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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

AI-powered topic modeling: comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women

Topic modeling is a crucial technique in natural language processing (NLP), enabling the extraction of latent themes from large text corpora. Traditional topic modeling, such as Latent Dirichlet Allocation (LDA), faces limitations in capturing the semantic relationships in the text document although it has been widely applied in text mining. BERTopic, created in 2022, leveraged advances in deep learning and can capture the contextual relationships between words. In this work, we integrated Artificial Intelligence (AI) modules to LDA and BERTopic and provided a comprehensive comparison on the analysis of prescription opioid-related cardiovascular risks in women. Opioid use can increase the risk of cardiovascular problems in women such as arrhythmia, hypotension etc. 1,837 abstracts were retrieved and downloaded from PubMed as of April 2024 using three Medical Subject Headings (MeSH) words: “opioid,” “cardiovascular,” and “women.” Machine Learning of Language Toolkit (MALLET) was employed for the implementation of LDA. BioBERT was used for document embedding in BERTopic. Eighteen was selected as the optimal topic number for MALLET and 23 for BERTopic. ChatGPT-4-Turbo was integrated to interpret and compare the results. The short descriptions created by ChatGPT for each topic from LDA and BERTopic were highly correlated, and the performance accuracies of LDA and BERTopic were similar as determined by expert manual reviews of the abstracts grouped by their predominant topics. The results of the t-SNE (t-distributed Stochastic Neighbor Embedding) plots showed that the clusters created from BERTopic were more compact and well-separated, representing improved coherence and distinctiveness between the topics. Our findings indicated that AI algorithms could augment both traditional and contemporary topic modeling techniques. In addition, BERTopic has the connection port for ChatGPT-4-Turbo or other large language models in its algorithm for automatic interpretation, while with LDA interpretation must be manually, and needs special procedures for data pre-processing and stop words exclusion. Therefore, while LDA remains valuable for large-scale text analysis with resource constraints, AI-assisted BERTopic offers significant advantages in providing the enhanced interpretability and the improved semantic coherence for extracting valuable insights from textual data.

Research & Experimental Medicine↗
DOE OSTI2024

The brine of the times

New processes directly extract lithium from dilute sources. The accelerating global push to reduce carbon emissions is driving substantial demand for clean energy technologies and the materials on which they depend. Nations are vying for critical minerals, such as lithium, cobalt, and rare earth elements), to produce electric vehicles, energy storage devices, and other low-carbon technologies. Tremendous attention focuses on lithium, which is primarily sourced from either hard rock called spodumene or natural brines—solutions containing lithium and other salts found in continental deposits, for example. However, mining lithium often involves energy-intensive, laborious processes. On pages 1438 and 1444 of this issue, Li et al. and Song et al., respectively, report efficient ways to extract lithium from dilute brine. In conclusion, these advances open up opportunities for sustainable extraction from unexploited dilute brines.

Darling, Seth B.↗
DOE OSTI2024

Three-chamber electrochemical reactor for selective lithium extraction from brine

Efficient lithium recovery from geothermal brines is crucial for the battery industry. Current electrochemical separation methods struggle with the simultaneous presence of Na + , K + , Mg 2+ , and Ca 2+ because these cations are similar to Li + , making it challenging to separate effectively. We address these challenges with a three-chamber reactor featuring a polymer porous solid electrolyte in the middle layer. This design improves the transference number of Li + (t Li+ ) by 2.1 times compared to the two-chamber reactor and also reduces the chlorine evolution reaction, a common side reaction in electrochemical lithium extraction, to only 6.4% in Faradaic Efficiency. Employing a lithium-ion conductive glass ceramic (LICGC) membrane, the reactor achieved high t Li+ of 97.5% in LiOH production from simulated brine, while the concentrations of Na + K + , Mg 2+ , and Ca 2+ are below the detection limit. Electrochemical experiments and surface analysis elucidated the cation transport mechanism, highlighting the impact of Na + on Li + migration at the LICGC interface.

Science & Technology - Other Topics↗
DOE OSTI2023

Volatile Fatty Acid Recovery from Arrested Anaerobic Digestion for the Production of Sustainable Aviation Fuel: A Review

The process of producing sustainable aviation fuel (SAF) from organic waste involves the use of volatile fatty acids (VFAs) as intermediates that are obtained via arrested anaerobic digestion (AAD) and VFA recovery. This recovery process often requires several steps, including dewatering, filtration, extraction, and purification. The recovery of VFAs is crucial for their upgrading and can pose a challenge in the production of SAF from organic waste due to high costs and compatibility issues. This review discusses various dewatering methods, including centrifuges, belt filter presses, and screw presses, and explores conditioning technologies that can improve dewatering performance. It also introduces filtration technologies, with a focus on dynamic filtration, which shows promise in addressing the issue of membrane fouling. Additionally, the review describes extraction technologies such as electrodialysis, adsorption, and liquid–liquid extraction (LLE). By providing insights into these different techniques, the review aims to contribute to the development of an integrated VFA recovery process with low carbon footprint.

