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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Spectral emittance measurements of micro/nanostructures in energy conversion: a review

Micro/nanostructures play a key role in tuning the radiative properties of materials and have been applied to the high-temperature energy conversion systems for improved performance. Among the various radiative properties, spectral emittance is of integral importance for the design and analysis of materials that function as radiative absorbers or emitters. This article presents an overview of the spectral emittance measurement techniques using both the direct and indirect methods. Several micro/nanostructures are also introduced, and a special emphasis is placed on the emissometers developed for characterizing engineered micro/nanostructures in high-temperature applications (e.g., solar energy conversion and thermophotovoltaic devices). Both experimental facilities and measured results for different materials are summarized. Future prospects in developing instrumentation and micro/nanostructured surfaces for practical applications are also outlined. This study provides a comprehensive source of information for the application of micro/nanostructures in high-temperature energy conversion engineering.

14 SOLAR ENERGY↗
DOE OSTI2024

Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response

Composite materials with different microstructural material symmetries are common in engineering applications where grain structure, alloying and particle/fiber packing are optimized via controlled manufacturing. In fact these microstructural tunings can be done throughout a part to achieve functional gradation and optimization at a structural level. To predict the performance of particular microstructural configuration and thereby overall performance, constitutive models of materials with microstructure are needed. In this work we provide neural network architectures that provide effective homogenization models of materials with anisotropic components. These models satisfy equivariance and material symmetry principles inherently through a combination of equivariant and tensor basis operations. We demonstrate them on datasets of stochastic volume elements with different textures and phases where the material undergoes elastic and plastic deformation, and show that the these network architectures provide significant performance improvements.

anisotropy↗
DOE OSTI2023

Magnetic and Magnetocaloric Properties of the A 2 LnSbO 6 Lanthanide Oxides on the Frustrated fcc Lattice

Frustrated lanthanide oxides are promising candidates for cryogen-free magnetic refrigeration due to their suppressed ordering temperatures and high magnetic moments. While much attention has been paid to the garnet and pyrochlore lattices, the magnetocaloric effect in frustrated face-centered cubic (fcc) lattices remains relatively unexplored. We previously showed that the frustrated fcc double perovskite Ba 2 GdSbO 6 is a top-performing magnetocaloric material (per mol Gd) because of its small nearest-neighbor interaction between spins. Here we investigate different tuning parameters to maximize the magnetocaloric effect in the family of fcc lanthanide oxides, A 2 LnSbO 6 (A = {Ba 2+ , Sr 2+ } and Ln = {Nd 3+ , Tb 3+ , Gd 3+ , Ho 3+ , Dy 3+ , Er 3+ }), including chemical pressure via the A site cation and the magnetic ground state via the lanthanide ion. Bulk magnetic measurements indicate a possible trend between magnetic short-range fluctuations and the field-temperature phase space of the magnetocaloric effect, determined by whether an ion is a Kramers or a non-Kramers ion. We report for the first time on the synthesis and magnetic characterization of the Ca 2 LnSbO 6 series with tunable site disorder that can be used to control the deviations from Curie–Weiss behavior. Taken together, these results suggest fcc lanthanide oxides as tunable systems for magnetocaloric design.

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

Substitution Effect on Thiobarbituric Acid End Groups for High Open-Circuit Voltage Non-Fullerene Organic Solar Cells

