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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Advanced genetic tools enable synthetic biology in the oleaginous microalgae Nannochloropsis sp.

Nannochloropsis is a genus of fast-growing microalgae that are regularly used for biotechnology applications. Nannochloropsis species have a high triacylglycerol content and their polar lipids are rich in the omega-3 long-chain polyunsaturated fatty acid, eicosapentaenoic acid. Placed in the heterokont lineage, the Nannochloropsis genus has a complex evolutionary history. Genome sequences are available for several species, and a number of transcriptomic datasets have been produced, making this genus a facile model for comparative genomics. There is a growing interest in Nannochloropsis species as models for the study of microalga lipid metabolism and as a chassis for synthetic biology. Recently, techniques for gene stacking, and targeted gene disruption and repression in the Nannochloropsis genus have been developed. These tools enable gene-specific, mechanistic studies and have already allowed the engineering of improved Nannochloropsis strains with superior growth, or greater bioproduction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Nanopore-enabled electrode arrays and ensembles

Here, (with 116 refs.) we address recent developments in nanoelectrode arrays and ensembles with particular attention to nanopore-enabled arrays and ensembles. Nanoelectrode-based arrays exhibit unique mass transport and ion transfer properties, which can be exploited for electroanalytical measurements with enhanced figures-of-merit with respect to microscale and larger components. Following an introduction into the topic, we cover (a) methods for fabrication of solid-state nanopore electrodes, (b) chemical and biochemical sensors, (c) nanochannel arrays with embedded nanoelectrodes; (d) recessed nanodisk electrode arrays; (e) redox cycling in nanopore electrode arrays, (f) finally discuss novel nanoarrays for electrochemistry, and then give a future outlook. We also discuss a wide variety of nanoelectrode array-based chemical and biochemical sensors properties in addition to faradaic, ion transfer and spectroelectrochemical applications.

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

MADE3D: Enabling the next generation of high-torque density wind generators by additive design and 3D printing

Direct-drive wind turbine generators are increasing in popularity, thanks to recent project developments—especially offshore, where reliability and efficiency are major cost drivers. Yet, high capital costs are forcing many original equipment manufacturers to consider lightweight, high-torque density generators for next-generation multi-megawatt turbines that may be difficult to realize by traditional design or manufacturing methods. In this study, we present a new design framework enabled by advanced machine learning and multimaterial additive manufacturing to perform a magnetic topology optimization that maximizes the torque per rotor active mass for a 15-megawatt direct-drive permanent magnet wind generator. A comparison of the proposed approach against conventional topology optimization demonstrated a significant increase in computational efficiency and accuracy in performance predictions. Results using single and multimaterial compositions for rotor core and magnets identify a wider choice of 3D printable designs for a given specification. A hybrid combination of sintered and dysprosium-free polymer-bonded magnets shows good potential for torque performance by saving material costs up to 8.75%. More than 30% improvement in rotor torque densities is identified which can marginally improve the overall generator torque density. With the rapid evolution of multipowder deposition technolgies, this study can greatly inspire a new paradigm for design-driven manufacturing with novel material compositions and lightweight, low-cost, high-strength multimaterial geometries that were previously unexplored for direct-drive generators.

17 WIND ENERGY↗
DOE OSTI2021

Ecological stoichiometry as a foundation for omics-enabled biogeochemical models of soil organic matter decomposition

Coupled biogeochemical cycles drive ecosystem ecology by influencing individual-to-community scale behaviors; yet the development of process-based models that accurately capture these dynamics remains elusive. Soil organic matter (SOM) decomposition in particular is influenced by resource stoichiometry that dictates microbial nutrient acquisition (‘ecological stoichiometry’). Despite its basis in biogeochemical modeling, ecological stoichiometry is only implicitly considered in high-resolution microbial investigations and the metabolic models they inform. State-of-science SOM decomposition models in both fields have advanced largely separately, but they agree on a need to move beyond seminal pool-based models. This presents an opportunity and a challenge to maximize the strengths of various models across different scales and environmental contexts. To address this challenge, we contend that ecological stoichiometry provides a framework for merging biogeochemical and microbiological models, as both explicitly consider substrate chemistries that are the basis of ecological stoichiometry as applied to SOM decomposition. We highlight two gaps that limit our understanding of SOM decomposition: (1) understanding how individual microorganisms alter metabolic strategies in response to substrate stoichiometry and (2) translating this knowledge to the scale of biogeochemical models. We suggest iterative information exchange to refine the objectives of high-resolution investigations and to specify limited dynamics for representation in large-scale models, resulting in a new class of omics-enabled biogeochemical models. Assimilating theoretical and modelling frameworks from different scientific domains is the next frontier in SOM decomposition modelling; advancing technologies in the context of stoichiometric theory provides a consistent framework for interpreting molecular data, and further distilling this information into tractable SOM decomposition models.

