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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 NTRS1988

Automation of surface observations program

At present, surface weather observing methods are still largely manual and labor intensive. Through the nationwide implementation of Automated Surface Observing Systems (ASOS), this situation can be improved. Two ASOS capability levels are planned. The first is a basic-level system which will automatically observe the weather parameters essential for aviation operations and will operate either with or without supplemental contributions by an observer. The second is a more fully automated, stand-alone system which will observe and report the full range of weather parameters and will operate primarily in the unattended mode. Approximately 250 systems are planned by the end of the decade. When deployed, these systems will generate the standard hourly and special long-line transmitted weather observations, as well as provide continuous weather information direct to airport users. Specific ASOS configurations will vary depending upon whether the operation is unattended, minimally attended, or fully attended. The major functions of ASOS are data collection, data processing, product distribution, and system control. The program phases of development, demonstration, production system acquisition, and operational implementation are described.

Short, Steve E.↗

DOE OSTI2023

Synthesis of Pyrimidine Modified Seleno–DNA as a Novel Approach to Antisense Candidate

Chemically modified antisense oligonucleotides (ASO) currently in pre–clinical and clinical experiments mainly focus on the 2’–position derivatizations to enhance stability and targeting affinity. Considering the possible incompatibility of 2’–modifications with RNase H stimulation and activity, we have hypothesized that the atom specific modifications on nucleobases can retain the complex structure and RNase H activity, while enhancing ASO's binding affinity, specificity, and stability against nucleases. Herein we report a novel strategy to explore our hypothesis by synthesizing the deoxynucleoside phosphoramidite building block with the seleno–modification at 5–position of thymidine, as well as its Se–oligonucleotides. Via X–ray crystal structural study, we found that the Se–modification was located in the major groove of nucleic acid duplex and didn't cause the thermal and structural perturbations. Surprisingly, our nucleobase–modified Se–DNAs were exceptionally resistant to nuclease digestion, while compatible with RNase H activity. In conclusion, this affords a novel avenue for potential anti–sense modification in the form of Se–antisense oligonucleotides (Se–ASO).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Physics-Guided Continual Learning for Predicting Emerging Aqueous Organic Redox Flow Battery Material Performance

Aqueous organic redox flow batteries (AORFBs) have gained popularity in renewable energy storage due to their low cost, environmental friendliness and scalability. The rapid discovery of aqueous soluble organic (ASO) redox-active materials necessitates efficient machine learning surrogates for predicting battery performance. The physics-guided continual learning (PGCL) method proposed in this study can incrementally learn data from new ASO electrolytes while addressing catastrophic forgetting issues in conventional machine learning. Using a AORFB database with a thousand potential materials generated by a 780 $\text{cm}^2$ interdigitated cell model, PGCL incorporates AORFB physics to optimize the continual learning task formation and training strategies to retain previously learned battery material knowledge. Finally, the trained PGCL demonstrates its capability in assessing emerging ASO materials within the established parameter space when evaluated with the dihydroxyphenazine isomers.

25 ENERGY STORAGE↗

DOE OSTI2021

The combined application of the anti- RAS1 and anti- RIM101 2'- O MethylRNA oligomers enhances Candida albicans filamentation control

Abstract Although antisense oligomers (ASOs) have been successfully utilized to control gene expression, they have been little exploited to control Candida albicans virulence's determinants. Filamentation is an important virulence factor of C. albicans, and RAS1 and RIM101 genes are involved in its regulation. Thus, the main goal of this work was to project ASOs, based on 2'-OMethyl chemical modification, to target RAS1 and RIM101 mRNA and to validate its application either alone or in combination, to reduce Candida filamentation in different human body fluids. It was verified that both, anti-RAS1 2'OMe and anti-RIM101 2'OMe oligomers, were able to reduce the levels of RAS1 and RIM101 genes’ expression and to significantly reduce C. albicans filamentation. Furthermore, the combined application of anti-RAS1 2'OMe oligomer and anti-RIM101 2'OMe oligomer enhances the control of C. albicans filamentation in artificial saliva and urine. Our work confirms that ASOs are useful tools for research and therapeutic development on the control of candidiasis.