Recent advances in non-fullerene acceptors (NFAs) have resulted in significant improvement in the power conversion efficiencies (PCEs) of organic solar cells (OSCs). In our efforts to boost open-circuit voltage (V OC ) for OSCs, the molecular design employing thiobarbituric acid (TBTA) end groups and an indacenodithieno[3,2-b]thiophene (IDTT) core gives rise to NFAs with significantly raised lowest unoccupied molecular orbital (LUMO) energy level, which, when paired with PCE10, can achieve V OC 's over 1.0 V and decent PCEs that outperform the equivalent devices based on the benchmark ITIC acceptor. While the use of a TBTA end group is effective in tuning energy levels, very little is known about how the alkyl substitution on the TBTA group impacts the solar cell performance. To this end, TBTA end groups are alkylated with linear, branched, and aromatic sidechains to understand the influence on thin-film morphology and related device performances. We report our study has confirmed the dependence of solar cell performance on the end-group substituents. More importantly, we reveal the presence of an ideal window of crystallinity associated with the medium-length hydrocarbon chains such as ethyl and benzyl. Deviation to the shorter methyl group makes the acceptor too crystalline to mix with the polymer donor and form proper domains, whereas longer and branched alkyl chains are too sterically bulky and hinder charge transport due to nonideal packing. Such findings underline the comprehensive nature of thin-film morphology and the subtle end-group effects for the design of non-fullerene acceptors.

14 SOLAR ENERGY↗
DOE OSTI2023

A rule-free workflow for the automated generation of databases from scientific literature

Abstract In recent times, transformer networks have achieved state-of-the-art performance in a wide range of natural language processing tasks. Here we present a workflow based on the fine-tuning of BERT models for different downstream tasks, which results in the automated extraction of structured information from unstructured natural language in scientific literature. Contrary to existing methods for the automated extraction of structured compound-property relations from similar sources, our workflow does not rely on the definition of intricate grammar rules. Hence, it can be adapted to a new task without requiring extensive implementation efforts and knowledge. We test our data-extraction workflow by automatically generating a database for Curie temperatures and one for band gaps. These are then compared with manually curated datasets and with those obtained with a state-of-the-art rule-based method. Furthermore, in order to showcase the practical utility of the automatically extracted data in a material-design workflow, we employ them to construct machine-learning models to predict Curie temperatures and band gaps. In general, we find that, although more noisy, automatically extracted datasets can grow fast in volume and that such volume partially compensates for the inaccuracy in downstream tasks.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Strong and recyclable bio-derived poly(ester amide) hot-melt adhesive

Bio-based adhesives offer inherent advantages over conventional petrochemical-derived systems, including renewable sourcing, reduced environmental impact and potential degradability. However, most bio-based adhesives suffer from poor adhesion strength, limited substrate compatibility and a lack of chemical recyclability. Here, in this work, we present a bio-derived multiblock poly(ester amide) adhesive that leverages microphase segregation between different segments to reconcile mechanical robustness with strong interfacial bonding. Notably, this multiblock architecture is accessed through a one-pot, selective acceptorless dehydrogenative polymerization, obviating the need for multistep synthesis. The materials exhibit excellent adhesion across a range of substrates including metals, glass and wet wood surpassing commercial benchmarks, while also demonstrating thermal stability, tunable mechanical properties and closed-loop chemical recyclability even in the presence of other commodity plastics. Furthermore, the adhesive strength of these materials could be tuned for various potential applications through control over the chemical composition of the polymer. By integrating renewable feedstocks, high-performance functionality and efficient chemical circularity within a single platform, this work provides a viable pathway toward more sustainable adhesive technologies and contributes to advancing circular materials manufacturing.

09 BIOMASS FUELS↗
DOE OSTI2024

Numerical studies of collinear laser-assisted injection from a foil for plasma wakefield accelerators

We present a laser-assisted electron injection scheme for beam-driven plasma wakefield acceleration. The laser is collinear with the driver and triggers the injection of hot electrons into the plasma wake by interaction with a thin solid target. We present a baseline case using the AWAKE Run 2 parameters and then perform variations on key parameters to explore the scheme. It is found that the trapped witness electron charge may be tuned by altering laser parameters, with a strong dependence on the phase of the wake upon injection. Normalized emittance settles at the order of micrometres and varies with witness charge. The scheme is robust to misalignment, with a 1/10th plasma skin-depth offset ( 20 μ m for the AWAKE case) having a negligible effect on the final beam. The final beam quality is better than similar existing schemes, and several avenues for further optimization are indicated. The constraints on the AWAKE experiment are very specific, but the general principles of this mechanism can be applied to future beam-driven plasma wakefield accelerator experiments. Published by the American Physical Society 2024

