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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Identification and evolution of active sites in isomorphously substituted Fe-ZSM-5 catalysts for methane dehydroaromatization (MDA)

Methane dehydroaromatization (MDA) enables the catalytic conversion of methane into aromatics, mainly benzene, along with hydrogen, in a single-step process under non-oxidative conditions. In this study, we prepared a series of isomorphously substituted Fe-ZSM-5 catalysts with exclusively isolated or low-oligomerized sites up to 3.2% Fe weight loading by adding ethylenediaminetetraacetic acid (EDTA) to the crystallization gel. These catalysts exhibit no induction/activation period and a significantly higher maximum product yield compared to Fe/ZSM-5 catalysts prepared by the incipient wetness impregnation method and containing extensively aggregated FeOx. This investigation focuses on the evolution of Fe sites during the post-synthetic treatments. Most importantly, we demonstrate the quantitative relation between Fe site isolation and catalytic activity, proving that isolated or low-oligomerized Fe species at the exchange sites formed during post-synthetic retreatment are the active sites for methane activation.

02 PETROLEUM↗
DOE OSTI2021

La 3+ and Y 3+ interactions with the carboxylic acid moiety at the liquid/vapor interface: Identification of binding complexes, charge reversal, and detection limits

Specific interactions of yttrium and lanthanum ions with a fatty acid Langmuir monolayer were investigated using vibrational sum frequency spectroscopy. The trivalent ions were shown to interact with the charged form of the carboxylic acid group from nanomolar concentrations (<300 nM). Analysis of the spectral features from both the symmetric and the asymmetric carboxylate modes reveals the presence of at least three distinct coordination structures linked to specific binding configurations. Although the same species were identified for both La 3+ and Y 3+ , they display a different concentration dependence, highlighting the ion-specificity of the interaction. From the analysis of the response of interfacial water molecules, the reversal of the surface charge, as well as the formation of yttrium hydroxide complexes, were detected upon increasing the amount of salt in solution. The binding interaction and kinetics of absorption are sensitive to the solution pH, showing a distinct ion speciation in the interfacial region when compared to the bulk. Changing the subphase pH or adding a monovalent background electrolyte that promotes deprotonation of the carboxylic acid headgroup could further improve the detection limit of La 3+ and Y 3+ to concentrations < 100 nM. These findings demonstrate that nM concentrations of trace metals contaminants, typically found on monovalent salts, can significantly influence the binding structure and kinetics in Langmuir monolayers.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

ECLEIRS: Exact conservation law embedded identification of reduced states for parameterized nonlinear conservation laws from sparse and noisy data

Multi-query applications such as parameter estimation, uncertainty quantification and design optimization for parameterized partial differential equation (PDE) systems are expensive. While reduced/latent state dynamics approaches for parameterized PDEs offer a viable alternative, these approaches rely on high-quality data and struggle with highly sparse spatiotemporal noisy measurements typically obtained from experiments. Furthermore, there is no guarantee that these models satisfy governing physical conservation laws. In this article, we propose a reduced state dynamics approach, referred to as ECLEIRS, that embeds exact conservation in the solution and flux representation by utilizing a space-time divergence-free neural network formulation. We compare ECLEIRS with other reduced state dynamics approaches, those that do not enforce any physical constraints and those with physics-informed loss functions, for three shock-propagation problems: 1-D advection, 1-D Burgers and 2-D Euler equations. In conclusion, the numerical experiments conducted in this study demonstrate that ECLEIRS provides the most accurate prediction of dynamics for unseen parameters even in the presence of highly sparse and noisy data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Enhancing phase identification in waste-to-energy fly ashes: Role of Raman spectroscopy, background fluorescence, and photobleaching

Waste-to-Energy (WTE) facilities incinerate ~11% (~ 222 Mt) of global solid waste, generating bottom and fly ashes. Landfilling these ashes is costly, and risks releasing contaminants into the environment. Instead, using WTE ashes in secondary industrial applications can circumvent such environmental risks. However, their secondary use is restricted by their inconsistent mineralogy, which may vary due to fluctuating waste composition and combustion conditions. Therefore, there is a need for rapid and reliable monitoring of WTE fly ash mineralogy. Here, we evaluate the employment of Raman spectroscopy for that purpose. Our initial investigation of 12 unique WTE fly ashes resulted in excessive fluorescence, rendering key Raman peaks obscure. To address this issue, we report that a mere 2 minutes of photobleaching can significantly reduce this fluorescence, facilitating the detection of calcite, calcium sulfate, zincite, and carbon – phases previously undetectable in original spectra. Furthermore, these results show the potential of Raman spectroscopy for rapid monitoring of WTE fly ash mineralogy, which could be beneficial in diverting these ashes from landfill.

