related to collaboration to facilitate clarity in later discussions. We define provisioning as infrastructure capabilities such as computation, storage, data, and tools provided by some agency or similarly trusted institution. Sharing is defined as the process of exchanging data, programs, and knowledge among individuals (often strangers) and groups. Collaboration is a specialized case of sharing. In collaboration, sharing with others (usually known colleagues) is done in pursuit of a common scientific goal or objective. Collaboration entails more dynamic and frequent interactions and can occur at different speeds. Synchronous collaboration occurs in real time such as editing a shared document on the fly, chatting, video conference, etc., and typically requires a peer-to-peer connection. Asynchronous collaboration is episodic in nature based on a push-pull model. Examples of asynchronous collaboration include email exchanges, blogging, repositories, etc. The purpose of a workbench is to provide a customizable framework for different applications. Since the workbench will be common to all the customized tools, it promotes building modular functionality that can be used and reused by multiple tools. The objective of our Collaborative Workbench (CWB) is thus to create such an open and extensible framework for the Earth Science community via a set of plug-ins. Our CWB is based on the Eclipse [2] Integrated Development Environment (IDE), which is designed as a small kernel containing a plug-in loader for hundreds of plug-ins. The kernel itself is an implementation of a known specification to provide an environment for the plug-ins to execute. This design enables modularity, where discrete chunks of functionality can be reused to build new applications. The minimal set of plug-ins necessary to create a client application is called the Eclipse Rich Client Platform (RCP) [3]; The Eclipse RCP also supports thousands of community-contributed plug-ins, making it a popular development platform for many diverse applications including the Science Activity Planner developed at JPL for the Mars rovers [4] and the scientific experiment tool Gumtree [5]. By leveraging the Eclipse RCP to provide an open, extensible framework, a CWB supports customizations via plug-ins to build rich user applications specific for Earth Science. More importantly, CWB plug-ins can be used by existing science tools built off Eclipse such as IDL or PyDev to provide seamless collaboration functionalities.

Ramachandran, Rahul↗
DOE OSTI2021

Crowdsourcing Global Perspectives in Ecology Using Social Media

Transparent, open, and reproducible research is still far from routine, and the full potential of open science has not yet been realized. Crowdsourcing–defined as the usage of a flexible open call to a heterogeneous group of individuals to recruit volunteers for a task –is an emerging scientific model that encourages larger and more outwardly transparent collaborations. While crowdsourcing, particularly through citizen- or community-based science, has been increasing over the last decade in ecological research, it remains infrequently used as a means of generating scientific knowledge in comparison to more traditional approaches. We explored a new implementation of crowdsourcing by using an open call on social media to assess its utility to address fundamental ecological questions. We specifically focused on pervasive challenges in predicting, mitigating, and understanding the consequences of disturbances. In this paper, we briefly review open science concepts and their benefits, and then focus on the new methods we used to generate a scientific publication. We share our approach, lessons learned, and potential pathways forward for expanding open science. Our model is based on the beliefs that social media can be a powerful tool for idea generation and that open collaborative writing processes can enhance scientific outcomes. We structured the project in five phases: (1) draft idea generation, (2) leadership team recruitment and project development, (3) open collaborator recruitment via social media, (4) iterative paper development, and (5) final editing, authorship assignment, and submission by the leadership team. We observed benefits including: facilitating connections between unusual networks of scientists, providing opportunities for early career and underrepresented groups of scientists, and rapid knowledge exchange that generated multidisciplinary ideas. We also identified areas for improvement, highlighting biases in the individuals that self-selected participation and acknowledging remaining barriers to contributing new or incompletely formed ideas into a public document. While shifting scientific paradigms to completely open science is a long-term process, our hope in publishing this work is to encourage others to build upon and improve our efforts in new and creative ways.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

R&D Towards CUPID, a Tonne-Scale Bolometric 0νββ Experiment (Collaborative Proposal)

As endorsed by the 2015 Long Range Plan for Nuclear Science, development of the ton-scale 0νββ experiment with the sensitivity to the effective neutrino mass at the level of 15 meV is one of the highest priorities for the nuclear physics. Bolometric detector based on CUORE design is very well matched to this goal. We have assembled a Collaboration to develop CUPID, a next-generation bolometric 0νββ detector based on CUORE experience and design, but improving upon CUORE sensitivity by more than an order of magnitude.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2022

