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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Earth and Space Science Informatics Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

Abstract This article is composed of three independent commentaries about the state of Integrated, Coordinated, Open, Networked (ICON) principles (Goldman, et al., 2021b, https://doi.org/10.1029/2021EO153180 ) in Earth and Space Science Informatics (ESSI) and includes discussion on the opportunities and challenges of adopting them. Each commentary focuses on a different topic: (Section 2) Global collaboration, cyberinfrastructure, and data sharing; (Section 3) Machine learning for multiscale modeling; (Section 4) Aerial and satellite remote sensing for advancing Earth system model development by integrating field and ancillary data. ESSI addresses data management practices, computation and analysis, and hardware and software infrastructure. Our role in ICON science therefore involves collaborative work to assess, design, implement, and promote practices and tools that enable effective data management, discovery, integration, and reuse for interdisciplinary work in Earth and space science disciplines. Networks of diverse people with expertise across Earth, space, and data science disciplines are essential for efficient and ethical exchanges of findable, accessible, interoperable, and reusable (FAIR) research products and practices. Our challenge is then to coordinate the development of standards, curation practices, and tools that enable integrating and reusing multiple data types, software, multi‐scale models, and machine learning approaches across disciplines in a way that is as open and/or FAIR as ethically possible. This is a major endeavor that could greatly increase the pace and potential of interdisciplinary scientific discovery.

58 GEOSCIENCES↗
DOE OSTI2022

Hydrological Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

Hydrologic sciences depend on data monitoring, analyses, and simulations of hydrologic processes to ensure safe, sufficient, and equal water distribution. These hydrologic data come from but are not limited to primary (lab, plot, and field experiments) and secondary sources (remote sensing, UAVs, hydrologic models) that typically follow FAIR Principles (Findable, Accessible, Interoperable, and Reusable: (go-fair.org)). Easy availability of FAIR data has become possible because the hydrology-oriented organizations have pushed the community to increase coordination of the protocols for generating data and sharing model platforms. In addition, networking at all levels has emerged with an invigorated effort to activate community science efforts that complement conventional data collection methods. However, it has become difficult to decipher various complex hydrologic processes with increasing data. Machine learning, a branch of artificial intelligence, provide more accurate and faster alternatives to better understand different hydrological processes. The Integrated, Coordinated, Open, Networked (ICON) framework provides a pathway for water users to include and respect diversity, equity, and inclusivity. In addition, ICONs support the integration of peoples with historically marginalized identities into this professional discipline of water sciences. This article comprises three independent commentaries about the state of ICON principles in hydrology and discusses the opportunities and challenges of adopting them.

(ICON) principles to address↗
DOE OSTI2026

Crystallization-assisted water adsorption in amorphous molecular adsorbents

Efficient and stable water adsorbents are essential for removing moisture from natural gas and olefins during their production. Industrial desiccants such as alumina and zeolites require high regeneration temperatures, while metal-organic frameworks often suffer from limited long-term stability and reusability. Here, we introduce a new type of molecular desiccants (M-PyC) that, in principle, can be reused indefinitely. These simple molecular coordination complexes undergo fully reversible phase transitions between crystalline and amorphous states through the decoordination (bond breaking) and recoordination (bond reforming) of water molecules. They exhibit high water uptake (30 wt%) and superb selectivity, excluding hydrocarbons entirely. Their effectiveness for dehydration, combined with low regeneration temperature, fast desorption kinetics, low-cost and green synthesis, easy scalability to kilogram quantities, and essentially unlimited recyclability, makes them truly competitive dehydration agents for industrial separations. The use of amorphous molecular adsorbents, where crystallinity and porosity are no longer stringent requirements, and the adsorption-desorption process entirely under ambient air, offers a conceptually different approach to adsorption-based separation science and potentially realistic solution to the long-standing challenge of sustained stability in coordinate-bonded adsorbents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Common workflows for computing material properties using different quantum engines

