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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Superior removal of As(III) and As(V) from water with Mn-doped β-FeOOH nanospindles on carbon foam

Arsenic pollution of water is one of the severest environmental challenges threatening human health. Iron-based nanomaterials have been demonstrated effective in arsenic removal. However, they generally suffer from low removal efficiency towards highly toxic As(III), loss of active sites owing to agglomeration, and poor reusability. Herein, we report a carbonized melamine foam supported Mn(IV)-doped β-FeOOH nanospindles(CF@Mn-FeOOH NSp) for tackling the technical hurdles. The designed CF@Mn-FeOOH NSp appears as a free-standing monolith through a low-cost and straightforward hydrothermal method. The atomic-scale integration of Mn(IV) into β-FeOOH enables an oxidation-adsorption bifunctionality, where Mn(IV) serves as oxidizer for As(III) and Fe(III) acts as adsorber for As(V). The maximal adsorption capacity for As(V) and As(III) can reach 152 and 107 mg g -1 , respectively. Meanwhile, As in simulated high arsenic groundwater can be decreased to below 10 μg L -1 within 24 h. By simple "filtrating-washing", 85% and 82% of its initial adsorption capacity for As(V) and As(III) can be easily recovered even after 5-cycles reuse. Kinetics and isotherm adsorption study indicate that the arsenic adsorption behavior is mainly through chemical bonding during single-layer adsorbing process. The as-prepared CF@Mn-FeOOH offers a scalable, efficient, and recyclable solution for arsenic removal in groundwater and wastewater from mines and industry.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2022

PDBx/mmCIF Ecosystem: Foundational Semantic Tools for Structural Biology

PDBx/mmCIF, Protein Data Bank Exchange (PDBx) macromolecular Crystallographic Information Framework (mmCIF), has become the data standard for structural biology. With its early roots in the domain of small-molecule crystallography, PDBx/mmCIF provides an extensible data representation that is used for deposition, archiving, remediation, and public dissemination of experimentally determined three-dimensional (3D) structures of biological macromolecules by the Worldwide Protein Data Bank (wwPDB, wwpdb.org). Extensions of PDBx/mmCIF are similarly used for computed structure models by ModelArchive (modelarchive.org), integrative/hybrid structures by PDB-Dev (pdb-dev.wwpdb.org), small angle scattering data by Small Angle Scattering Biological Data Bank SASBDB (sasbdb.org), and for models computed generated with the AlphaFold 2.0 deep learning software suite (alphafold.ebi.ac.uk). Community-driven development of PDBx/mmCIF spans three decades, involving contributions from researchers, software and methods developers in structural sciences, data repository providers, scientific publishers, and professional societies. Having a semantically rich and extensible data framework for representing a wide range of structural biology experimental and computational results, combined with expertly curated 3D biostructure data sets in public repositories, accelerates the pace of scientific discovery. Herein, we describe the architecture of the PDBx/mmCIF data standard, tools used to maintain representations of the data standard, governance, and processes by which data content standards are extended, plus community tools/software libraries available for processing and checking the integrity of PDBx/mmCIF data. Use cases exemplify how the members of the Worldwide Protein Data Bank have used PDBx/mmCIF as the foundation for its pipeline for delivering Findable, Accessible, Interoperable, and Reusable (FAIR) data to many millions of users worldwide.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

PDB-IHM: A System for Deposition, Curation, Validation, and Dissemination of Integrative Structures

Structures of many large biomolecular assemblies are now being determined using integrative approaches. In these approaches, information derived from multiple experimental and computational methods is combined to compute three-dimensional structures of multi-protein complexes and other macromolecular machines. A standalone prototype data resource for integrative structures called PDB-Dev was built, based on recommendations of the Integrative and Hybrid Methods (IHM) Task Force of the Worldwide Protein Data Bank (wwPDB). This effort included developing data standards and software tools for collecting, curating, validating, visualizing, archiving, and disseminating integrative structures that span diverse spatiotemporal scales and conformational states. Mechanisms have been created to validate integrative structures based on the experimental data underpinning them. Building upon this foundational framework, PDB-Dev has been further expanded to handle large dynamic macromolecular systems and integrative structures that combine, for example, experimental restraints with atomic coordinates computed by machine learning algorithms. Data standards and supporting tools have also been extended to capture information about biomolecular dynamics, such as conformational transitions and related kinetic data derived from biophysical methods. Recently, PDB-Dev was unified with the PDB archive and rebranded as PDB-IHM (pdb-ihm.org), further promoting FAIR (Findable, Accessible, Interoperable, and Reusable) principles of data stewardship for integrative structural biology.