Barbosa, Ana↗

DOE OSTI2026

Data From: "Warming and snow loss increase reliance on old groundwater in a Colorado River headwater"

This repository contains the data and code associated with the paper titled "Warming and snow loss increase reliance on old groundwater in a Colorado River headwater," published in Nature Geoscience, 2026. This study seeks to answer how various ages of groundwater interact with mountainous streamflow in mountainous headwaters such as the East River. It includes various model-data processing scripts, primarily for ParFlow-CLM analysis of simulated water years 2015-2021, and two numerical warming experiments (+2.5 and +4.0 degrees C), including run scripts, forcing scripts, and post-processing, as well as comparison to observation datasets, detailed below. This data requires the use of R (.r, .rmd), Python (.py), Jupyter Notebook or Jupyter Lab (.ipynb), ParFLOW-CLM, EcoSLIM. Further information on the use of all file formats mentioned below (e.g. .tff. .nc) are provided within the associated scripts and directory where the files are located. Contents & Usage ASO/: Contains the bash and python scripts used to convert airborne snow observatory (ASO) data (ASO, 2023) in various data formats (georeferenced tiff file, NetCDF, UTM, and to latitude/longitude) then regrided to the ParFlow equivalent grid. Output data are in regrid_regll_data.zip and subsequently visualized and analyzed in plot_and_compare.py for Supplementary Figures A14 and A15. The wksht_ASO_comparison.xlsx spreadsheet is used to calculate the data for Supplementary Figure A16. EcoSLIM/: Contains the scripts and input files to run the EcoSLIM particle tracking simulations (/run_scripts) and the post-processing python script (/plot_scripts/eco_agedist_plots.ipynb). Jasechko et al./: Contains the jupyter notebook (Extract_Elevation.ipynb) to determine the outlet elevations of the 260 watersheds used in Jasechko et al. (2016), and the corresponding table, Table_S1_Watersheds_alt.csv. Used to create Supplementary Information Figure A2. PLM_Wells/: Contains the QA/QC-ed groundwater level time series of the PLM-1 and PLM-6 Monitoring Wells from Faybishenko et al. (2023), reformatted to water years used for Supplementary Figures A19 and and A20. ParFlow/: Contains the input files and run scripts to run ParFlow-CLM (/run_scripts), the python and tool command language (Tcl) scripts to create and distribute the ParFlow forcing simulation files (/forcing), and various scripts and intermediary files to analyze the model outputs (/post_process). SQUIRE/: Contains the processing scripts and intermediary files for the Surface QUantitatIve pRecipitation Estimation (SQUIRE) data (Grover, 2023) used to generate Supplementary Figure A18. USGS_Streamflow/: Contains the raw and gap-filled United States Geological Survey streamflow data (U.S. Geological Survey, 2026) used at the Almont station (site number 09112500). Gap-filling is performed in the R script with data from the Taylor station (site number 09110000). (/USGS_09112500_EAST_RIVER_AT_ALMONT_GAP_FILLED/code_almont_streamflow_gap_fill.Rmd). discharge/: Contains the gap-filled discharge data at the Watershed Function SFA East River pumphouse site (Newcomer et al., 2022) used to generate Supplementary Figure A13 and to compute hourly Nash-Sutcliffe model efficiency coefficients (NSE) in Table A4. snotel_and_flux_tower/: Contains the snow telemetry data (U.S. Department of Agriculture, 2024) from the Butte (site ID 380) and Schofield (site ID 737) stations, reformatted by water year, accessed with the snotelr R package. Used to create Supplementary Figure A17. Also contains the flux tower observational data (FluxTower_Pumphouse_ESS-DIVE.ET_only.h.txt) from Ryken et al. (2022) and sap flux transpiration data (MaxB_Transpiration_5Sites.daily_sums.h.txt) from Ryken (2021), used to create Supplementary Figures A22 and A23, respectively. Raw EcoSLIM model outputs are in excess of 24TB, and are stored on National Energy Research Scientific Computing Center (NERSC) and publicly available via the external link provided in the paper.

