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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Formation trajectories of solution-processed perovskite thin films from mixed solvents

The engineering of mixed-solvent formulations and their evaporation conditions are key to reproducible perovskite coatings for high-performance photovoltaics. Here, we report a lumped-parameter evaporation model to predict the evolution of a perovskite ink liquid film over time (solvent ratio, solute concentration, and film thickness). The drying-rate model is validated via in situ film-thickness measurements, and the predicted transient liquid film state is mapped as a process path. These methods allow for the prediction of process sensitivity to local environmental factors and the understanding and visualization of a broader processing parameter space enabled through the coupling of process and ink engineering. Process maps are applied to create a new framework for scalable perovskite coating development with a goal of improving the reproducibility and transferability of perovskite fabrication. This approach is demonstrated with blade-coated FA 0.83 Cs 0.17 PbI 3 photovoltaic devices, improving the photovoltaic conversion efficiency from 17.5% ± 1.7% to 20.3% ± 0.6%.

14 SOLAR ENERGY↗
DOE OSTI2023

Dynamically Formed Active Sites on Liquid Boron Oxide for Selective Oxidative Dehydrogenation of Propane

Boron-based catalysts have been shown to be both active and selective for driving the oxidative dehydrogenation of propane (ODHP) without the use of precious metals. This reaction occurs at temperatures that melt the oxide catalyst which challenges our ability to identify the liquid structures of the boron oxide phase under reaction conditions, hindering the understanding of its active sites and reaction mechanism. By combining ab initio molecular dynamics simulation, in-situ Raman characterization, and microkinetic modeling, we propose that the di-coordinated boron sites (BO2) in liquid boron oxide are the active species for O2 activation under reaction conditions. The formed peroxy-like species (>B-O-O-B<) can be viewed as a moderate oxidant for ODHP. The dynamical >B-O* dangling bond originated from >B-O-O-B< site as well as the liquid B2O3 structure itself, plays a critical role in the abstraction of H atoms from propane (C3H7 radical formation). Microkinetic modeling reveals C3H7 radical formation to be the main rate controlling step (~75% degree of rate control) with the dehydration of boron hydroxyls (B-OHs) to recover the di-coordinated boron active sites controlling the remainder of the rate (~25% degree of rate control). Moreover, the activation barriers are found to strongly depend upon the surface B-OH concentration. These findings provide significant insights into the active site and reaction mechanisms on boron-based catalysts for ODHP and underlie the importance of understanding the liquid nature of the catalyst to account for the catalytic activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

The Helicity Sign of Flux Transfer Event Flux Ropes and Its Relationship to the Guide Field and Hall Physics in Magnetic Reconnection at the Magnetopause

Flux Transfer Events (FTEs) are transient magnetic flux ropes typically found at the Earth's magnetopause on the dayside. While it is known that FTEs are generated by magnetic reconnection, it remains unclear how the details of magnetic reconnection controls their properties. A recent study showed that the helicity sign of FTEs positively correlates with the east-west (B y ) component of the Interplanetary Magnetic Field (IMF). With data from the Cluster and Magnetospheric Multiscale missions, we performed a statistical study of 166 quasi force-free FTEs. We focus on their helicity sign and possible association with upstream solar wind conditions and local magnetic reconnection properties. Using both in situ data and magnetic shear modeling, we find that FTEs whose helicity sign corresponds to the IMF B y are associated with moderate magnetic shears while those that do not correspond to the IMF B y are associated with higher magnetic shears. While uncertainty in IMF propagation to the magnetopause may lead to randomness in the determination of the flux rope core field and helicity, we rather propose that for small IMF B y , which corresponds to high shear and low guide field, the Hall pattern of magnetic reconnection determines the FTE core field and helicity sign. In that context we explain how the temporal sequence of multiple X-line formation and the reconnection rate are important in determining the flux rope helicity sign. This work highlights a fundamental connection between kinetic processes at work in magnetic reconnection and the macroscale structure of FTEs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

One-dimensional twisted and tubular structures of zinc oxide by semiconductor-catalyzed vapor–liquid–solid synthesis