IHMCIF↗
DOE OSTI2023

Experiential findings for sustainable software ecosystems to support experimental and observational science

In the search for a sustainable approach for software ecosystems that supports experimental and observational science (EOS) across Oak Ridge National Laboratory (ORNL), we conducted a survey to understand the current and future landscape of EOS software and data. This paper describes the survey design we used to identify significant areas of interest, gaps, and potential opportunities, followed by a discussion on the obtained responses. The survey formulates questions about project demographics, technical approach, and skills required for the present and the next five years. Here, the study was conducted among 38 ORNL participants between June and July of 2021 and followed the required guidelines for human subjects training. We plan to use the collected information to help guide a vision for sustainable, community-based, and reusable scientific software ecosystems that need to adapt effectively to: (i) the evolving landscape of heterogeneous hardware in the next generation of instruments and computing (e.g. edge, distributed, accelerators), and (ii) data management requirements for data-driven science using artificial intelligence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Universal Workflow Language and Software Enable Geometric Learning and FAIR Scientific Protocol Reporting

Written language and conventional data structures for representing scientific procedures suffer from low process detail, often fail to accurately represent protocols, and lack universality. New strategies for the handling of experimental data are needed to provide viable process information for both humans and machines. In this work, we present the universal workflow language (UWL) and interface (UWLi). UWL is a findable, accessible, interoperable, and reusable (FAIR)-compatible, graph-based data architecture that can capture arbitrary scientific procedures through workflow representation, and UWLi is an accompanying software package for building, manipulating, and interpreting UWL entries. The UWL format was found to be highly effective in identifying deficiencies in the reported process details of high-impact, peer-reviewed scientific journals, and in simulated scenarios, the graph format was shown to be more effective than conventional methods in predictively modeling the outcome of diverse scientific protocols. Implementation of UWL could enable more accurate scientific communication and more impactful process datasets.

14 SOLAR ENERGY↗
DOE OSTI2022

Electrochemically decorated gold nanoparticles on CVD graphene ChemFET sensor for the highly sensitive detection of As(III)

Here, in this paper, we report a novel reusable receptor-free chemically sensitive field-effect transistor (ChemFET) sensor for the sensitive detection of As(III) using electrochemical doping of Au nanoparticles (AuNPs)-chemical vapour deposition (CVD) graphene (Gr) sensing film (AuNPs-Gr). The sensor consists of a single-gap gold electrode and a single layer of CVD graphene as a conducting/sensing channel decorated with a thin layer of electrochemically deposited AuNPs to catalyze the electroreduction of As(III) ions onto the sensing channel. A new detection strategy was proposed by measuring the resistance change rate of the AuNPs-Gr sensing channel modified between the source electrode and drain electrode, wherein the resistance change was caused by the electrochemical doping of As(0). The results indicated that the electrochemical doping of As(0) onto the sensing channel led to an increase in resistance through the doping interaction among As(0), AuNPs and graphene, enabling the proposed sensor to have an extraordinary detection performance with a high sensitivity. Moreover, the proposed sensor can be recycled by removing the As(0) deposited on the sensing film with the aid of a simple electrochemical oxidization process. Additionally, the mechanism of the sensing strategy based on the electrochemical n-doping of As(0) into the AuNPs-Gr sensing film was investigated using a top liquid gate electrode. The As(III) concentration detected by the AuNPs-Gr ChemFET sensor was as low as 0.001 μg/L, demonstrating that such sensors with a compatible sensing strategy can be used to detect As(III) with high sensitivity, easy fabrication, fast response and can be recycled.