Carbon cycling↗
DOE OSTI2019

Data-enabled structure–property mappings for lanthanide-activated inorganic scintillators

The use of machine learning (ML) models toward development of validated structure–property relationships for two fundamental properties of activated inorganic scintillators for high energy radiation detection, namely the light yield (LY) and the decay time constant, is explored. The ML models are built on easily accessible proxies of materials—interchangeably referred to as features, descriptors or fingerprints—that are carefully selected on the basis of a physical understanding of the scintillation mechanism. Our study indicates that the developed physics-based ML models employing kernel ridge regression (KRR) and AdaBoost algorithm applied on top of a decision tree-based regression are able to “learn” the underlying design rules in a multi-dimensional feature space and thereby enable reasonably accurate predictions of the two target properties on unseen compounds (i.e., on a held-out test set). For instance, within a set of twenty-five cerium- or europium-doped scintillator materials, our analysis reveals a strong correlation between the average ionic part of the dielectric constant and the LY, irrespective of the specific chemistry of the compounds, indicating that the average ionic part of the dielectric constant is a particularly relevant descriptor toward prediction of the LY. Our results also demonstrate that, despite the use of small training datasets, the developed models are able to quickly distinguish high performing chemistries from those with relatively poor performance and therefore can play a crucial role in screening of new compounds with an attractive combination of targeted properties. Finally, the present study provides necessary motivation for future efforts involving ML models with relatively large training datasets, vast feature space explorations, and experimental design in search of promising novel scintillator chemistries.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Recent Developments in Femtosecond Laser-Enabled TriBeam Systems

Streams of multimodal three-dimensional (3D) and four-dimensional (4D) data are revolutionizing our ability to design and predict the behavior of a broad array of advanced materials systems. Over the last 10 years, a new 3D imaging platform consisting of a femtosecond (fs) pulsed laser coupled with a focused ion beam scanning electron microscope (FIB SEM) has been developed by UC Santa Barbara in collaboration with Thermo Fisher Scientific (formerly FEI). The femtosecond-laser-enabled FIB SEM, called the TriBeam, has become one of the only 3D serial sectioning methods available that can gather millimeter-scaled multimodal datasets at sub-μm voxel resolutions; these length scales are critical for many materials problems. Multimodal chemical, crystallographic, and morphological information can be gathered rapidly on a layer-by-layer basis and reconstructed in 3D. Large (gigabyte to terabyte scale) 3D datasets have been generated for a broad array of materials systems, including metallic alloys, ceramics, biomaterials, polymer- and ceramic-matrix composites, and semiconductors. The research tasks conducted have resulted in a completely new design, operating with a dual-wavelength femtosecond-pulsed laser on a plasma focused ion beam (PFIB) platform.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Chloride Molten Salt Electrolysis Enables Integrated and Energy-Efficient Process for NdFeB Magnet Fabrication

Rare-earth elements (REEs) have been identified by NATO, the USDOE, and USGS as critical materials, i.e., materials which have significant demand yet pose supply-chain risks. Many of the existing processes for separations, metallization, and final parts production used across the REE supply chain involve energy intensive steps. For example, neodymium (Nd or NdPr) is produced using oxyfluoride electrolysis of Nd 2 O 3 , which requires hydrofluoric acid to produce a key electrolyte component (NdF 3 ) and generates undesired perfluorocarbon (PFC) gases. Such challenges make securing a resilient supply chain for NdFeB permanent magnets in countries like the United States prohibitively difficult. Here, we propose a chloride-based MSE process that circumvents these challenges, delivering high-purity NdPr from a (NdPr)Cl 3 feed from upstream REE separations. This eliminates environmentally-damaging steps of oxalate or carbonate precipitation and calcination, and enables superior production rates due to greater solubility of (NdPr)Cl 3 in chloride melts compared to Nd 2 O 3 . We show that CMSE generates high-purity NdPr (99.4 wt.%) while being energy-efficient (~ 6 kWh/kg-Nd). NdPr from CMSE was used to fabricate a NdFeB magnet with an excellent maximum energy product (> 40 MGOe), comparable to commercially available NdFeB magnets. This establishes CMSE as a leading approach for integrated, energy-efficient NdFeB magnet production.