The exploration of unconventional catalysts for the vapor-liquid-solid synthesis of one-dimensional materials promises to yield new morphologies and functionality. Here, we show, for the model ZnO system, that unusual nanostructures can be produced via a semiconductor (Ge) catalyst. As well as the usual straight nanowires, we describe two other distinct morphologies: twisted nanowires and twisted nanotubes. The twisted nanotubes show large hollow cores and surprisingly high twisting rates, up to 9°/μm, that cannot be easily explained through the Eshelby twist model. A combination of ex situ and in situ transmission electron microscopy measurements suggest that the hollow core results from a competition between growth and etching at the Ge-ZnO interface during synthesis. The twisting rate is consistent with a softening of elastic rigidity. Finally, these results indicate that the use of unconventional, nonmetallic catalysts provides opportunities to synthesize unusual oxide nanostructures with potentially useful properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Toward a quantitative description of solvation structure: a framework for differential solution scattering measurements

Appreciating that the role of the solute–solvent and other outer-sphere interactions is essential for understanding chemistry and chemical dynamics in solution, experimental approaches are needed to address the structural consequences of these interactions, complementing condensed-matter simulations and coarse-grained theories. High-energy X-ray scattering (HEXS) combined with pair distribution function analysis presents the opportunity to probe these structures directly and to develop quantitative, atomistic models of molecular systems in situ in the solution phase. However, at concentrations relevant to solution-phase chemistry, the total scattering signal is dominated by the bulk solvent, prompting researchers to adopt a differential approach to eliminate this unwanted background. Though similar approaches are well established in quantitative structural studies of macromolecules in solution by small- and wide-angle X-ray scattering (SAXS/WAXS), analogous studies in the HEXS regime—where sub-ångström spatial resolution is achieved—remain underdeveloped, in part due to the lack of a rigorous theoretical description of the experiment. To address this, herein we develop a framework for differential solution scattering experiments conducted at high energies, which includes concepts of the solvent-excluded volume introduced to describe SAXS/WAXS data, as well as concepts from the time-resolved X-ray scattering community. Our theory is supported by numerical simulations and experiment and paves the way for establishing quantitative methods to determine the atomic structures of small molecules in solution with resolution approaching that of crystallography.

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

Fluorescent Detection of Carbon Disulfide by a Highly Emissive and Robust Isoreticular Series of Zr-Based Luminescent Metal Organic Frameworks (LMOFs)

Carbon disulfide (CS 2 ) is a highly volatile neurotoxic species. It is known to cause atherosclerosis and coronary artery disease and contributes significantly to sulfur-based pollutants. Therefore, effective detection and capture of carbon disulfide represents an important aspect of research efforts for the protection of human and environmental health. In this study, we report the synthesis and characterization of two strongly luminescent and robust isoreticular metal organic frameworks (MOFs) Zr 6 (µ 3- O) 4 (OH) 8 (tcbpe) 2 (H 2 O) 4 (here termed 1) and Zr 6 (µ 3- O) 4 (OH) 8 (tcbpe-f) 2 (H 2 O) 4 (here termed 2) and their use as fluorescent sensors for the detection of carbon disulfide. Both MOFs demonstrate a calorimetric bathochromic shift in the optical bandgap and strong luminescence quenching upon exposure to carbon disulfide. The interactions between carbon disulfide and the frameworks are analyzed by in-situ infrared spectroscopy and computational modelling by density functional theory. These results reveal that both the Zr metal node and organic ligand act as the preferential binding sites and interact strongly with carbon disulfide.

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

BETO 2021 Peer Review - Overview of the Chemical Catalysis for Bioenergy Consortium

Catalysis plays a central role in converting biomass and carbon-rich waste feedstocks into fuels and chemicals; however, critical catalysis challenges exist that are limiting commercialization of emerging bioenergy technologies. By leveraging unique U.S. Department of Energy National Laboratory capabilities and expertise, the Chemical Catalysis for Bioenergy consortium seeks to overcome these catalysis challenges and accelerate the catalyst and process development cycle. The foundation of the consortium consists of an integrated and collaborative portfolio of catalytic technologies and enabling capabilities, which positions ChemCatBio to address both technology-specific and overarching catalysis challenges across the development cycle from discovery to scale-up. The core catalysis projects target technological advancements for specific conversion processes, such as catalytic upgrading of biochemical process intermediates, catalytic fast pyrolysis, C1 and C2 upgrading, and electrochemical CO2 reduction, while the enabling technologies provide access to world-class capabilities and expertise in computational modeling, materials synthesis, advanced in situ and in operando catalyst characterization, and catalyst design tools.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

New Insights into Secondary Organic Aerosol Formation at the Air–Liquid Interface