Earth System Digital Twins (ESDT) Workshop

"The Earth Science Information Partners (ESIP) is hosting an Earth System Digital Twins (ESDT) workshop in collaboration with NASA’s Advanced Information Systems Technology (AIST) Program. The event will bring together science and technology communities to explore the use and benefits of ESDT and their enabling technologies. This workshop is one of the first steps in developing ESDT reference use cases and corresponding technology needs to guide the development of ESDT technologies that will be needed by NASA Earth Science within the next five to ten years. The workshop goals are to: • identify driving Earth science use cases that will benefit from unique ESDT capabilities • identify emerging technologies that will enable such ESDT systems within the next five to ten years • understand opportunities and technologies for federating ESDTs and creating more capable systems"

Earth Science Remote Sensing; Information Systems↗
DOE OSTI2023

Advanced Research Directions on AI for Science, Energy, and Security: Report on Summer 2022 Workshops

Over the past decade, fundamental changes in artificial intelligence (AI)—from foundational to applied—have delivered dramatic insights across a wide breadth of U.S. Department of Energy (DOE) mission space. AI is helping to augment and improve scientific and engineering workflows (e.g., for control, design, and dramatic performance gains through surrogate models) in national security, the Office of Science, and DOE’s applied energy programs. The progress and potential for AI in DOE science was captured in the 2020 “AI for Science” report from the DOE laboratory community in collaboration with academia and industry. Specific scientific areas ready to further leverage the power of AI ranged from the scale and performance of computational models to data analysis to creating new classes of observations using computer vision. Since that report, the scale and scope of scientific AI have accelerated, revealing new, emergent properties that yield insights that go beyond enabling opportunities to being potentially transformative in the way that scientific problems are posed and solved. Thus, under the guidance of both the Office of Science (SC) and the National Nuclear Security Administration (NNSA), the DOE national laboratories organized a series of workshops in 2022 to gather input on new and rapidly emerging opportunities and challenges of scientific AI. This 2023 report is a synthesis of those workshops. The scientific community believes AI can have a foundational impact on a broad range of DOE missions, including science, energy, and national security. Further, DOE has unique capabilities that enable the community to drive progress in scientific use of AI, building on long-standing DOE strengths and investments in computation, data, and communications infrastructure, spanning the Energy Sciences Network (ESnet), the Exascale Computing Project (ECP), and integrative programs such as the NNSA Office of Defense Programs Advanced Simulation and Computing (ASC) and the SC Scientific Discovery through Advanced Computing (SciDAC) programs.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2020

Preparing the JWST Observatory for Science Observations

The James Webb Space Telescope (JWST) is a large (6.5 m) near-and mid-infrared telescope scheduled for launch in 2021. JWST will be used to explore fundamental questions in astrophysics and planetary science, including the evolution of galaxies, the first light of stars, the formation of stars and planets as well as the characterization of exoplanets. To achieve these scientific goals, NASA and its international partners, the Canadian Space Agency (CSA) and the European Space Agency (ESA), have developed four science instruments for JWST and have prepared detailed commissioning plans for each. The NASA Project Science team has also outlined activities to characterize the performance of the Observatory as a whole. These activities include: 1) monitoring of the instrument and telescope cooldown for contamination mitigation; 2) measurements of straylight and other backgrounds for validation of the JWST stray light models as well as instrument background subtraction algorithms; 3) characterization of optical thermal distortion between its hot and cold telescope pointings within the observable field of regard and 4) trending of observatory performance parameters. Each of these activities has been planned in close collaboration with the observatory wavefront, commissioning planning, and science instrument teams. Here we present the plans for these activities, the expected results and how they will impact future guest observer (GO) proposals and JWST science community