The prediction of material properties based on density-functional theory has become routinely common, thanks, in part, to the steady increase in the number and robustness of available simulation packages. This plurality of codes and methods is both a boon and a burden. While providing great opportunities for cross-verification, these packages adopt different methods, algorithms, and paradigms, making it challenging to choose, master, and efficiently use them. We demonstrate how developing common interfaces for workflows that automatically compute material properties greatly simplifies interoperability and cross-verification. We introduce design rules for reusable, code-agnostic, workflow interfaces to compute well-defined material properties, which we implement for eleven quantum engines and use to compute various material properties. Each implementation encodes carefully selected simulation parameters and workflow logic, making the implementer’s expertise of the quantum engine directly available to non-experts. All workflows are made available as open-source and full reproducibility of the workflows is guaranteed through the use of the AiiDA infrastructure.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Hierarchical transfer learning: an agile and equitable strategy for machine-learning interatomic models

Machine-learned interatomic models are growing in popularity due to their ability to afford near quantum-accurate predictions for complex phenomena with orders-of-magnitude greater computational efficiency. However, these models struggle when applied to systems of many element types due to the approximately exponential increase in number of parameters that must be determined. To mitigate this challenge, we present a new hierarchical transfer learning approach that allows the fitting problem to be decomposed into smaller independent and reusable parameter blocks that enable development of explicitly chemically extensible ML-IAM. Application of this strategy is demonstrated for C and N mixtures under conditions ranging from nominally ambient to ~10,000 K and 200 GPa for compositions from 0 to 100% N. Ultimately, this strategy makes model generation for chemically complex systems more tractable and efficient, facilitates comprehensive model validation, and makes ML-IAM development for problems of this nature more accessible to users with limited access to extreme computing infrastructure.

Lindsey, Rebecca K. [Univ. of Michigan, Ann Arbor,↗
DOE OSTI2021

Accelerated, scalable and reproducible AI-driven gravitational wave detection

The development of reusable artificial intelligence (AI) models for wider use and rigorous validation by the community promises to unlock new opportunities in multi-messenger astrophysics. Here we develop a workflow that connects the Data and Learning Hub for Science, a repository for publishing AI models, with the Hardware-Accelerated Learning (HAL) cluster, using funcX as a universal distributed computing service. Using this workflow, an ensemble of four openly available AI models can be run on HAL to process an entire month's worth (August 2017) of advanced Laser Interferometer Gravitational-Wave Observatory data in just seven minutes, identifying all four binary black hole mergers previously identified in this dataset and reporting no misclassifications. This approach combines advances in AI, distributed computing and scientific data infrastructure to open new pathways to conduct reproducible, accelerated, data-driven discovery. By combining a repository for artificial intelligence models and a supercomputing cluster, an entire month's worth of advanced LIGO data is analysed in just 7 min, finding all binary black hole mergers previously identified in this dataset and reporting no misclassifications.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

COVID-19 pandemic reveals the peril of ignoring metadata standards

Efficient response to the pandemic through the mobilization of the larger scientific community is challenged by the limited reusability of the available primary genomic data. Here, the Genomic Standards Consortium board highlights the essential need for contextual genomic data FAIRness, for empowering key data-driven biological questions.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy

Abstract A concise and measurable set of FAIR (Findable, Accessible, Interoperable and Reusable) principles for scientific data is transforming the state-of-practice for data management and stewardship, supporting and enabling discovery and innovation. Learning from this initiative, and acknowledging the impact of artificial intelligence (AI) in the practice of science and engineering, we introduce a set of practical, concise, and measurable FAIR principles for AI models. We showcase how to create and share FAIR data and AI models within a unified computational framework combining the following elements: the Advanced Photon Source at Argonne National Laboratory, the Materials Data Facility, the Data and Learning Hub for Science, and funcX, and the Argonne Leadership Computing Facility (ALCF), in particular the ThetaGPU supercomputer and the SambaNova DataScale ® system at the ALCF AI Testbed. We describe how this domain-agnostic computational framework may be harnessed to enable autonomous AI-driven discovery.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

A materials data framework and dataset for elastomeric foam impact mitigating materials

The availability of materials data for impact-mitigating materials has lagged behind applications-based data. For example, data describing on-field helmeted impacts are available, whereas material behaviors for the constituent impact-mitigating materials used in helmet designs lack open datasets. Here, we describe a new FAIR (findable, accessible, interoperable, reusable) data framework with structural and mechanical response data for one example elastic impact protection foam. The continuum-scale behavior of foams emerges from the interplay of polymer properties, internal gas, and geometric structure. This behavior is rate and temperature sensitive, therefore, describing structure-property characteristics requires data collected across several types of instruments. Data included are from structure imaging via micro-computed tomography, finite deformation mechanical measurements from universal test systems with full-field displacement and strain, and visco-thermo-elastic properties from dynamic mechanical analysis. These data facilitate modeling and design efforts in foam mechanics, e.g., homogenization, direct numerical simulation, or phenomenological fitting. The data framework is implemented using data services and software from the Materials Data Facility of the Center for Hierarchical Materials Design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