Air-liquid interfacial processing of volatile organic compound oxidation has been suggested as an important source of secondary organic aerosols. However, owing to the lack of techniques for in situ air-liquid interface analysis, the detailed interfacial mechanism remains speculative. To obviate this, the analysis of air-liquid interfacial reactions and the dynamics of glyoxal oxidation as a model volatile organic compound using in situ liquid synchrotron-based vacuum ultraviolet single photon ionization mass spectrometry enabled by the system for analysis at the liquid vacuum interface microreactor is reported. This approach allows for determination of reaction intermediates and oxidation products including polymers and oligomers at the air-liquid interface by mass spectral analysis and appearance energy measurements. Furthermore, an expanded reaction mechanism of photooxidation free radical induced reactions as a source of aqueous secondary organic aerosol formation is proposed based on these new results, suggesting that single photon ionization could be applied to provide unique insights into interfacial chemistry changing the earth atmospheric composition.

aqueous secondary organic aerosol, glyoxal, photoo↗
DOE OSTI2026

Attention-based 3D – convolutional neural network model for mechanical property predictions using visible light images in metal additive manufacturing

Additive manufacturing (AM), while commonly used for rapid prototyping and creating components with complex geometries, has not been widely adopted for critical applications across the aerospace, automotive, defense, energy, and medical industries. This is, in part, due to the challenges of controlling flaws and uncertainty in the mechanical behavior of additively manufactured components. In recent years, there has been an increase in research aimed at predicting the final mechanical properties of additively manufactured components during the printing process. To address these issues, a 3D-CNN model was trained using low-cost in situ visible-light camera data, anomaly classifications, and the chosen process parameters to predict the ultimate tensile strength (UTS), yield strength (YS), total elongation (TE), and uniform elongation (UE). The 3D-CNN layers of the model employed attention mechanisms to prioritize features in the data, thereby improving prediction accuracy. Furthermore, the effect of each process parameter and anomaly class is investigated using attention-based dynamic sigmoid weighted gates to interpret the influence each class has on the final prediction. Different combinations of the in situ data were fed into the 3D-CNN, with varying amounts of image layers, to determine the ideal combination for predicting mechanical properties in situ. Here, the 3D-CNN model achieved mean absolute percentage errors (MAPE) below 5% for both UTS and YS while using only a single camera input and under half of the available image layers.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Microstructure, Thermal, and Mechanical Properties Relationships in U and UZr Alloys (Final Report)

Uranium-zirconium (U-Zr) alloys are candidate fuel systems for transmutation based reactors that can be used to burn long-lived minor actinides and fission products in fast spectrum reactors. Metallic fuels have also been gaining more recent attention for applications as accident tolerant fuels, as well as for use in small modular reactors. This research focused on a “science-based” approach to capture the connections between U and U-Zr alloys’ three-dimensional (3-D) microstructure, thermal properties, and mechanical properties through closely coordinated experiments and modeling efforts from the unirradiated to the irradiated fuels. Advanced characterization and modeling techniques were used to understand irradiation-induced microstructural evolution and its direct impact on the thermal and mechanical properties of U and U-Zr fuel. Closely coordinated experiments and modeling were performed to provide crucial data that does not currently exist. Overall, this research spanned multiple length and time scales within the models and experiments. The scope of the research encompassed the understanding of the irradiation effects in U and various U-Zr alloys with particular attention paid to three task areas: (1) microstructural evolution, (2) in-situ/ex-situ thermal and mechanical properties, and (3) multiscale modeling connections to microstructure, thermal, and mechanical properties. This research resulted in (1) the 3-D characterization of neutron irradiated U-Zr fuel in multiple phase regions to better understand fission gas swelling and constituent redistribution, (2) development of a microstructural model linking thermal and mechanical properties via in situ Raman and nanoindentation, (3) and mesoscale phase field modeling was coupled with the AEH method in the MOOSE framework was used to calculate the effective thermal conductivities of U-Zr fuels consisting of α-U and δ-UZr 2 heterogeneous microstructures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Threefold reduction of modeled uncertainty in direct radiative effects over biomass burning regions by constraining absorbing aerosols