atmospheric warming↗

NASA NTRS1974

The ASSESS program documenting and evaluating low cost techniques for conducting research in the flight environment

The Airborne Science Office (ASO) of the Ames Research Center has for 10 years operated an airborne scientific research program in infrared astronomy and other disciplines. The Lear Jet, CV-990, and C-141 flying laboratories are flown by ASO crews, while the major responsibility for defining, developing, and operating the experimental equipment is placed on individual researchers, who have included scientists from many countries. The ASSESS (Airborne Science/Shuttle Experiment Systems Simulation) program consists of two phases: Phase A documents the present management and operational practices of the ASO, and Phase B consists of airborne research missions constrained (for example, by crew confinement) to simulate certain aspects of experimental operations on Shuttle/Spacelab missions. Various parallels between the Airborne Science Program and Spacelab are pointed out and their applications to Spacelab planning are discussed.

Weaver, L. B.↗

NASA NTRS2019

Artificial Intelligence: Powering Human Exploration of the Moon and Mars

Artificial Intelligence (AI) is a growing field of computa- tional science techniques designed to mimic functions per- formed by people. Advancements in autonomy will depend on a portfolio of AI technologies. Automated planning and scheduling is a venerable field of study in AI, and is needed for a variety of mission planning functions. Plan execution technology is less well studied, but important for auton- omy and robotics. Specialized forms of automated reason- ing and machine learning are key technologies to enable fault management. Over the past decade, the NASA Au- tonomous Systems and Operations (ASO) project has devel- oped and demonstrated numerous autonomy enabling tech- nologies employing AI techniques. Our work has employed AI in three distinct ways to enable autonomous mission op- erations capabilities. Crew Autonomy gives astronauts tools to assist in the performance of each of these mission oper-ations functions. Vehicle System Management uses AI tech- niques to turn the astronaut's spacecraft into a robot, allow- ing it to operate when astronauts are not present, or to reduce astronaut workload. AI technology also enables Autonomous Robots as crew assistants or proxies when the crew are not present. When these capabilities are used to enable astro- nauts to operate autonomously, they must be integrated with user interfaces, introducing numerous human factors con- siderations; when these capabilities are used to enable vehi- cle system management, they must be integrated with flight software, and run on embedded processors under the control of real-time operating systems.We first describe human spaceflight mission operations capabilities. The remainder of the paper will describe the ASO project, and the development and demonstration per- formed by ASO since 2011. We will describe the AI tech- niques behind each of these demonstrations, which include a variety of symbolic automated reasoning and machine learn- ing based approaches. Finally, we conclude with an assess- ment of future development needs for AI to enable NASA's future Exploration missions.

Mission Operations↗

DOE OSTI2021

Removal of As(III) by Electrically Conducting Ultrafiltration Membranes

As(III) species are the predominant form of arsenic found in groundwater. However, nanofiltration (NF) and reverse osmosis (RO) membranes are often unable to effectively reject As(III). In this study, we fabricate highly conducting ultrafiltration (UF) membranes for effective As(III) rejection. Here, these membranes consist of a hydrophilic nickel-carbon nanotubes layer deposited on a UF support, and used as cathodes. Applying cathodic potentials significantly increased As(III) rejection in synthetic/real tap water, a result of locally elevated pH that is brought upon through water electrolysis at the membrane/water interface. The elevated pH conditions convert H 3 ASO 3 to H 2 AsO 3 – /HAsO 3 2– that are rejected by the negatively charged membranes. In addition, it was found that Mg(OH) 2 that precipitates on the membrane can further trap arsenic. Importantly, almost all As(III) passing through the membranes is oxidized to As(V) by hydrogen peroxide produced on the cathode, which significantly decreased its overall toxicity and mobility. Although the high pH along the membrane surface led to mineral scaling, this scale could be partially removed by backwashing the membrane. To the best of our knowledge, this is the first report of effective As(III) removal using low-pressure membranes, with As(III) rejection higher than that achieved by NF and RO, and high water permeance.