Erin C Smith↗
NASA NTRS2015

Atmospheric Research 2014 Technical Highlights

Atmospheric research in the Earth Sciences Division (610) consists of research and technology development programs dedicated to advancing knowledge and understanding of the atmosphere and its interaction with the climate of Earth. The Division's goals are to improve understanding of the dynamics and physical properties of precipitation, clouds, and aerosols; atmospheric chemistry, including the role of natural and anthropogenic trace species on the ozone balance in the stratosphere and the troposphere; and radiative properties of Earth's atmosphere and the influence of solar variability on the Earth's climate. Major research activities are carried out in the Mesoscale Atmospheric Processes Laboratory, the Climate and Radiation Laboratory, the Atmospheric Chemistry and Dynamics Laboratory, and the Wallops Field Support Office. The overall scope of the research covers an end-to-end process, starting with the identification of scientific problems, leading to observation requirements for remote-sensing platforms, technology and retrieval algorithm development; followed by flight projects and satellite missions; and eventually, resulting in data processing, analyses of measurements, and dissemination from flight projects and missions. Instrument scientists conceive, design, develop, and implement ultraviolet, infrared, optical, radar, laser, and lidar technology to remotely sense the atmosphere. Members of the various Laboratories conduct field measurements for satellite sensor calibration and data validation, and carry out numerous modeling activities. These modeling activities include climate model simulations, modeling the chemistry and transport of trace species on regional-to-global scales, cloud resolving models, and developing the next-generation Earth system models. Satellite missions, field campaigns, peer-reviewed publications, and successful proposals are essential at every stage of the research process to meeting our goals and maintaining leadership of the Earth Sciences Division in atmospheric science research. Figure 1.1 shows the 20-year record of peer-reviewed publications and proposals among the various Laboratories. This data shows that the scientific work being conducted in the Laboratories is competitive with the work being done elsewhere in universities and other government agencies. The office of Deputy Director for Atmospheric Research will strive to maintain this record by rigorously monitoring and promoting quality while emphasizing coordination and integration among atmospheric disciplines. Also, an appropriate balance will be maintained between the scientists' responsibility for large collaborative projects and missions and their need to carry out active science research as a principal investigator. This balance allows members of the Laboratories to improve their scientific credentials, and develop leadership potentials. Interdisciplinary research is carried out in collaboration with other laboratories and research groups within the Earth Sciences Division, across the Sciences and Exploration Directorate, and with partners in universities and other government agencies. Members of the Laboratories interact with the general public to support a wide range of interests in the atmospheric sciences. Among other activities, the Laboratories raise the public's awareness of atmospheric science by presenting public lectures and demonstrations, by making scientific data available to wide audiences, by teaching, and by mentoring students and teachers. The Atmosphere Laboratories make substantial efforts to attract and recruit new scientists to the various areas of atmospheric research. We strongly encourage the establishment of partnerships with Federal and state agencies that have operational responsibilities to promote the societal application of our science products. This report describes our role in NASA's mission, provides highlights of our research scope and activities, and summarizes our scientists' major accomplishments during calendar year 2014. The composition of the organization is shown in Figure 1.2 for each code. This report is published in a printed version with an electronic version on our atmospheres Web site, http://atmospheres.gsfc.nasa.gov/.

Technical↗
DOE OSTI2020

Deuterium Concentration Effects on Cell Cycle Progression

Deuterium (D), which is found in natural water at ~ 150 ppm, seems to play an important role in biology. For example, D concentrations above 150 ppm are known to produce toxic effects in many organisms. There is also evidence to suggest D levels significantly less than 150 ppm can cause delays in progression through the normal mitotic cell cycle. Some have even theorized that the D:H ratio in cells may impact an organism’s radiation resistance. Therefore, evaluating the role of D and the D:H ratio in eukaryotic and prokaryotic cells should lead to a better understanding of cell cycle progression and radiation resistance in these organisms. Research in this field has likely been stalled by the limited availability of D 2 O with varying D concentrations needed to accurately study the deuterium effects. However, SRNL can currently manufacture D 2 O in varying concentrations, and we have assembled a unique team of radiation biologists, microbiologists, radiochemists, and health physics to form an interdisciplinary research group to study the cell cycle as a function of D concentration in order to address several fundamental science questions. Proposed work in FY20 was a collaborative effort with Augusta University to utilized BSL-2 mammalian cell lines. Lab work was halted due to the COVID-19 pandemic. An intensive literature review was performed and identified pertinent knowledge gaps that could be filled in future research efforts.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