Absorbing aerosols emitted from biomass burning (BB) greatly affect the radiation balance, cloudiness, and circulation over tropical regions. Assessments of these impacts rely heavily on the modeled aerosol absorption from poorly constrained global models and thus exhibit large uncertainties. By combining the AeroCom model ensemble with satellite and in situ observations, we provide constraints on the aerosol absorption optical depth (AAOD) over the Amazon and Africa. Our approach enables identification of error contributions from emission, lifetime, and MAC (mass absorption coefficient) per model, with MAC and emission dominating the AAOD errors over Amazon and Africa, respectively. In addition to primary emissions, our analysis suggests substantial formation of secondary organic aerosols over the Amazon but not over Africa. Furthermore, we find that differences in direct aerosol radiative effects between models decrease by threefold over the BB source and outflow regions after correcting the identified errors. This highlights the potential to greatly reduce the uncertainty in the most uncertain radiative forcing agent.

09 BIOMASS FUELS↗
DOE OSTI2022

AI-Enabled Discovery and Physics-Based Optimization of Energy Efficient Processing Strategies for Advanced Turbine Alloys (Final Technical Report)

In this project, the multi-organizational team of academic and industrial researchers from the University of Kentucky an aerospace and energy generation OEM partner has leveraged novel Digital Process Twin (DPT) models of process/structure interactions (i.e., process-induced surface integrity) to advance a paradigm of fully integrated computational materials engineering (ICME). Using efficient process models as the core of a digital process simulator for a reinforcement learning algorithm, the team has integrated industrial data and metrics of structure/performance/energy relationships and manufacturing-related energy metrics to optimize dynamic processing parameters for significantly improved life-cycle energy efficiency of advanced γ-TiAl low-pressure turbine (LPT) alloys, as indicated by a set of design relevant parameters (e.g., residual stresses and scrap rate). The key objective and anticipated outcome of the project was at least a 10% reduction in life-cycle embodied energy for a recently developed, γ-TiAl low-pressure turbine (LPT) alloy and nickel-based superalloy Inconel 718, through the adoption of the proposed AI-enabled process optimization approach. The final project outcomes significantly exceeded this original target, realizing manufacturing-related energy efficiency improvements of more than 130% for TiAl and up to 80% for Inconel 718. Rather than following the prevailing and highly inefficient empirical paradigm, the proposed study demonstrated the feasibility of adopting a digital, physics-based process design and optimization paradigm. The recurring need for manual intervention, rework, reinspection causes significant production bottlenecks and unnecessary expense associated with delivering the requisite component quality. The OEM partner, and turbine industry in general, expect to reap significant cost and resource savings if an AI-optimized set of parameters can be applied to specific machining operations. The technical scope of the proposed project involved the paving of a realistic path towards model-based and AI-enabled Integrated Computational Materials Engineering (ICME), and away from inefficient empirical process optimization and legacy manufacturing practices, which are no longer able to efficiently process novel high-performance turbine alloy materials. The project team will address the fundamental knowledge gap that currently exists within the ICME paradigm with respect to the process/structure/performance/energy impacts of finishing processes. While significant resources have been devoted to the ‘early stages’ of manufacturing, such as alloy design, primary and secondary processing, finishing processes have not been adequately integrated within ICME. To provide an actionable path towards model-based finishing process design (e.g., machining, burnishing, grinding, polishing), we will employ a novel AI-enabled process optimization paradigm, based on a computationally efficient, physics-based process simulator. Through limited experimental work to calibrate and validate our process simulator model via an advanced in-situ characterization technique and process optimization via reinforcement learning, the project will seek to demonstrate a viable alternative to the inefficient ‘legacy’ processing strategies, empirical testing and broad scope machining learning approaches, all of which fail to adequately consider complex process physics. The project team has identified an intermetallic γ-TiAl LPT alloy, which is currently being used as part of the OEM partner’s advanced gas turbine designs. This particular alloy poses significant manufacturing challenges during finishing operations, which limit the degree to which the current turbine design can be manufactured in an energy- and cost-efficient manner. Empirical testing and numerical modeling efforts to optimize processing parameters for γ-TiAl have not been able to resolve these manufacturing challenges, so the proposed physics-based AI-enabled optimization technology would offer a truly novel and transformative capability. The multi-organizational team of academic and industry experts from the UKY and the OEM partner will work together closely to demonstrate the analytical and experimental critical function and characteristic proof of concept of this novel approach.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2020

Modeling the smoky troposphere of the southeast Atlantic: a comparison to ORACLES airborne observations from September of 2016