36 MATERIALS SCIENCE↗
DOE OSTI2024

High resolution identification and quantification of diffuse deep groundwater discharge in mountain rivers using continuous boat-mounted helium measurements

Discharge of deeply sourced groundwater to streams is difficult to locate and quantify, particularly where both discrete and diffuse discharge points exist, but diffuse discharge is one of the primary controls on solute budgets in mountainous watersheds. The noble gas helium is a unique identifier of deep groundwater discharge because groundwater with long residence times is commonly enriched in helium. In this study, a portable mass spectrometer was used to measure longitudinal variation in dissolved helium concentrations in two mountainous rivers at high spatial resolution not feasible with traditional sampling techniques. Helium profiles were then simulated using a mass-balance model to quantify longitudinal variation in groundwater discharge to the receiving rivers. Results indicate helium concentrations were enriched by multiple orders of magnitude above atmospheric equilibrium in both rivers and that this persisted for up to 18 km below observed pulse inputs in the Colorado River. Helium mass-balance models match observed longitudinal patterns with the exception of sharp initial increases in helium observed in the rivers. Increased longitudinal groundwater discharge rates correspond to mapped geologic structures in both watersheds that likely transport deep geothermal water. Models show variable sensitivity to spatial assignment of input variables representing the groundwater source, illustrating the importance of collecting data from discrete groundwater discharges where possible. The methodology shows promise for field experiments designed to assess air–water exchange rates and to quantify total groundwater discharge from a combination of discrete and diffuse sources.

Deep groundwater↗
DOE OSTI2025

Identification of uranium oxidation states using oxygen K-edge scanning transmission X-ray microscopy

The field of nuclear forensics is growing in importance, and the increasing capabilities at synchrotron radiation light sources enable non-destructive characterization of oxide particles with better spatial, compositional, and oxidation state speciation resolution than ever before. Here, uranium oxide particles derived from multiple wet chemical processing methods were examined using a scanning transmission X-ray microscope (STXM), and a weakly-supervised method was developed to automatically analyze the collected data. Multiple uranium oxidation states were observed and quantified within and between samples, yielding information about differences between particles produced via the various processing routes.

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

Battery aging mode identification across NMC compositions and designs using machine learning

A comprehensive understanding of lithium-ion battery (LiB) lifespan is the key to designing durable batteries and optimizing use protocols. Although battery lifetime prediction methods are flourishing, diagnosis of the root causes of aging and degradation have not yet been well developed nor studied for a broad mixture of designs and use cases. Here, we create a machine-learning (ML)-based framework that distinguishes aging modes using multiple electrochemical signatures recorded cycle-by-cycle. The predominant aging behaviors include a combination of loss of active materials in cathode (LAMPE) and a loss of Li inventory (LLI) in Li plating or solid electrolyte interphase (SEI) formation, manifested from 44 batteries representing two cathode chemistries, two electrode loadings, and five charging rates. Here, the aging mode classification accuracy is 86% using features within the first 50 cycles and increases to 88% beyond 225 cycles. The same features can quantify the percentage of end-of-life LAMPE with only 4.3% of error.

25 ENERGY STORAGE↗
DOE OSTI2022

Identification of a pharmacological approach to reduce ACE2 expression and development of an in vitro COVID-19 viral entry model

