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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Identification of surface urban heat versus cool islands for arid cities depends on the choice of urban and rural definitions

The urban heat island (UHI) effect in arid cities can be small or even negative, the latter known as the urban cool island (UCI) effect. Differences in defining urban and rural areas can introduce uncertainties in detecting UHI or UCI, especially when the UHI signal is small. Here, we compared the surface UHI intensity (SUHII) estimated by a dozen different methods (with multiple urban and/or rural definitions) across 104 arid cities globally, providing a comprehensive evaluation of the uncertainty in SUHII estimates. Results show that the absolute difference in annual average SUHII (ΔSUHII) among methods exceeded 1°C in about half of the arid cities during both daytime and nighttime. Further, the overall annual mean ΔSUHII for all arid cities was 1.35°C during daytime and 1.03 °C at night. The uncertainty arising from simultaneous variations in urban and rural definitions was generally higher than that resulting from their individual changes. It was observed that, with varying definitions of urban and rural areas, nearly 50% of arid cities experienced a sign reversal in daytime SUHII estimates, while approximately 15% exhibited a sign reversal in nighttime SUHII. Variations in urban-rural differences in surface properties, such as vegetation index and albedo, due to differing urban and rural definitions, contributed strongly to the observed SUHII uncertainties. Overall, our results offer new insights into the ongoing debate on heat and cold islands in arid cities, emphasizing a critical need to standardize SUHII estimation frameworks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Spectral line identification from a photoionised silicon plasma in emission

Next-generation X-ray satellite telescopes such as XRISM, NewAthena and Lynx will enable observations of exotic astrophysical sources at unprecedented spectral and spatial resolution. Proper interpretation of these data demands that the accuracy of the models is at least within the uncertainty of the observations. One set of quantities that might not currently meet this requirement is transition energies of various astrophysically relevant ions. Current databases are populated with many untested theoretical calculations. Accurate laboratory benchmarks are required to better understand the coming data. We obtained laboratory spectra of X-ray lines from a silicon plasma at an average spectral resolving power of ∼7500 with a spherically bent crystal spectrometer on the Z facility at Sandia National Laboratories. Many of the lines in the data are measured here for the first time. We report measurements of 53 transitions originating from the K-shells of He-like to B-like silicon in the energy range between ∼1795 and 1880 eV (6.6–6.9 Å). The lines were identified by qualitative comparison against a full synthetic spectrum calculated with ATOMIC. The average fractional uncertainty (uncertainty/energy) for all reported lines is ∼5.4 × 10 −5 . We compare the measured quantities against transition energies calculated with RATS and FAC as well as those reported in the NIST ASD and XSTAR’s uaDB. Average absolute differences relative to experimentally measured values are 0.20, 0.32, 0.17 and 0.38 eV, respectively. All calculations/databases show good agreement with the experimental values; NIST ASD shows the closest match overall.

astrophysical plasmas↗
DOE OSTI2024

Identification of Potent and Selective Inhibitors of Acanthamoeba : Structural Insights into Sterol 14α-Demethylase as a Key Drug Target

Acanthamoeba are free-living pathogenic protozoa that cause blinding keratitis, disseminated infection, and granulomatous amebic encephalitis, which is generally fatal. The development of efficient and safe drugs is a critical unmet need. Acanthamoeba sterol 14α-demethylase (CYP51) is an essential enzyme of the sterol biosynthetic pathway. Repurposing antifungal azoles for amoebic infections has been reported, but their inhibitory effects on Acanthamoeba CYP51 enzymatic activity have not been studied. Here, we report catalytic properties, inhibition, and structural characterization of CYP51 from Acanthamoeba castellanii. The enzyme displays a 100-fold substrate preference for obtusifoliol over lanosterol, supporting the plant-like cycloartenol-based pathway in the pathogen. The strongest inhibition was observed with voriconazole (1 h IC 50 0.45 μM), VT1598 (0.25 μM), and VT1161 (0.20 μM). The crystal structures of A. castellanii CYP51 with bound VT1161 (2.24 Å) and without an inhibitor (1.95 Å), presented here, can be used in the development of azole-based scaffolds to achieve optimal amoebicidal effectiveness.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Identification of the Elusive Methyl-Loss Channel in the Crossed Molecular Beam Study of Gas-Phase Reaction of Dicarbon Molecules (C 2 ; X 1 Σ g + /a 3 Π u ) with 2-Methyl-1,3-butadiene (C 5 H 8 ; X 1 A')