The southeast Atlantic is home to well-defined smoke outflow from Africa coinciding vertically with extensive marine boundary-layer cloud decks, both reaching their climatological maxima in spatial extent around September. A framework is put forth for evaluating the performance of a range of global and regional aerosol models against observations made during the NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) airborne mission in September 2016. The sparse airborne observations are first aggregated into 2o grid boxes and into three vertical layers: the cloud-topped marine boundary layer (MBL), the layer from cloud top to 3 km, and the 3-6 km layer. Aerosol extensive properties simulated for the entire study region for all September suggest that the 2016 ORACLES observations are reasonably representative of the regional monthly average, with systematic deviations of 30% or less. All six models typically place the bottom of the smoke layer at lower altitudes than do the airborne lidar observations by 300-1400 m, whereas model aerosol top heights are within 0-500 m of the observations. All but one of the models that report carbonaceous aerosol masses underestimate the ratio of particulate extinction to the masses, a proxy for mass extinction efficiency, in 3-6 km. Notable findings on individual models include that WRF-CAM5 predicts the mass of black carbon and organic aerosols with minor (~10% or less) biases. GEOS-5 overestimates the carbonaceous particle masses in the MBL by a factor of 3-6. Extinction coefficients in the free troposphere (FT) and above-cloud aerosol optical depth (ACAOD) are 10-30% lower in WRF-CAM5, 30-50% lower in GEOS-5, 10-40% higher in GEOS-Chem, 10-20% higher in EAM-E3SM except for the practically unbiased 3-6 km extinction, and 20-70% lower in the Unified Model, than the airborne in situ, lidar and sunphotometer measurements. ALADIN-Climate also underestimates the ACAOD, by 30%. GEOS-5 and GEOS-Chem predict carbon monoxide in the MBL with small (10% or less) negative biases, despite their overestimates of carbonaceous aerosol masses. Overall, this study highlights a new approach to utilizing airborne aerosol measurements for model diagnosis.

Shinozuka, Yohei↗
DOE OSTI2024

Resource Assessment for Distributed Wind Energy: An Evaluation of Best-Practice Methods in the Continental US

Current wind resources within the United States (US) indicate a potential to profitably install nearly 1,400 gigawatts of distributed wind (DW) capacity. This amount is equivalent to over half of the United States’ current energy demand from electricity, making it enough to power millions of homes and businesses and replace countless fossil fuel-based generating plants. Despite the potential growth of DW in the US, deployments are presently hindered by a lack of confidence in resource estimation methods. One potential challenge is that smaller-scale turbines, with hub heights of 40 meters or less, are disproportionately impacted by obstacles such as buildings and vegetation. These obstacles may produce complex wake effects, best modeled with high-fidelity complex fluid dynamics (CFD) models that are too computationally expensive to use for routine siting and resource assessment. Thus, installers today make use of heuristics and simple equations to approximate the impact of obstacles while also leveraging long-term resource data from commercial or publicly available atmospheric models. This study evaluates these historical and commonly used methods alongside new lower-order obstacle models produced from CFD simulations and measurement-based bias correction. The preliminary results from this study show the importance of taking care in the choice and application of mesoscale atmospheric models and the significant value of bias correction using measurements from nearby meteorological towers. Detailed obstacle modeling provides only modest additional gains in performance and, in some cases, can add error, especially at sites where turbines have already been located to avoid obvious impact from upwind obstacles. These findings reinforce the importance of collecting in situ measurements and suggest that obstacle models may be better applied in practice to automated or computer-aided siting, rather than in economic wind resource assessments.

17 WIND ENERGY↗
DOE OSTI2024

Phase Field Modeling of Near-Wellbore Hydraulic Fracture Nucleation and Propagation

The goal of Task 2 of the project titled "Closing the loop between in situ stress complexity and EGS fracture complexity" is to model fracture nucleation and propagation in the near wellbore region and, in combination with the experimental study conducted as part of Task 4, investigate the relationship between the in situ stress and the near-wellbore fracture patterns. To this end, We have identified phase field as a promising modeling approach to model the nucleation and propagation process in the near wellbore region. In phase field, fractures are represented by a diffuse variable, i.e the damage. Compared to approaches that model fractures as sharp interfaces, phase field does not require any re-meshing or element insertion algorithm, which makes the treatment of complex fracture geometries straightforward, even with simple meshes.

15 GEOTHERMAL ENERGY↗