Because of rapid emergence and circulation of the SARS-CoV-2 variants, especially Omicron which shows increased transmissibility and resistant to antibodies, there is an urgent need to develop novel therapeutic drugs to treat COVID-19. In this study we developed an in vitro cellular model to explore the regulation of ACE2 expression and its correlation with ACE2-mediated viral entry. We examined ACE2 expression in a variety of human cell lines, some of which are commonly used to study SARS-CoV-2. Using the developed model, we identified a number of inhibitors which reduced ACE2 protein expression. The greatest reduction of ACE2 expression was observed when CK869, an inhibitor of the actin-related protein 2/3 (ARP2/3) complex, was combined with 5-(N-ethyl-N-isopropyl)-amiloride (EIPA), an inhibitor of sodium-hydrogen exchangers (NHEs), after treatment for 24 h. Using pseudotyped lentivirus expressing the SARS-CoV-2 full-length spike protein, we found that ACE2-dependent viral entry was inhibited in CK869 + EIPA-treated Calu-3 and MDA-MB-468 cells. This study provides an in vitro model that can be used for the screening of novel therapeutic candidates that may be warranted for further pre-clinical and clinical studies on COVID-19 countermeasures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Identification of drug repurposing candidates for amyotrophic lateral sclerosis using electronic health records: a retrospective cohort study

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease with a life expectancy of only 3–5 years and few approved treatments. To identify drug repurposing candidates for the treatment of ALS, we analysed the electronic health records (EHRs) of a large cohort of military veterans with ALS. We analysed the EHRs of individuals in the US Veterans Health Administration (VHA) database who were diagnosed with ALS between Jan 1, 2009 and Dec 31, 2019 to assess medication effects. Individuals without recorded prescriptions after the date of diagnosis were excluded. Two sets of criteria were applied to ascertain exposure. Exposure criteria A were met if the dispense date or the end date of the medication was within 12 months of ALS diagnosis and the end date was at least 6 months after the dispense date. Exposure criteria B were met if there were at least two dispenses within 6 months before diagnosis and 12 months after diagnosis. Propensity score-matched control groups were generated on the basis of confounders included in the EHR, with methodology of potential outcomes used to infer treatment effects. The primary outcome was death. A standard Cox proportional hazards analysis was done to assess association with survival. Survival was defined as the time from diagnosis date recorded in the EHR to death reported in the Department for Veterans Affairs Vital Status File. Follow-up survival time was censored on Dec 31, 2020, for those alive on this date. Downstream protein targets of drugs with clinically significant effects were analysed using the protein–protein interaction networks-based algorithm PathFX. The EHRs of 11 003 individuals with ALS in the VHA database were appropriate for analysis. 162 medications with treatment groups of 30 or more individuals were identified. Among these 162 medications, 27 were associated with statistically significant changes (≥0·1) in the hazard ratio (HR) for death. 18 of the medications were associated with a reduced HR for death (prolonged survival), and nine were associated with an increased HR for death (reduced survival). Drugs associated with reduced HR included HMG-CoA reductase inhibitors (simvastatin, pravastatin, lovastatin, and atorvastatin), PDE5 inhibitors (vardenafil and sildenafil), and α-adrenergic antagonists (tamsulosin and terazosin). The medications associated with an increased HR were drugs used either in the management of clinical features of ALS associated with poor outcomes or in end-of-life care. PathFx analysis identified a complex of proteins interacting with several of the identified drugs. To our knowledge, this analysis is the largest EHR-based study for identifying drug repurposing candidates for ALS. We identified several drugs that warrant further assessment as therapeutic options in ALS, as well as a protein network complex that might serve as a therapeutic target for ALS.

Reimer, Richard J. [Stanford Univ., CA (United Sta↗
DOE OSTI2026

A foundation model for non-destructive defect identification from vibrational spectra

Defects are ubiquitous in solids and strongly influence materials’ functional properties. However, non-destructive characterization and quantification of defects, especially when multiple types coexist, remain a long-standing challenge. Here, we introduce DefectNet, a foundation machine learning model that predicts the chemical identity and concentration of substitutional point defects with multiple coexisting elements directly from vibrational spectra, specifically phonon density-of-states (PDoS). Trained on over 16,000 simulated spectra from 2,000 semiconductors, DefectNet employs a tailored attention mechanism to identify up to six distinct defect elements at concentrations ranging from 0.2% to 25%. The model generalizes well to unseen crystals across 56 elements and can be fine-tuned on experimental data. Validation using inelastic scattering measurements of SiGe alloys and MgB 2 superconductor demonstrates its accuracy and transferability. Furthermore, our work establishes vibrational spectroscopy as a viable, non-destructive probe for bulk point defect quantification, and highlights the promise of foundation models in data-driven defect engineering.

artificial intelligence↗