Physics↗
DOE OSTI2021

An Iterative Response-Surface-Based Approach for Chance-Constrained AC Optimal Power Flow Considering Dependent Uncertainty

A modern power system is characterized by a stochastic variation of the loads and an increasing penetration of renewable energy generation, which results in large uncertainties in its states. These uncertainties bring formidable challenges to the power system planning and operation process. To address these challenges, we propose a cost-effective, iterative response-surface-based approach for the chance-constrained AC optimal power-flow problem that aims to ensure the secure operation of the power systems considering dependent uncertainties. Starting from a stochastic-sampling-based framework, we first utilize the copula theory to simulate the dependence among multivariate uncertain inputs. Then, to reduce the prohibitive computational time required in the traditional Monte-Carlo method, we propose, instead of using the original complicated power-system model, to rely on a polynomial-chaos-based response surface. This response surface allows us to efficiently evaluate the time-consuming power-system model at arbitrary distributed sampled values with a negligible computational cost. This further enables us to efficiently conduct an online stochastic testing for the system states that not only screens out the statistical active constraints, but also assists in a better design of the tightened bounds without using any Gaussian or symmetric assumption. Finally, an iterative procedure is executed to fine-tune the optimal solution that better satisfies a predefined probability. The simulations conducted in multiple test systems demonstrate the excellent performance of the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Measurement of the Lund jet plane in hadronic decays of top quarks and W bosons with the ATLAS detector

The Lund jet plane (LJP) is measured for the first time in $t\bar{t}$ events, using 140 $\textrm{fb}^{-1}$ of $\sqrt{s} = 13$ TeV pp collision data collected with the ATLAS detector at the LHC. The LJP is a two-dimensional observable of the sub-structure of hadronic jets that acts as a proxy for the kinematics of parton showers and hadron formation. The observable is constructed from charged particles and is measured for R = 1.0 anti-$k_t$ jets with transverse momentum above 350 GeV containing the full decay products of either a top quark or a daughter W boson. The other top quark in the event is identified from its decay into a b-quark, an electron or a muon and a neutrino. The measurement is corrected for detector effects and compared with a range of Monte Carlo predictions sensitive to different aspects of the hadronic decays of the heavy particles. In the W-boson-initiated jets, all the predictions are incompatible with the measurement. In the top quark initiated jets, disagreement with all predictions is observed in smaller subregions of the plane, and with a subset of the predictions across the fiducial plane. The measurement could be used to improve the tuning of Monte Carlo generators, for better modelling of hadronic decays of heavy quarks and bosons, or to improve the performance of jet taggers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

(Invited) Virtual Analysis of Gas-Diffusion-Electrode CO 2 Electrolyzers

The electrochemical reduction of CO 2 (CO 2 R) to value-added products is an attractive technology for tackling the rising atmospheric CO 2 levels and storing intermittent renewable energy into chemical bonds. Fundamental understanding of CO 2 R has progressed significantly in recent years and is critical in the development of CO 2 R to liquid-fuel electrolyzers, where gas-diffusion electrodes (GDEs) have been shown to be key enabling architectures. Various designs have been proposed and studied in the literature to enhance overall selectivity, rates, and maximize the conversion of CO 2 , the latter of which is only now being recognized as a critical issue. In this respect, there is a need to explore the governing phenomena inherent in these architectures to enable optimization. Mathematical modeling is ideally suited to tackle and explore these multiphysics interactions and provide virtual design analysis. In this talk, we discuss modeling methodologies and physics inherent in these devices and present our recent modeling of GDEs for CO 2 reduction. We specifically examine the impacts of multiphase flow and related phenomena on overall cell performance. We then explore the performance and limitations of various cell designs guided by simulation results and examine potential methods for improving water management and tuning catalyst selectivity including the use of different anion-exchange and bipolar membranes. Finally, we discuss the disparities in local environments between aqueous and GDE devices and propose strategies to reduce the gap in knowledge between the two systems.