09 BIOMASS FUELS↗
DOE OSTI2020

Antimicrobial Properties of Corn Stover Lignin Fractions Derived from Catalytic Transfer Hydrogenolysis in Supercritical Ethanol with a Ru/C Catalyst

Converting lignin to value-added products at high yields provides an avenue for making ethanol biorefineries more profitable while reducing the carbon footprint of products generally derived from petroleum. In this study, corn stover lignin was depolymerized by catalytic transfer hydrogenolysis (CTH) in supercritical ethanol with a Ru/C catalyst. The lignin-derived bio-oil was then sequentially extracted utilizing hexane, petroleum ether, chloroform, and ethyl acetate as solvents in order of less polar to polar, and the subsequent bio-oils were characterized using GPC, GC/MS, and HSQC NMR. In this work, results show that the monomers in the bio-oil fractions contained primarily alkylated phenols, hydrogenated hydroxycinnamic acid derivatives, syringol and guaiacol-type lignins created from reductive cleavages of ether linkages, which were sequentially extracted into groups depending on the solvent polarity. The antimicrobial properties of the bio-oils were screened against Gram-positive (Bacillus subtilis, Lactobacillus amylovorus, and Staphylococcus epidermidis) and Gram-negative (Escherichia coli) bacteria and yeast (Saccharomyces cerevisiae) by examining microbial growth inhibition. Results show that CTH-derived bio-oils inhibited all tested organisms at concentrations less than 3 mg/mL. Total monomer concentration and the presence of specific monomers (i.e., syringyl propane) showed correlations to antimicrobial activity, likely due to cell death or membrane damage. This study provides insights into using sequential extraction to fractionate lignin-derived compounds and correlations between the properties of the extracted compounds and their antimicrobial activity.

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

Cation-Dependent Multielectron Kinetics of Metal Oxide Splitting

We report direct electrolytic extraction of metals from metal oxides is a promising process for the sustainable production of metals. In this work, we elucidate the inherent thermodynamic driving forces behind the reduction of metal oxides to metals (M-OER). It is shown that the thermodynamics of M-OER can be systematically tuned via the interactions of oxygen with the participating metal cations as a function of metal–oxygen covalency, oxygen–oxygen covalency, and metal–oxygen ionicity. We screen both group 1 elements and metals that are able to exist in the +2 oxidation state for M-OER thermodynamics. Li, Fe, and Co are identified as having low thermodynamic overpotentials for electrolytic extraction from their metal oxides due to interactions between oxygen and these metals being neither too strong (covalent) nor too weak (ionic). We further show that the bulk formation energies are predictive of M-OER reaction energetics on surfaces by developing unified design principles for tuning the thermodynamics of these reduction reactions both in bulk oxides and on surfaces.

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

A Generalized Transformer-Based Pulse Detection Algorithm

Pulse-like signals are ubiquitous in the field of single molecule analysis, e.g., electrical or optical pulses caused by analyte translocations in nanopores. The primary challenge in processing pulse-like signals is to capture the pulses in noisy backgrounds, but current methods are subjectively based on a user-defined threshold for pulse recognition. Here, we propose a generalized machine-learning based method, named pulse detection transformer (PETR), for pulse detection. PETR determines the start and end time points of individual pulses, thereby singling out pulse segments in a time-sequential trace. It is objective without needing to specify any threshold. It provides a generalized interface for downstream algorithms for specific application scenarios. PETR is validated using both simulated and experimental nanopore translocation data. It returns a competitive performance in detecting pulses through assessing them with several standard metrics. Finally, the generalization nature of the PETR output is demonstrated using two representative algorithms for feature extraction.

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

Uranium In Situ Electrolytic Deposition with a Reusable Functional Graphene-Foam Electrode

Nuclear fission produces 400 GWe which represents 11% of the global electricity output. Uranium is the essential element as both fission fuel and radioactive waste. Therefore, the recovery of uranium is of great importance. In this study, an in situ electrolytic deposition method to extract uranium from aqueous solution is reported. A functionalized reduced graphene oxide foam (3D-FrGOF) is used as the working electrode, which acts as both a hydrogen evolution reaction catalyst and a uranium deposition substrate. The specific electrolytic deposition capacity for U(VI) ions with the 3D-FrGOF is 4560 mg g –1 without reaching saturation, and the Coulombic efficiency can reach 54%. Moreover, reduction of the uranium concentration in spiked seawater from 3 ppm to 19.9 ppb is achieved, which is lower than the US Environmental Protection Agency uranium limits for drinking water (30 ppb). Furthermore, the collection electrode can be efficiently regenerated and recycled at least nine times without much efficiency fading, by ejecting into 2000 ppm concentrated uranium solution in a second bath with reverse voltage bias. All these findings open new opportunities in using free-standing 3D-FrGOF electrode as an advanced separation technique for water treatment.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A machine learning pipeline for membrane segmentation of cryo-electron tomograms