42 ENGINEERING↗

DOE OSTI2022

Oxyanion Surface Complexes Control the Kinetics and Pathway of Ferrihydrite Transformation to Goethite and Hematite

The rate and pathway of ferrihydrite (Fh) transformation at oxic conditions to more stable products is controlled largely by temperature, pH, and the presence of other ions in the system such as nitrate (NO 3 - ), sulfate (SO 4 2- ), and arsenate (AsO 4 3- ). Although the mechanism of Fh transformation and oxyanion complexation have been separately studied, the effect of surface complex type and strength on the rate and pathway remains only partly understood. We have developed a kinetic model that describes the effects of surface complex type and strength on Fh transformation to goethite (Gt) and hematite (Hm). Two sets of oxyanion-adsorbed Fh samples were prepared, nonbuffered and buffered, aged at 70 ± 1.5 °C, and then characterized using synchrotron X-ray scattering methods and wet chemical analysis. Kinetic modeling showed a significant decrease in the rate of Fh transformation for oxyanion surface complexes dominated by strong inner-sphere (SO 4 2- and AsO 4 3- ) versus weak outer-sphere (NO 3 - ) bonding and the control. The results also showed that the Fh transformation pathway is influenced by the type of surface complex such that with increasing strength of bonding, a smaller fraction of Gt forms compared with Hm. In conclusion, these findings are important for understanding and predicting the role of Fh in controlling the transport and fate of metal and metalloid oxyanions in natural and applied systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

The structures of mcgovernite and carlfrancisite reconsidered: static disorder or ordered defects and twinning?

Here, the minerals mcgovernite and carlfrancisite possess an extraordinary rhombohedral unit cell [mcgovernite: a = b = 8.206 Å and c = 204.118 Å, with the complex formula unit M 2+ 19 Zn 3 (OH) 21 (AsO 3 )(AsO 4 ) 3 (SiO 4 ) 3 (M = Mn, Mg, Zn); carlfrancisite similar]. Hawthorne [(2018), Mineral. Mag. 82, 1101–1118] reported a single-crystal study using a centrosymmetric model ($R\bar{3}c$) containing disorder on three cationic sites and an AsIII lone-pair site for both mcgovernite and carlfrancisite. A solution of the structure of mcgovernite from high-resolution synchrotron powder diffraction data suggests a reinterpretation of the crystal structures of mcgovernite and carlfrancisite as noncentrosymmetric R3c with ordered planar defects which allows for merohedral twinning.

carlfrancisite↗

DOE OSTI2020

Hydrologic Model Files (PRMS) for Historical Conditions in the East River Watershed, Colorado between 1987-2019

The data package contains model input and output file for the East River watershed (85 km2) located in the headwaters of the Colorado River. The model is the U.S. Geological Survey (USGS) Precipitation-Modeling Runoff System (PRMS) for the historical, or baseline, condition (1987-2019) to assess the efficiency of summer monsoon rains to generate streamflow. The finite difference grid resolution is 100 m with elevations resampled from the USGS National Elevation Dataset. LANDFIRE raster data is used to derive parameters of dominant cover type, summer and winter cover density, canopy interception characteristics for snow and rain and transmission coefficient for shortwave radiation. Climate forcing uses minimum and maximum daily temperature lapse rates defined by the two Snow Telemetry (SNOTEL) stations located in the vicinity of the East River adjusted for aspect. Schofield SNOTEL snowfall is spatially distributed using LiDAR derived snow depth observations from the Airborne Snow Observatory (ASO) flown 4 April 2016. Snow depths are converted to snow water equivalent based on ground surveys and density modeling. Rainfall is spatially distributed using the monthly Parameter-elevation Relationships on Independent Slopes Model 30-year monthly averages. Simulated solar radiation is calibrated to match weather station observations. Model verification of snow water equivalent (SWE) accumulation and ablation relies on the 2018 and 2019 ASO maps and weather station snow depth. The data package contains model input files in text format, model documentation in pfd and a readme.txt file to help users understand the file structure to run the program.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Materials Data on LaAsRuO by Materials Project