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

Multifunctional transition metal doped titanium dioxide reduced graphene oxide composites as highly efficient adsorbents and photocatalysts

A series of novel, mesoporous transition metal doped titanium dioxide-reduced graphene oxide composites are synthesized where the dopants were Mn, Co, Ni, Mo, and W. An in-situ synthesis method was used which involves a green, economic, hydrothermal process which can be scaled up easily. The composites show high surface areas (165–245 m 2 /g) and monomodal pore size distributions with average pore diameters in the range of 3–5 nm. The homogeneous distributions of the dopants are seen in SEM-EDX mapping data. The indirect band gaps of the composite materials are in the range of 2.20–2.48 eV. The composites show excellent dye adsorption activities where 100% adsorption of Rhodamine B occurs within 5 min when Mo–TiO 2 /rGO composite is used. Furthermore, the adsorbed dye can either be desorbed or photocatalytically degraded and the composite is reusable up to 10 cycles without losing significant activity. Mo–TiO 2 /rGO composite showed excellent adsorption of both cationic and anionic dyes at neutral pH.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Enantioselective catalysts based on metal-organic framework-supported nucleotides

Adenosine triphosphate (ATP) and other nucleotides can be irreversibly bound to the metal-organic framework (MOF) MIL-101(Cr). Analysis of X-ray diffraction data suggests that the location of the adsorbed ATP molecule is in proximity of the Cr 3 clusters. Solid-state NMR and DFT calculations indicate that ATP is bound to MIL-101(Cr) through linkages of the terminal phosphate group with Cr(III) of the framework. In the presence of Cu(II) ions, the MOF-supported nucleotides can function as stable and reusable enantioselective heterogeneous catalysts for reactions like Diels-Alder and Michael addition. In conclusion, compared to the corresponding homogeneous nucleotide-based artificial metalloenzymes (ArMs), the MOF-supported nucleotide-based ArMs exhibit significantly enhanced activity and selectivity in certain cases, demonstrating their potential as a new class of enantioselective heterogeneous catalysts.

36 MATERIALS SCIENCE↗
DOE OSTI2023

ZnO/CuO nanostructures anchored over Ni/Cu tubular films via pulse electrodeposition for photocatalytic and antibacterial applications

In this work, we report the fabrication of Ni and Cu tubular substrates and the synthesis of ZnO/CuO nanocomposite on them through the process of pulse electrodeposition. The systematic variation in CuO incorporation in the ZnO matrix and the processing technique were noticed to affect the structural, optical, photocatalytic, and anti-bacterial properties, which are well in accordance with the Field Emission-Scanning Electron Microscope, X-ray Diffraction, Fourier transform Infrared Spectroscopy and UV-Differential reflectance spectroscopy results. The remediation capabilities of the photocatalytic substrates were assessed through the degradation of methylene blue (MB) dye under solar irradiation. Optimized CuO incorporation within the ZnO nanorods resulted in the degradation of a 20 ppm of MB dye solution within 40 min and a higher concentration of 50 ppm within 95 min. The Ni and Cu electroformed tubes as substrates provided not only a reusable supporting frame but also a large surface-area for the growth of ZnO/CuO nanocomposite. The current study also dealt with the anti-bacterial efficacy of the above-mentioned substrates against E.coli. Hence, the Ni and Cu tubular thin film substrates with nanorods of ZnO/CuO composite were explored for the removal of organic as well as biological contaminants from waste water.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Effective removal of trace-level toxic metals from flue gas desulfurization wastewater using SiO 2 supported hydrogel sorbent

Flue gas desulfurization (FGD) wastewater generated from coal-fired power plants contain potentially harmful heavy metal pollutants that pose a threat to public health and clean water. In this work, we present a water stable polyethylenimine-n,n’-methylenebisacrylamide (PEI-MBAA) functionalized SiO 2 solid sorbent material (PMS-1.2/1/4) and investigate its metal adsorption kinetics, selectivity, regenerability, and space velocity. The kinetic studies of six of the toxic heavy metals (As, Cd, Cr, Pb, Se, and Hg) prepared with single elements in Milli-Q water showed the effect of chemical bonding and intraparticle mass transfer resistance on the sorption process. The selectivity studies demonstrated the significant adsorption efficiency toward trace-level heavy metals (Se, Cd, U, Al, etc.) from authentic industrial FGD wastewater. Through five consecutive adsorption–desorption cycles with the FGD (uptake)-citrate (release)-based buffer pair, the sorbent showed high heavy metal removal ability and good reusability. The maximum flow rate for the removal of Se from industrial FGD wastewater was determined to be as high as 8 bed volumes/minute of the sorbent bed. Finally, the results demonstrate the PMS-1.2/1.4 sorbent is a promising candidate for the removal of heavy metals from practical aqueous solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Cascading economic losses from port disruptions under capacity constrained multimodal freight networks