The crossed molecular beams technique was utilized to explore the reaction of dicarbon C 2 (X 1 Σ g + /a 3 Π u ) with 2-methyl-1,3-butadiene (isoprene, CH 2 C(CH 3 )CHCH 2 ; X 1 A') at a collision energy of 28 ± 1 kJ mol⁻¹ using a supersonic dicarbon beam generated via photolysis (248 nm) of helium-seeded tetrachloroethylene (C 2 Cl 4 ). Here, experimental data combined with previous ab initio calculations provide evidence of the detection of the hitherto elusive methyl elimination channels leading to acyclic resonantly stabilized hexatetraenyl radicals: 1,2,4,5-hexatetraen-3-yl (CH 2 CC•CHCCH 2 ) and/or 1,3,4,5-hexatetraen-3-yl (CH 2 CHC•CCCH 2 ). These pathways are exclusive to the singlet potential energy surface, with the reaction initiated by the barrierless addition of dicarbon to one of the carbon-carbon double bonds in the diene. In combustion systems, both hexatetraenyl radicals can isomerize to the phenyl radical (C 6 H 5 ) through a hydrogen atom assisted isomerization – the crucial reaction intermediate and molecular mass growth species step toward the formation of polycyclic aromatic hydrocarbons (PAHs) and soot.

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

Identification of Carbonyl Species on Palladium Supported on Ceria in Complex Microenvironments

Herein, we present a systematic comparison between Pd carbonyl (Pd-CO) species, specifically over Pd/CeO 2 based catalysts, observed during isothermal adsorption and in several prototypical catalytic reactions to identify and understand CO adsorption on palladium-ceria based catalysts. Pd-CO is observed via DRIFTS to probe the gas-solid conditions, while ATR-IR is used to probe the affinity of Pd-CO under more complex solvated gas-solid-liquid conditions to discern the influence of the microenvironments for carbonyl adsorption. Here, we explore the presence of Pd-CO under several reactive environments, including CO adsorption, CO 2 + H 2 , CO + H 2 , CH 4 + CO 2 and CO under gas-solid-liquid media, highlighting reactions with notable Pd-CO formation. The differences between palladium carbonyls and carbonate species show that carbonyl species are much more affected via a shifting of the peak position than carbonates, which remain static irrespective of the immediate chemical environment. By following the rate of CO accumulation via K-M mode DRIFTS, we observe migration from linear, 2095 cm -1 , to bridge site, 1978 cm -1 , as a function of time under a static CO atmosphere. With the use of DFT, we discerned changes in Pd-carbonyl stretches due to both coverage effects of CO under simulated reaction conditions and temperature effects. Regardless of whether CO is formed as an intermediate or a reactant, the competitive adsorption of *H and *CO affects the binding strength of *CO at all temperatures, with low temperature favoring atop binding and high temperature favoring the more stable FCC Pd-CO site.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Spectroscopic Identification of Carbamate Formation and Synergistic Binding in Amide–CO$^{–}_{2}$ Complexes

Carbamate formation is an elementary step that governs nitrogencentered nucleophilic CO 2 capture across diverse environments, yet a direct, structure-specific understanding has been lacking. Here, we report the first gas-phase characterization of closed-shell carbamate formation by deprotonated amides, using cryogenic ion trap vibrational spectroscopy combined with quantum chemical calculations. Reactions of deprotonated benzamide and isophthalamide anions with CO 2 form carbamate species, with the amide nitrogen serving as the nucleophilic site. Diagnostic, strongly red-shifted antisymmetric CO 2 stretching vibrational bands, supported by a bonding analysis, establish chemisorption with substantial charge transfer. In the multiamide system, an intramolecular N−H···O hydrogen bond provides synergistic stabilization, correlating with larger red shifts and more exothermic binding. These structure-assigned benchmarks provide molecular-level insights into the binding motif, charge redistribution, and hydrogen bond-mediated stabilization of amide carbamates, which aid the characterization of carbamate formation in condensed phase.