42 ENGINEERING↗
DOE OSTI2020

Virtual Analysis of Gas-Diffusion-Electrode CO 2 Electrolyzers

The electrochemical reduction of CO 2 (CO2R) to value-added products is an attractive technology for tackling the rising atmospheric CO 2 levels and storing intermittent renewable energy into chemical bonds. Fundamental understanding of CO 2 R has progressed significantly in recent years and is critical in the development of CO 2 R to liquid-fuel electrolyzers, where gas-diffusion electrodes (GDEs) have been shown to be key enabling architectures. Various designs have been proposed and studied in the literature to enhance overall selectivity, rates, and maximize the conversion of CO 2 , the latter of which is only now being recognized as a critical issue. In this respect, there is a need to explore the governing phenomena inherent in these architectures to enable optimization. Mathematical modeling is ideally suited to tackle and explore these multiphysics interactions and provide virtual design analysis. In this talk, we discuss modeling methodologies and physics inherent in these devices and present our recent modeling of GDEs for CO 2 reduction. Here, we specifically examine the impacts of multiphase flow and related phenomena on overall cell performance. We then explore the performance and limitations of various cell designs guided by simulation results and examine potential methods for improving water management and tuning catalyst selectivity including the use of different anion-exchange and bipolar membranes. Finally, we discuss the disparities in local environments between aqueous and GDE devices and propose strategies to reduce the gap in knowledge between the two systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentials

Accurate molecular property predictions require 3D geometries, which are typically obtained using expensive methods such as density functional theory (DFT). Here, we attempt to obtain molecular geometries by relying solely on machine learning interatomic potential (MLIP) models. To this end, we first curate a large-scale molecular relaxation dataset comprising 3.5 million molecules and 300 million snapshots. Then MLIP pre-trained models are trained with supervised learning to predict energy and forces given 3D molecular structures. Once trained, we show that the pre-trained models can be used in different ways to obtain geometries either explicitly or implicitly. First, it can be used to obtain approximate low-energy 3D geometries via geometry optimization. While these geometries do not consistently reach DFT-level chemical accuracy or convergence, they can still improve downstream performance compared to non-relaxed structures. To mitigate potential biases and enhance downstream predictions, we introduce geometry fine-tuning based on the relaxed 3D geometries. Second, the pre-trained models can be directly fine-tuned for property prediction when ground truth 3D geometries are available. Our results demonstrate that MLIP pre-trained models trained on relaxation data can learn transferable molecular representations to improve downstream molecular property prediction and can provide practically valuable but approximate molecular geometries that benefit property predictions. Our code is publicly available at: https://github.com/divelab/AIRS/.

Fu, Cong [Texas A & M Univ., College Station, TX (↗
DOE OSTI2022

Factors Controlling a Synthetic Aperture Radar (SAR) Derived Root-Zone Soil Moisture Product over The Seward Peninsula of Alaska

Root-zone soil moisture exerts a fundamental control on vegetation, energy balance, and the carbon cycle in Arctic ecosystems, but it is still not well understood in vast, remote, and understudied regions of discontinuous permafrost. The root-zone soil moisture product (30 m resolution) used in this analysis was retrieved from a time-series P-Band (420–440 MHz) synthetic aperture radar (SAR) backscatter observations (August 2017 & October 2017). While similar approaches have been taken to retrieve surface (0 cm to 5 cm) soil moisture from L-Band (1.2 GHz) SAR backscatter, this is one of the first known attempts at reaching the root-zone in permafrost regions. Here, we analyze secondary factors (excluding primary factors, such as precipitation) controlling summer (August) soil moisture at depths of 6 cm, 12 cm, and 20 cm over a 4500 km2 area on the Seward Peninsula of Alaska. Using a random forest model, we quantify the impact of topography, vegetation, and meteorological factors on soil moisture distributions. In developing the random forest model, we explore a variety of feature scales (30 m, 60 m, 90 m, 120 m, 180 m, and 240 m), tune hyperparameters (the structure of individual decision trees making up the ensemble including the number and depth of trees), and perform the final feature selection using cross-validated recursive feature elimination. Results suggest that root-zone soil moisture on the Seward Peninsula is primarily controlled by vegetation at 6 cm, but deeper in the soil column topography and meteorological factors, such as predominant winter wind direction and summer insolation, play a larger role. The random forest model accounts for 40% to 60% of the variation observed (R2 = 0.44 at 6 cm, R2 = 0.52 at 12 cm, R2 = 0.58 at 20 cm). These results indicate that vegetation is the dominant control on soil moisture shallow in the soil column, but the impact of vegetation does not extend to deeper layers retrieved from P-Band SAR backscatter.