Materials science↗
DOE OSTI2021

Enabling modern data discovery for atmospheric measurements

The Atmospheric Radiation Measurement (ARM) user facility is a US Department of Energy Office of Science user facility that is managed and operated through a collaborative effort led by nine US Department of Energy national laboratories. The ARM Data Center, located at Oak Ridge National Laboratory, is responsible for the timely collection, processing, and delivery of data products to the scientific community. The ARM Data Center holds more than 11,000 data products, including metadata collected from field campaigns, instruments, value-added products, and principal investigator–contributed data. These data sets are checked for successful transfer (for most data, this transfer is carried out automatically via the network; however, some of the largest data sets and some of the most remote sites require manual shipping of hard disks) and both the data and metadata are processed to a standard format, which is an ARM-standardized structure, via the Network Common Data Form. The Network Common Data Form is a self-describing binary format with many compatible software tools. Once processed, the data are cataloged, stored in the ARM Data Archive, and made discoverable through association with an array of metadata-characterizing information, such as location and measurement classification. These metadata enable powerful search capabilities through the ARM Data Center Data Discovery interface. This paper discusses the workflow of how the new discovery system has been redesigned from user requirements and how the data are distributed to the scientific community.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

New electric force and charge exchange modules in discrete element model enables particle dynamics simulation in electric field

Discrete element modeling (DEM) is an important technique for particle dynamics simulation. The field of metal additive manufacturing often utilizes DEM to simulate the rheological behaviors of powder. Standard contact and short-range interactions are sufficient in most cases but insufficient to describe the particle dynamics with the influence of an electric field. Modeling such a system requires additional physics to describe the particle–field interactions. The relevant physics has been experimentally understood but is not yet available in DEM. Here, we develop a charge exchange and an electric force module. The electric force module governs particle response to the electric field, while the charge exchange module enables particles to acquire proper charge during contact with charged geometries. We validate the modules against analytical calculations and high-speed videos of electrostatic powder deposition experiments. Notably, the model struggles to capture the initial particle levitation. We later deploy a modified electric field, as supported by static electric field simulation, to better approximate the electric field penetration into the powder layer. This modification improves the model’s capability of simulating realistic particle levitation. The results highlight the challenges of modeling particle behaviors in the electric field while demonstrating the feasibility of obtaining quantitative results, which are difficult to measure experimentally.

charge exchange↗
DOE OSTI2026

Causality-respecting adaptive refinement for PINNs: enabling precise interface evolution in phase field modeling

Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving physical systems described by partial differential equations (PDEs). However, their accuracy in dynamical systems, particularly those involving sharp moving boundaries with complex initial morphologies, remains a challenge. Here, this study introduces an approach combining residual-based adaptive refinement (RBAR) with causality-informed training to enhance the performance of PINNs in solving spatio-temporal PDEs. Our method employs a three-step iterative process: initial causality-based training, RBAR-guided domain refinement, and subsequent causality training on the refined mesh. Applied to the Allen-Cahn equation, a widely-used model in phase field simulations, our approach demonstrates significant improvements in solution accuracy and computational efficiency over traditional PINNs. Notably, we observe an ‘overshoot and relocate’ phenomenon in dynamic cases with complex morphologies, showcasing the method’s adaptive error correction capabilities. This synergistic interaction between RBAR and causality training enables accurate capture of interface evolution, even in challenging scenarios where traditional PINNs fail. Our framework not only resolves the limitations of uniform refinement strategies but also provides a generalizable methodology for solving a broad range of spatio-temporal PDEs. The enhanced performance of the RBAR–causality combined framework demonstrates its strong potential for advancing PINN-based modeling of physical systems characterized by complex, evolving interfaces.

Allen-Cahn equations↗
DOE OSTI2020

A versatile and low-cost chip-to-world interface: enabling ICP-MS characterization of isotachophoretically separated lanthanides on a microfluidic device

Microfluidics offer novel and state-of-the-art pathways to process materials. Microfluidic systems drastically reduce timeframes and costs associated with traditional lab-scale efforts in the area of analytical sample preparations. The challenge arises in effectively connecting microfluidics to off-chip analysis tools to accurately characterize samples after treatment on-chip. Here, a fused silica microfluidic device (MFD) is connected to a fused-silica capillary in order to create a world-to-chip connection between the MFD and an inductively coupled plasma mass spectrometer (ICP-MS). Specifically, this is demonstrated by utilizing isotachophoresis (ITP) on a microfluidic chip to separate samples containing up to 14 lanthanides into elementally pure bands. The separated sample is successfully transferred across a 7 nL dead space at the microchip-capillary junction, such that separation resolution is maintained and even increased through the interface and into the ICP-MS, where the elemental composition of the separated sample is analysed. Lanthanide samples of varying composition are detected using ICP-MS, demonstrating this versatile and cost-effective approach that does not degrade the separation quality achieved on the MFD. This simple and inexpensive connection enables fast, low-cost sample preparation immediately prior to injection into an ICP-MS or other analytical instruments.