We describe how to use several machine learning techniques organized in a learning pipeline to segment and identify cell membrane structures from cryo electron tomograms. These tomograms are difficult to analyze with traditional segmentation tools. The learning pipeline in our approach starts from supervised learning via a special convolutional neural network trained with simulated data. It continues with semi-supervised reinforcement learning and/or a region merging technique that tries to piece together disconnected components belonging to the same membrane structure. A parametric or non-parametric fitting procedure is then used to enhance the segmentation results and quantify uncertainties in the fitting. Domain knowledge is used in generating the training data for the neural network and in guiding the fitting procedure through the use of appropriately chosen priors and constraints. We demonstrate that the approach proposed here works well for extracting membrane surfaces in two real tomogram datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

EMPDF : inferring the Milky Way mass with data-driven distribution function in phase space

We introduce the emPDF (empirical distribution function), a novel dynamical modelling method that infers the gravitational potential from kinematic tracers with optimal statistical efficiency under the minimal assumption of steady state. emPDF determines the best-fitting potential by maximizing the similarity between instantaneous kinematics and the time-averaged phase-space distribution function (DF), which is empirically constructed from observation upon the theoretical foundation of oPDF (Han et al. 2016). This approach eliminates the need for presumed functional forms of DFs or orbit libraries required by conventional DF- or orbit-based methods. emPDF stands out for its flexibility, efficiency, and capability in handling observational effects, making it preferable to the popular Jeans equation or other minimal assumption methods, especially for the Milky Way (MW) outer halo where tracers often have limited sample size and poor data quality. We apply emPDF to infer the MW mass profile using Gaia DR3 data of satellite galaxies and globular clusters, obtaining enclosed masses of M (,r) = 26±8, 46±8, 90±13⁠, and 149±40 x 10 10 M ⊙ at r = 30, 50, 100⁠, and 200 kpc, respectively. These are consistent with the updated constraints from simulation-informed DF fitting (Li et al. 2020). While the simulation-informed DF offers superior precision owing to the additional information extracted from simulations, emPDF is independent of such supplementary knowledge and applicable to general tracer populations. emPDF is currently implemented for tracers with complete 6D kinematics within spherical potentials, but it can potentially be extended to address more general problems.

Astrophysics of Galaxies (astro-ph.GA)↗
DOE OSTI2024

Evolution of the two-neutron configuration from 11 Li to 13 Li

In this work we investigate the two-neutron decay of 13 Li and of the excited states of 11 Li populated via one-proton removal from 14 Be and 12 Be, respectively. A phenomenological model is used to describe the decay of 11 Li and 13 Li. While the first one displays important sequential components, the second one appears dominated by the direct two-neutron decay. A microscopic three-body model is used to extract information on the spatial configuration of the emitted neutrons before the decay and shows that the average distance between the neutrons increases going from 11 Li to 13 Li.

Dineutron decay↗
DOE OSTI2021

Lithium Metal as an Anode in Electrochemical Energy Storage

The drastic environmental and economic costs of global warming have driven the necessity to reduce, if not eliminate, a dependency on oil and gas for energy applications. Alternative clean energy reliance has been initiated as a global investment for a more sustainable future. Part of this transitional effort is focused on energy storage via a rechargeable battery. High energy density storage is necessary for utilizing non-dispatchable green energy technologies, as well in the electric transition of our vehicle fleet. Further, understanding methods of extracting, manufacturing, and recycling lithium is necessary to lessen environmental impact. Lithium metal is often considered the “Holy Grail” of battery technology, due to its high theoretical capacity (3,860 mAh/g) and low electrochemical potential (-3.04 V vs. SHE). However, parasitic reactions lead to rapid and dangerous battery failure. Therefore, determining methods of stabilizing lithium metal is paramount for the transition to a greener future.

25 ENERGY STORAGE↗
DOE OSTI2022

Functionalized Mesoporous Carbon and Silica for Effective Recovery of Rare Earth Elements from Magnet Scrap

The dependence on international supplies of rare earth elements (REEs) have prompted the US to explore alternative sources and sustainable technologies for domestic REE production. One potential source is the secondary electronic and industrial wastes at the Department of Defense (DoD) complexes and across the US. These hazardous wastes require high costs for the maintenance and remediation, while their high-REE contents offer unique opportunities for REE recovery. However, current hydrometallurgical technologies for REE extraction are energy intensive and costly, with low REE recovery rate and the generation of toxic wastes. Thus, it is beneficial to develop environmentally friendly technologies for REE extraction and processing from the secondary wastes. This project aims to develop novel functionalized mesoporous carbon fiber (MCF) and silica for effective recovery of REEs from end-of-life NdFeB magnet scrap.

36 MATERIALS SCIENCE↗