An open source knowledge graph ecosystem for the life sciences

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoint resources and abstraction algorithms), and benchmarks (e.g., prebuilt KGs). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 different large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Applying the FAIR Principles to computational workflows

Recent trends within computational and data sciences show an increasing recognition and adoption of computational workflows as tools for productivity and reproducibility that also democratize access to platforms and processing know-how. As digital objects to be shared, discovered, and reused, computational workflows benefit from the FAIR principles, which stand for Findable, Accessible, Interoperable, and Reusable. The Workflows Community Initiative’s FAIR Workflows Working Group (WCI-FW), a global and open community of researchers and developers working with computational workflows across disciplines and domains, has systematically addressed the application of both FAIR data and software principles to computational workflows. We present recommendations with commentary that reflects our discussions and justifies our choices and adaptations. These are offered to workflow users and authors, workflow management system developers, and providers of workflow services as guidelines for adoption and fodder for discussion. The FAIR recommendations for workflows that we propose in this paper will maximize their value as research assets and facilitate their adoption by the wider community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

CAMELSH: A Large-Sample Hourly Hydrometeorological Dataset and Attributes at Watershed-Scale for CONUS

We present CAMELSH (Catchment Attributes and Hourly HydroMeteorology for Large-Sample Studies), the first large-sample hydrometeorological dataset at the hourly scale for the contiguous United States. CAMELSH intergrates hourly meteorological time series, catchment attributes and boundaries from GAGES-II and HydroATLAS for 9,008 catchments across diverse climatic, hydrological, and anthropogenic conditions. In addition, hourly streamflow time series is provided for 3,166 catchments. The dataset spans 45 years (1980–2024) with 11 meteorological variables from the NLDAS-2 forcing dataset, from which we compute nine climate indices related to precipitation, evapotranspiration, seasonality, and snow fraction. Additionally, CAMELSH includes two sets of catchment attributes: 439 from GAGES-II and 195 derived from HydroATLAS. These attributes include factors related to climate, geology, hydrology, river/stream morphology, landscape, nutrient, soil, topography, and anthropogenic influences. Developed in accordance with FAIR (Findability, Accessibility, Interoperability, and Reusability) principles, CAMELSH is the first large-sample dataset at an hourly timescale, supporting machine learning applications for short-term streamflow (flood) prediction and advancing data-driven hydrological research across multiple timescales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A quantum encryption design featuring confusion, diffusion, and mode of operation

Abstract Quantum cryptography—the application of quantum information processing and quantum computing techniques to cryptography has been extensively investigated. Two major directions of quantum cryptography are quantum key distribution (QKD) and quantum encryption, with the former focusing on secure key distribution and the latter focusing on encryption using quantum algorithms. In contrast to the success of the QKD, the development of quantum encryption algorithms is limited to designs of mostly one-time pads (OTP) that are unsuitable for most communication needs. In this work we propose a non-OTP quantum encryption design utilizing a quantum state creation process to encrypt messages. As essentially a non-OTP quantum block cipher the method stands out against existing methods with the following features: 1. complex key-ciphertext relation (i.e. confusion) and complex plaintext-ciphertext relation (i.e. diffusion); 2. mode of operation design for practical encryption on multiple blocks. These features provide key reusability and protection against eavesdropping and standard cryptanalytic attacks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Cu and Ni Co-sputtered heteroatomic thin film for enhanced nonenzymatic glucose detection