This study quantifies how throughput disruptions at major seaports cascade through capacity-constrained multimodal freight networks and interregional production systems. We couple an agent-based model (ABM) multimodal freight simulation that resolves rerouting, terminal queueing, and inventory drawdown under binding modal and facility capacities with a multiregional output loss input-output (MRIIM) model that propagates realized delivery shortfalls across regions and sectors. The framework is demonstrated for the Port of Los Angeles using Freight Analysis Framework flows and Bureau of Economic Analysis input-output accounts and is evaluated over a 52-week horizon under deterministic sector targeted shocks and stochastic disruption realizations with uncertain severity and duration. Results indicate nonlinear amplification: realized national losses concentrate in manufacturing and transportation/warehousing even when exogenous port shocks are dispersed, suggesting that congestion spillback and limited short-run substitution can dominate the initial shock allocation. We further evaluate a tabular reinforcement-learning (Q-learning) intervention layer that selects among a small set of implementable system level levers (truck-to-rail and truck-to-barge shift settings) without overriding shipper routing, finding that such interventions reduce total losses for moderate disruptions but yield diminishing returns once substitute modes approach capacity. By linking operational freight behavior to system wide impacts under uncertainty, the proposed ABM-MRIIM pipeline provides a reusable workflow for port disruption stress testing, identification of structurally critical sectors/corridors, and evaluation of resilience interventions under realistic capacity limits.

42 ENGINEERING↗
DOE OSTI2020

An Automated Ultraclean Ion Exchange Separation Method for the Determinations of 232Th and 238U in Copper using Inductively Coupled Plasma Mass Spectrometry

This work presents a novel automated analytical method developed for high throughput ultrasensitive determinations of Th and U in copper using inductively coupled plasma mass spectrometry (ICP-MS). The method is based on the use of ultra clean sample preparation procedures including the use of a fully automated off-line chromatography system for the extraction and pre-concentration of the analytes prior to ICP-MS analysis. The separation system is equipped with a single reusable chromatographic column that, with the rigorously clean procedures developed herein, provided a low carryover, low background, and reproducible automated separation method. Isotope dilution methods were used for quantitation. Method Detection Limits (MDLs) of 3.7 and 9.4 fg·g-1 were obtained for the quantitation of 232Th and 238U in copper, respectively, corresponding to activities of 0.0148 and 0.116 microBq·kg-1 for 232Th and 238U, respectively. The analytical method provided high and reproducible tracer recoveries. The use of an automated system significantly reduced the extremely tedious active work time required for the chemist (by ca. 80%) relative to manually performing the separation. This method could be adapted to other critical sample matrices requiring the utmost in high throughput clean chemistry procedures for automated ultrasensitive analyses.

Arnquist, Isaac J.↗
DOE OSTI2024

Functional stimuli-responsive polymers on micro- and nano-patterned interfaces

Micro- and nano-patterned surfaces offer precise control over morphology and chemical composition, enhancing the stability, durability, and functionality of coating materials. When combined with stimuli-responsive polymers, these surfaces gain dynamic adaptability, enabling reversible binding, reusable sensing, and selective molecular capture. Furthermore, while recent review articles have explored various aspects of stimuli-responsive materials, from hydrogel patterns for bioanalytical applications to shape-morphing hydrogels for soft robotics and sensors, a comprehensive review focused on the integration of smart polymers with micro- or nano-patterned interfaces remains absent. This review addresses key surface patterning techniques, including soft lithography, colloidal lithography, and polymer brush photolithography, as well as advances in surface-initiated polymerization methods, such as surface-initiated controlled radical polymerization (SI-CRP). In addition, we discuss recent progress in integrating stimuli-responsive polymers with patterned surfaces to create advanced, functional materials.