Dann, Julian↗
DOE OSTI2023

Classification Analytics of Pu-239 and U-235 Source Signatures Using Gamma Spectral Regions

Machine learning detection methods using gamma signatures from spectral measurements of low-intensity Pu-239 and U-235 sources are studied. NaI detectors located at different distances fromthe source have been used to collect the training and independent testing data sets. The source is introduced via a shielded conduit into the facility where it is surrounded by 21 NaI detectors deployed over 6 x 6 meters area in the formation of two concentric circles and a spiral. The counts in gamma spectral regions associated with these two sources are estimated at 1 second intervals for each NaI detector, and are used as classifier features for detecting the source presence. Eight different classifiers with five basic properties — namely, smooth, non-smooth, statistical, structural, and hyper-parameter tuning — are trained and tested using the background and source measurements collected over multiple experimental runs. While the overall classifier performance improved as detectors closer to the source are used, some identically produced detectors under-performed but differently between two sources. Some classifiers achieved lower training error but their testing error based on independent measurements is higher for both sources. Overall, these results indicate significant over-fitting by these methods, and illustrate the complexity of training and selecting the machine learning methods to solve these detection problems.

Rao, Nageswara↗
DOE OSTI2020

Luminescent Enhancement for Combined Solar and Commercial Agriculture. Final Report

This proposal will develop a new racking / mounting system combined with a new agriculture-tuned luminescent solar panel for low cost high tunnel implementation to produce both crops and electricity for small local farms. Crop trials performed under this specialized panel/racking system can be extended to open field agriculture with standard utility scale racking system in phase II. The US agricultural industry uses about 300 billion KW-hr of energy / year. Of this energy consumption, roughly 20% goes towards food production and 20% goes towards food processing. This proposal will allow farm land to simultaneously generate electricity, food AND provide jobs. Employment is one of the largest hurdles for the introduction of solar into rural areas. The ability to provide agricultural jobs in addition to solar energy from a plot of land will pave the way for greater adoption of photovoltaics in rural areas. A cost effective and highly durable hybrid power generating high tunnel greenhouse will be developed that generates electricity at less than $2.50/watt INSTALLED cost and can be used for crop production of high values crops. The high tunnel will use a new design of Soliculture greenhouse integrated luminescent solar panels to selectively absorb the green portion of the solar spectrum that is not used by plants and convert the light to red light with higher photosynthetic efficiency and to enhance power output of solar cells. Green light is selectively downshifted to red light using luminescent materials that match the photosythetically active region for plants and the efficiency maximum of Si photovoltaic cells. The color tuning associated with converting green light to red light has been shown to facilitate fruit production in commercial greenhouses growing cucumbers, lettuce and herbs.