Abstract In this work, we report a wafer-scale and chemical-free fabrication of nickel (Ni) and copper (Cu) heteroatomic Cu–Ni thin films using RF magnetron sputtering technique for non-enzymatic glucose sensing application. The as-prepared wafer-scale Cu–Ni thin films exhibits excellent electrocatalytic activity toward glucose oxidation with a 1.86 μM detection limit in the range of 0.01 mM to 20 mM range. The Cu–Ni film shows 1.3- and 5.4-times higher glucose oxidation activity in comparison to the Cu and Ni electrodes, respectively. The improved electrocatalytic activity is attributed to the synergistic effect of the bimetallic catalyst and high density of grain boundaries. The Cu–Ni electrodes also possessed excellent anti-interference characteristics. These results indicate that Cu–Ni heteroatomic thin film can be a potential candidate for the development of non-enzymatic glucose biosensor because of its chemical free synthesis, excellent reproducibility, reusability, and long-term stability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Extraction, purification, and reuse of dyes from coloured polyester textiles

The removal of dyes from coloured textile waste represents a sustainable approach to textile recycling, enabling the recovery of valuable chemical, and material resources that would otherwise be discarded. Up to 40% of the greenhouse gas emissions from textiles originate from dye production, making efficient recycling of dyes a major opportunity for curbing emissions and minimizing waste in both textile manufacturing and recycling. Here, in this study, we demonstrate a process for the extraction, purification, and reuse of mixed dyes from polyester textiles using bio-based, non-hazardous solvents selected on the basis of computational predictions for polyester and dye solubilities. Extracted dyes are purified to individual compounds using counter-current chromatography and analysed via liquid chromatography-mass spectrometry. Post-extraction characterization of the extracted dyes and polymer substrate confirms dye colour retention and polyester fabric property preservation. Dye recycling is demonstrated by redyeing colour-free fabrics with the recovered dyes. We further show a potential process configuration for dye removal using a flow-through reactor packed with a textile substrate. The proposed dye removal process produces reusable, recyclable dyes, and dye-free fabrics, thus facilitating textile recycling.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A novel, mesoporous molybdenum doped titanium dioxide/reduced graphene oxide composite as a green, highly efficient solid acid catalyst for acetalization

A novel, mesoporous composite of Mo doped TiO 2 /reduced graphene oxide is synthesized to be used as a highly efficient heterogeneous acid catalyst. The composite has a high surface area (263 m 2 g -1 ) and a monomodal pore size distribution with an average pore diameter of 3.4 nm. A comprehensive characterization of the synthesized material was done using PXRD, Raman, BET, SEM, EDX, TEM, TGA, and XPS. The composite exhibited excellent catalytic activity (1.6 h -1 TOF, >99% GC yield, and >99% selectivity) towards acetalization of cyclohexanone at room tempertaure within 30 minutes. The catalyst was reusable up to 4 reaction cycles without any significant loss in the activity and the acidic site calculations showed that the reaction is mostly driven by the weak acidic sites on the composite.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Heterogenization of polyoxometalates as solid catalysts in aerobic oxidation of glycerol

A series of heterogeneous catalysts based on phosphomolybdic salts with different metals in counter or substituent positions, L n PMo 12 O 40 (L = Al 3+ , Fe 3+ , Cr 3+ , Ti 4+ , Zr 4+ and Zn 2+ , abbreviated as LPMo 12 ) and H x PMo 11 LO 39 (L = Zn 2+ , Cr 3+ , Fe 3+ , Al 3+ , Ti 4+ , for Ti 4+ ; the amount of O is 40, abbreviated as HPMo 11 L), were prepared using a simple calcination treatment and were evaluated in aerobic oxidation of glycerol. After calcination at about 250 °C for 4 h, homogeneous Ln P Mo 12 O 40 ·nH 2 O and H x PMo 11 LO 39 ·mH 2 O became heterogeneous. Their specific surface areas were also enhanced as well as their activity and reusability. Similar to the homogeneous catalysts, AlPMo 12 treated at 400 °C (AlPMo 12 -400) was found to be the most active catalyst in glycerol oxidation to lactic acid, with 96.1% yield and 98.6% conversion at 60 °C for 5 h with 1 MPa of O 2 ; it gave a turnover number (TON; TON = [LA]/[catalyst]) of 2.4 × 10 2 , higher than that of AlPMo 12 -250 treated at 250 °C (TON = 2.0 × 10 2 ). AlPMo 12 behaved as a heterogeneous catalyst in glycerol oxidation and could be reused at least 12 times. AlPMo 12 -400 also performed well in crude glycerol oxidation, affording lactic acid yields as high as 86.8%.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