Lackey, Hope E.↗
DOE OSTI2020

Coupled solute effects enable anomalous high-temperature strength and stability in nanotwinned Al alloys

Nanoprecipitates or grain refinement can effectively enhance the mechanical strength of Al alloys, but the room-temperature strengths of precipitation hardened and nanocrystalline Al alloys often fall below 1 GPa. Furthermore, they are largely plagued by precipitous mechanical softening at elevated temperature below 300°C, mostly due to degraded microstructural stability. Here, we report a mechanism of coupled solute effect in nanotwinned Al-Fe-Ti alloys that enables stability of nanograins up to 400°C and an unprecedented high-temperature flow stress of ~ 1.7 GPa at 300°C. The supersaturated Fe solutes in Al act as effective grain refiner, forming superstrong solid solution alloys. More importantly, empirical evidence combined with first principle calculations indicate that the Ti solutes delay the agglomeration of Fe solutes, thereby remarkably extending the temperature window for the stability of nanograins in nanotwinned Al alloys. Finally, this study highlights the opportunity to design ultrastrong and stable nanostructured alloys for potential high temperature applications via a coupled solute effect.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A semantics-driven framework to enable demand flexibility control applications in real buildings

Decarbonising and digitalising the energy sector requires scalable and interoperable Demand Flexibility (DF) applications. Semantic models are promising technologies for achieving these goals, but existing studies focused on DF applications exhibit limitations. These include dependence on bespoke ontologies, lack of computational methods to generate semantic models, ineffective temporal data management and absence of platforms that use these models to easily develop, configure and deploy controls in real buildings. This paper introduces a semantics-driven framework to enable DF control applications in real buildings. The framework supports the generation of semantic models that adhere to Brick and SAREF while using metadata from Building Information Models (BIM) and Building Automation Systems (BAS). The work also introduces a web platform that leverages these models and an actor and microservices architecture to streamline the development, configuration and deployment of DF controls. The paper demonstrates the framework through a case study, illustrating its ability to integrate diverse data sources, execute DF actuation in a real building, and promote modularity for easy reuse, extension, and customisation of applications. The paper also discusses the alignment between Brick and SAREF, the value of leveraging BIM data sources, and the framework's benefits over existing approaches, demonstrating a 75% reduction in effort for developing, configuring, and deploying building controls.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Integrating renewable energy into mining operations: Opportunities, challenges, and enabling approaches

Mining is one of the most energy-intensive industries worldwide. It also provides a critical source of raw materials for the manufacturing, transportation, construction, and energy sectors. Demand for raw materials is projected to increase as the world population grows and many low-income economies become middle-income countries. This growth in mineral demand, coupled with falling mineral ore grade, will likely increase the mining industry’s energy demand, used for activities across exploration, extraction, beneficiation and processing, and refining. At the time of this writing, mine operations are – due to their remoteness – dependent on fossil fuels such as diesel, heavy oils, and coal. In principle, mining could use energy recovery, renewable energy, and carbon capture to supplement, replace, or mitigate the impacts of fossil fuel use. However, a combination of renewable-energy technologies would be required. We explore challenges, opportunities, and enabling approaches to integrate renewable energy technologies into mining operations by examining the literature, including academic work, technical reports, and data produced by international agencies. Here, we find that despite numerous opportunities, technical issues still need to be considered, but solutions can tailor renewables to the mining industry. Further research should focus on identifying specific opportunities, technologies, and implementation strategies across the value chain of a variety of minerals with similar operational procedures.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Investigation of Stochastic Unit Commitment to Enable Advanced Flexibility Measures for High Shares of Solar PV