LaRuAsO crystallizes in the tetragonal P4/nmm space group. The structure is two-dimensional and consists of two lanthanum molecules; two ruthenium molecules; and one AsO sheet oriented in the (0, 0, 1) direction. In the AsO sheet, As3- is bonded in a distorted square co-planar geometry to four equivalent O2- atoms. All As–O bond lengths are 2.15 Å. O2- is bonded in a square co-planar geometry to four equivalent As3- atoms.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Cloud-Control of Legacy Building Automation System: A case study

As Internet of Things devices and cloud-based platforms become more mature, Energy Management and Information Systems (EMIS) are increasingly gaining momentum in the building industry. In large commercial buildings, Fault-Detection and Diagnostic (FDD) and energy information systems (EIS) are now established technologies with tens of providers and thousands of deployment sites across North America. The new frontier for the EMIS technology is now represented by control systems that use advanced system optimization (ASO) methods to improve the operations of the HVAC system. Given the complexity of the integration of such systems with the existing building automation systems (BAS) and the higher risk involved with direct control of the HVAC, these systems are still emerging in the market. This paper presents the results of a project in which a start-up company partnered with a research institution to develop a cloud-based software EMIS solution and deployed it in a university campus in California. The software system included advanced sensing, data acquisition, storage and advanced control and analytics applications developed on top of the native BAS. The new platform controls ten buildings on the campus and the FDD and the ASO applications deployed on this platform were able to generate energy savings of up to 35% and 25% in certain buildings for each functionality respectively. Where the platform did not save energy, it improved building service (air quality). Lessons learned include the importance of collaborating with and training the building operators and evaluating whether the legacy system can work reliably with the new technology.

Prakash, Anand Krishnan↗

DOE OSTI2022

Field Validation of a Building Operating System Platform

The U.S General Services Administration's (GSA's) Green Proving Ground program, in partnership with the National Renewable Energy Laboratory. completed a large pilot study of an Energy Management Information Systems (EMIS) with Automated System Optimization (ASO). Four test bed facilities, each with different building characteristics and systems, were chosen for the implementation of cloud-based EMIS with ASO. Depending on functionality, this tool can be extremely effective in energy management and energy optimization in buildings. The capabilities evaluated in the pilot ranged from energy savings and energy consumption predictions to evaluations of user acceptance, operability, and ease of installation. This report presents the methodology, lessons learned and best practices, and deployment recommendations for the GSA's portfolio of commercial office space, comprising more than 8,500 properties.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Platform for Remote Deployment and Training for Enhanced Building Operation Practices (Building Re-Tuning and On-going Commissioning)