14 SOLAR ENERGY↗
DOE OSTI2022

Lithiation-Induced Defect Engineering to Promote Oxygen Evolution Reaction

Exploring efficient electrocatalysts for oxygen evolution reaction (OER) is an urgent need to advance the development of sustainable energy conversion. Though defect engineering is considered an effective strategy to regulate catalyst activity for enhanced OER performance, the controllable synthesis of defective oxides electrocatalysts remains challenging. Here, oxygen defects are introduced into NiCo 2 O 4 nanorods by an electrochemical lithiation strategy. By tuning in situ lithiation potentials, the concentration of oxygen defects and the corresponding catalytic activity can be feasibly regulated. In addition, the relationship between the changes in the defect density and electronic structure and the lithiation cut-off voltages is revealed. The results show that NiCo 2 O 4 nanorods undertook intercalation and two-step conversion reaction, in which the lithiation-induced conversion reaction gives rise to a CoO@NiO-based structure with higher defect density and lower oxidation states. As a result, the defective CoO@NiO-based catalyst exhibits exceptional OER activity with an overpotential of 270 mV at 10 mA cm -2 , which is about 74 mV below the pristine nanomaterials. In conclusion, this research proposes a novel strategy to explore high-performance catalysts with structural stability and defect control.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Harnessing large language models’ zero-shot and few-shot learning capabilities for regulatory research

Abstract Large language models (LLMs) are sophisticated AI-driven models trained on vast sources of natural language data. They are adept at generating responses that closely mimic human conversational patterns. One of the most notable examples is OpenAI's ChatGPT, which has been extensively used across diverse sectors. Despite their flexibility, a significant challenge arises as most users must transmit their data to the servers of companies operating these models. Utilizing ChatGPT or similar models online may inadvertently expose sensitive information to the risk of data breaches. Therefore, implementing LLMs that are open source and smaller in scale within a secure local network becomes a crucial step for organizations where ensuring data privacy and protection has the highest priority, such as regulatory agencies. As a feasibility evaluation, we implemented a series of open-source LLMs within a regulatory agency’s local network and assessed their performance on specific tasks involving extracting relevant clinical pharmacology information from regulatory drug labels. Our research shows that some models work well in the context of few- or zero-shot learning, achieving performance comparable, or even better than, neural network models that needed thousands of training samples. One of the models was selected to address a real-world issue of finding intrinsic factors that affect drugs' clinical exposure without any training or fine-tuning. In a dataset of over 700 000 sentences, the model showed a 78.5% accuracy rate. Our work pointed to the possibility of implementing open-source LLMs within a secure local network and using these models to perform various natural language processing tasks when large numbers of training examples are unavailable.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Boosting the Oxygen Evolution Reaction by Tuning the Interfacial Iron Adsorption on Layered Double Hydroxide

Understanding the interaction between ions in the electrolyte and electrode materials plays an important role in optimizing the water electrolysis performance for hydrogen production. Herein, the synergistic effect of iron (Fe) in the electrolyte and interlayer anions within the layered structure on the oxygen evolution reaction (OER) has been investigated by combining material synthesis with controlled structure, multiple characterization techniques, and first-principles calculations. Nickel aluminum layered double hydroxides (NiAl-LDHs) with different interlayer anions (CO 3 2– , Cl – , and Br – ) show similar oxygen evolution activity in the absence of Fe species in the electrolyte. The addition of Fe into the electrolyte results in improved performance for all of the NiAl-LDHs, following the rank LDH-Br > LDH-Cl > LDH-CO 3 , under all of the conditions with varied concentration of Fe. X-ray absorption spectroscopy and identical location electron microscopy analyses show that the LDH structure remains unchanged after the OER activity test, while in situ stationary probe rotating disk electrode inductively coupled plasma mass spectrometry (SPRDE-ICP-MS) measurements show partial dissolution of the intercalating halide ions during cycling, with less dissolution for Br-intercalated materials. Insights from theoretical calculations demonstrate the thermodynamic preference of Br – to remain intercalated in the presence of Fe, while the stronger adsorption of Fe(OH) 3 species on the LDH-Br sample promotes the OER activity. In conclusion, these results provide mechanistic insights into the rational design of active layered materials with an enhanced OER performance for efficient water electrolysis.

58 GEOSCIENCES↗