As the share of solar photovoltaics (PV) in the power system increases, there is a growing need for flexibility from multiple, possibly interdependent sources to adjust to PV's variability, uncertainty, and diurnal dependence. This paper investigates how stochastic unit commitment leveraging probabilistic solar forecasts can support other flexibility measures under high solar shares. We consider two flexibility measures relevant to day-ahead scheduling: battery energy time-shifting and solar ancillary service provision. Unit commitment and economic dispatch simulations are conducted on a realistic test system based on Texas using day-ahead solar trajectories. The benefits of the two flexibility measures are pronounced when the instantaneous solar share is high, offering cost savings of 10%-20% in the spring. For a Texas-sized system, this translates to hundreds of millions of dollars in cost savings once the installed PV capacity enables instantaneous solar shares regularly exceeding 40%. Using probabilistic forecasts also greatly increases the reliability of upward reserve provision from solar PV, reducing unserved reserves by 50%-100%. Both day-ahead forecast resolution and errors can impact system reliability at high solar shares, but the stochastic formulation has significant value, mitigating reliability impacts on over-forecast days.

ancillary services↗
DOE OSTI2025

Survey-wide asteroid discovery with a high-performance computing enabled non-linear digital tracking framework

Modern astronomical surveys detect asteroids by linking together their appearances across multiple images taken over time. This approach faces limitations in detecting faint asteroids and handling the computational complexity of trajectory linking. Here, we present a novel method that adapts “digital tracking” – traditionally used for short-term linear asteroid motion across images – to work with large-scale synoptic surveys such as the Vera Rubin Observatory Legacy Survey of Space and Time (Rubin/LSST). Our approach combines hundreds of sparse observations of individual asteroids across their non-linear orbital paths to enhance detection sensitivity by several magnitudes. To address the computational challenges of processing massive data sets and dense orbital phase spaces, we developed a specialized high-performance computing architecture. We demonstrate the effectiveness of our method through experiments that take advantage of the extensive computational resources at Lawrence Livermore National Laboratory. This work enables the detection of significantly fainter asteroids in existing and future survey data, potentially increasing the observable asteroid population by orders of magnitude across different orbital families, from near-Earth objects (NEOs) to Kuiper belt objects (KBOs).

Asteroid discovery↗
DOE OSTI2023

Dairy manure acidogenic fermentation at hyperthermophilic temperature enabled superior activity of thermostable hydrolytic enzymes linked to the genus Caldicoprobacter

In this study, fermentation experiments were conducted under mesophilic, thermophilic, and hyperthermophilic conditions to investigate adaptation of microbial communities and its effect on extracellular enzyme activities toward degradation of cellulose, hemicellulose and proteins in dairy manure. Here, hyperthermophilic conditions transformed the microbiome structure and stimulated activity of extracellular proteolytic, cellulolytic, and hemicellulolytic enzymes. Specifically, the activities of protease, cellulose 1,4-β-cellobiosidase, and β-glucosidase secreted by hyperthermophilic microbes were higher by 22%, 47% and 49% compared to those produced by mesophilic and thermophilic communities. Enhanced hydrolytic activity of hyperthermophilic microbes enabled improved feedstock solubilization and production of 39% and 22% more soluble COD than mesophilic and thermophilic microbes, respectively. Connections between hydrolytic function and microbial community structure at various temperatures were assessed using the PICRUSt2 computational tool. Genus Caldicoprobacter was identified as the primary candidate responsible for increased production of thermostable endo-1,4-β-glucanase, β-glucosidase and endo-1,4-β-xylanase, and enhanced hydrolytic performance of hyperthermophilic microbial community.

09 BIOMASS FUELS↗
DOE OSTI2020

Nano-FET-enabled biosensors: Materials perspective and recent advances in North America

Field-effect transistor (FET) is a very promising platform for biosensor applications due to its magnificent properties, including label-free detection, high sensitivity, fast response, real-time measurement capability, low running power, and the feasibility to miniaturize to a portable device. 1D (e.g. carbon nanotubes, Si nanowires, conductive polymer nanowires, 1D metal oxides, and others) and 2D (e.g. graphene materials, transition metal dichalcogenides, black phosphorus, and 2D metal oxides) materials, with their unique structural and electronic properties that are unavailable in bulk materials, have helped improve the sensitivity of FET biosensors and enabled detection down to single molecule. In this article, we give insights into the rapidly evolving field of 1D and 2D materials-based FET biosensors, with an emphasis on structure and electronic properties, synthesis, and biofunctionalization approaches of these nanomaterials. In addition, the progress in the 1D/2D-FET biosensors in North America, in the last decade, is summarized in tables. Moreover, challenges and future perspectives of 1D/2D-FET biosensors are covered.

1D materials↗