While a building’s energy usage is driven largely by its design and use, building operator behavior has a strong influence on its energy consumption. This project developed and piloted a specific, data-driven coaching methodology to help operators understand how they can adjust operations and/or affect no/low-cost repairs or upgrades to their specific building HVAC systems to reduce energy consumption. Named BuildingCoach, the operational optimization method used is based on the Building Re-tuning approach developed by the Pacific Northwest National Laboratory. A building operations analytics market has matured over the past decade, though its potential to affect energy-saving changes has not been fully realized. Training operators to understand the methods for operational optimization with the explicit approach of using building-system performance data is hypothesized to create a more effective, longer lasting result in building energy efficiency, and this strategy is the fundamental premise of this project. With the support of an Industry Advisory Board, the project succeeded in developing materials and recruiting for and delivering three pilot cohorts. Deliverables included twenty-two self-paced training modules (accessed via a Learning Management System) and a web-based platform that includes access to real-time building system data and a repository for building system documentation. The project set out to have 100 participants from 50 buildings in three pilot cohorts. In the end, there were 28 participants from 17 buildings, i.e., a significant shortfall. The first two pilot cohorts had only two buildings in each, and this was partially due to difficulties in deploying the Building Operator Coaching Solution (“the BOCS”), which is technology that extracts the data from the controls network and presents it as prescribed for coaching. In the third cohort, the project team deployed the BOCS successfully to 13 buildings, the methodology was piloted as intended, and numerous opportunities for optimization were identified. The BuildingCoach business plan charts a path to an economically sustainable effort. However, even with a licensing model captured in the final version of the business plan, the scalability is still limited to keeping less than 1,000 buildings affected by 2033. Even so, there are unexplored paths to greater scalability that are being considered. CUNY BPL is working to perpetuate and grow the use of BuildingCoach. As of this writing, about twenty buildings have either been connected or will be connected with operators coached / to be coached in the NYC municipal portfolio, twelve buildings across four campuses in NY State will use BuildingCoach, a NY upstate county wishes for six or seven buildings to participate with the support of funding from NYSERDA, and others have also expressed interest. In the decades to come, there will be an increasing percentage of large and mid-sized buildings that incorporate automated system optimization (ASO), and the building operators’ role will shift to spend more time on maintenance and monitoring. Meanwhile, programs such as BuildingCoach will play a critical role in optimizing operations. And, regardless of the emergence of ASO, operators will still need to understand how their systems operate so that they can monitor them properly. Within that context, BuildingCoach is an important step towards operators’ understanding of efficient building system operations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1984

Future scientific missions for STS

The present investigation is concerned with a number of astrophysics missions which are currently being studied by NASA. The missions will be initially flown on the Space Transportation System (STS) and subsequently could be conducted in connection with the utilization of a Space Station on Space Platform. One of the missions involves the use of a large antenna system designed to significantly extend the aperture synthesis technique of earth-based very long baseline interferometry technique (orbiting VLBI). It has been recommended that a space VLBI antenna be launched in low earth orbit during this decade. Another mission considered is related to the Advanced Solar Observatory (ASO). ASO is a long duration space observatory. It will consist of four major instrument groupings including a High Resolution Solar Telescope Cluster (HRSTC), a Pinhole Occulter Facility, a Solar High Energy Facility, and a Solar Low Frequency Radio Facility. Attention is also given to a new program entitled Capabilities for Opportunity Payload Experiments (COPE).

De Sanctis, C. E.↗

NASA NTRS1985

Anticipated scientific return of the Advanced Solar Observatory

The scientific importance of the Advanced Solar Observatory (ASO) is discussed with emphasis on its soft X-ray, XUV, and EUV facilities. The principal achievement expected from the ASO's SXR/XUV and EUV telescope is a greatly improved resolution of the magnetic structure and activity in the transition region and corona. Observations from these facilities, combined with complementary observations of the photosphere and chromosphere from Solar Optical Telescope and of the higher corona from the Pinhole/Occulter Facility, are expectecd to yield significant advances in all major areas of solar physics concerning the causes and effects of solar magnetic fields.

Moore, Ron↗

NASA NTRS1990

Science and application payloads in the 90's

An overview is conducted of the payloads under development for NASA to support space science and the Mission to Planet Earth. The science payloads reviewed include the Controls, Astrophysics, and Structures Experiment in Space (CASES), the Advanced Solar Observatory (ASO), the Laser Atmospheric Wind Sounder (LAWS), and the Lightning Imaging Sensor. The payloads are supported by the Extended Duration Orbiter, the Space Station Freedom, and an infrastructure that includes platforms in polar and geosynchronous orbits. The LAWS payload can provide data for global wind-profile measurements, and the ASO can coordinate solar-physics observations and study solar dynamic processes. The CASES payload is designed to investigate critical control technology for stabilizing and pointing large flexible structures in space for astrophysics missions.

De Sanctis, Carmine E.↗