Colloidal lithography↗
DOE OSTI2021

An overview of data tools for representing and managing building information and performance data

Building information modeling (BIM) has been widely adopted for representing and exchanging building data across disciplines during building design and construction. However, BIM's use in the building operation phase is limited. With the increasing deployment of low-cost sensors and meters, as well as affordable digital storage and computing technologies, growing volumes of data have been collected from buildings, their energy services systems, and occupants. Such data are crucial to help decision makers understand what, how, and when energy is consumed in buildings—a critical step to improving building performance for energy efficiency, demand flexibility, and resilience. However, practical analyses and use of the collected data are very limited due to various reasons, including poor data quality, ad-hoc representation of data, and lack of data science skills. To unlock value from building data, there is a strong need for a toolchain to curate and represent building information and performance data in common standardized terminologies and schemas, to enable interoperability between tools and applications. This study selected and reviewed 24 data tools based on common use cases of data across the building life cycle, from design to construction, commissioning, operation, and retrofits. The selected data tools are grouped into three categories: (1) data dictionary or terminology, (2) data ontology and schemas, and (3) data platforms. The data are grouped into ten typologies covering most types of data collected in buildings. This study resulted in five main findings: (1) most data representation tools can represent their intended data typologies well, such as Green Button for smart meter data and Brick schema for metadata of sensors in buildings and HVAC systems, but none of the tools cover all ten types of data; (2) there is a need for data schemas to represent the basis of design data and metadata of occupant data; (3) standard terminologies such as those defined in BEDES are only adopted in a few data tools; (4) integrating data across various stages in the building life cycle remains a challenge; and (5) most data tools were developed and maintained by different parties for different purposes, their flexibility and interoperability can be improved to support broader use cases. Finally, recommendations for future research on building data tools are provided for the data and buildings community based on the FAIR principles to make data Findable, Accessible, Interoperable, and Reusable.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Neural network representation of electronic structure from ab initio molecular dynamics

Despite their rich information content, electronic structure data amassed at high volumes in ab initio molecular dynamics simulations are generally under-utilized. Here we introduce a transferable high-fidelity neural network representation of such data in the form of tight-binding Hamiltonians for crystalline materials. This predictive representation of ab initio electronic structure, combined with machine-learning boosted molecular dynamics, enables efficient and accurate electronic evolution and sampling. When it is applied to a one-dimension charge-density wave material, carbyne, we are able to compute the spectral function and optical conductivity in the canonical ensemble. The spectral functions evaluated during soliton-antisoliton pair annihilation process reveal significant renormalization of low-energy edge modes due to retarded electron-lattice coupling beyond the Born–Oppenheimer limit. The availability of an efficient and reusable surrogate model for the electronic structure dynamical system will enable calculating many interesting physical properties, paving the way to previously inaccessible or challenging avenues in materials modeling.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

GBOpt: Grain boundary structure optimization using Monte Carlo and evolutionary algorithms

Polycrystalline materials are made of many small crystals separated by grain boundaries (GBs), whose atomic structure strongly influences material properties. Because the structure of a GB determines its properties, the optimal structure must be known in order to determine those impacts. There are many ways of placing atoms in the GB region, but the optimal structure is defined as the one that gives the lowest value of a target property (typically energy). GB structure optimization has been successfully demonstrated using stochastic and evolutionary methods, but no reusable, community-maintained open-source workflow has been developed. GBOpt (Grain Boundary Optimization) is an open-source Python package that creates that workflow, where we have presently implemented two approaches: Markov Chain Monte Carlo, and genetic algorithm based on elite selection. We demonstrate this capability by successfully reproducing the known optimal structures of a specific GB in two materials, and point interested readers to the GitHub repository for additional examples, including optimization for different properties. Both of the implemented approaches recovered the known structures, with the genetic algorithm approach finding the optimal structure faster on average.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Challenges and Vision for Standardization of Biopolymer Data Sets for Machine Learning

Machine learning (ML) is transforming materials research, yet potential for biopolymer discovery remains constrained by fragmented data and nonstandardized reporting. Biopolymers differ significantly from synthetic polymers, requiring specialized approaches to represent their biosynthetic origins, hierarchical structures, and application-specific metrics. In this Perspective, we identify three core challenges limiting biopolymer representation: information encoding, data quality, and data sharing. We describe the most pressing issues and propose commensurate approaches to address each key challenge. Recommendations include the design and adoption of biopolymer-specific fingerprinting and representation frameworks, development of hybrid human-large language model (LLM) data extraction strategies, and expanding Findable, Accessible, Interoperable, Reusable (FAIR)-compliant repositories. We propose a robust foundation to define interoperable, high-quality data sets that capture the full context of biopolymer materials. Standardized metadata, shared ontologies, and community-driven infrastructure would enable scalable, reproducible workflows and accelerate the ML-driven development of biopolymers.

36 MATERIALS SCIENCE↗