Amides↗
DOE OSTI2025

Anisotropic Raman Scattering and Lattice Orientation Identification of 2M-WS 2

Anisotropic materials with low symmetries hold significant promise for next-generation electronic and quantum devices. 2M-WS 2 , which is a candidate for topological superconductivity, has garnered considerable interest. However, a comprehensive understanding of how its anisotropic features contribute to unconventional superconductivity, along with a simple, reliable method to identify its crystal orientation, remains elusive. Here, we combine theoretical and experimental approaches to investigate angle- and polarization-dependent anisotropic Raman modes of 2M-WS 2 . Through first-principles calculations, we predict and analyze the phonon dispersion and lattice vibrations of all Raman modes in 2M-WS 2 . We establish a direct correlation between their anisotropic Raman spectra and high-resolution transmission electron microscopy images. Finally, we demonstrate that anisotropic Raman spectroscopy can accurately determine the crystal orientation and twist angle between two stacked 2M-WS 2 layers. Furthermore, our findings provide insights into the electron–phonon coupling and anisotropic properties of 2M-WS 2 , paving the way for the use of anisotropic materials in advanced electronic and quantum devices.

2M-WS2↗
DOE OSTI2025

Machine Learning-Guided Identification of PET Hydrolases from Natural Diversity

The enzymatic depolymerization of poly(ethylene terephthalate) (PET) is emerging as a leading chemical recycling technology for waste polyester. As part of this endeavor, new candidate enzymes identified from natural diversity can serve as useful starting points for enzyme evolution and engineering. In this study, we improved upon HMM searches by applying an iterative machine learning strategy to identify 400 putative PET-degrading enzymes (PET hydrolases) from naturally occurring homologs. Using high-throughput (HTP) experimental techniques, we successfully expressed and purified >200 enzyme candidates and assayed them for PET hydrolysis activity as a function of pH, temperature, and substrate crystallinity. From this library, we discovered 91 previously unknown PET hydrolases, 35 of which retain activity at pH 4.5 on crystalline material, which are conditions relevant to developing more efficient commercial processes. Notably, four enzymes showed equal to or higher activity than LCC-ICCG, a benchmark PET hydrolase, at this challenging condition in our screening assay, and 11 of which have pH optima <7. Using these data, we identified regions of PETases statistically correlated to activity at lower pH. We additionally investigated the effect of condition-specific activity data on trained machine learning predictors and found a precision (putative hit rate) improvement of up to 30% compared to a Hidden Markov Model alone. Our findings show that by pointing enzyme discovery toward conditions of interest with multiple rounds of experimental and machine learning, we can discover large sets of active enzymes and explore factors associated with activity at those conditions.

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

Identification of In Situ Generated High-Spin Fe(II)-N 4 Active Sites for Acidic Oxygen Reduction Reaction via Operando 57 Fe Mössbauer Spectroscopy

Iron-nitrogen-carbon (FeNC) catalysts are considered among the most promising alternative to Pt catalysts in acidic oxygen reduction reaction (ORR), yet the geometric and electronic structures of the true active site under reaction conditions have not been clearly elucidated. Herein, we synthesized a representative FeNC catalyst by pyrolyzing Fe 3+ absorbed on ZIF-8-derived N-doped carbon at a mild temperature under the H 2 /Ar atmosphere, where a formation mechanism of FeN 4 sites through a Zn-mediated Fe nanoparticle atomization process was proposed. The resulting FeNC-750 catalyst shows high acidic ORR activity with a half-wave potential of 0.838 V and a peak power density of 1 W cm -2 in proton-exchange membrane fuel cell (PEMFC). By using operando 57 Fe Mössbauer spectroscopy on FeNC-750, it was revealed that the pyrrolic N-coordinated high-spin Fe 2+ N 4 sites, which are in situ generated from high-spin Fe 3+ N 4 during ORR, are identified as catalytic active states. Density functional theory calculations further verified that, compared to the pyridinic N-coordinated low-spin Fe 2+ N 4 , the pyrrolic N-coordinated high-spin Fe 2+ exhibits optimized adsorption energy for reaction intermediates, thereby lowering the energy barrier of the rate-determining step (RDS) and facilitating OH* desorption. In conclusion, this work provides both experimental and theoretical evidence of the true active site in the FeNC during acidic ORR, offering significant insights for the rational design of high-performance materials for acidic fuel cells.

FeNC catalyst↗
DOE OSTI2025

Identification and Exploration of a Series of SARS-Cov-2 M Pro Cyano-Based Inhibitors Revealing Ortho-Substitution Effects within the P3 Biphenyl Group

Starting from a simple scaffold hopping exercise based on our previous exploration of cysteine protease inhibitors against legumain, compound 6a was identified as a starting point for the development of a SARS-CoV-2 main protease (M Pro ) inhibitor. Compound 6a displayed submicromolar biochemical potency in the ultrasensitive assay developed by Drag and coworkers. Through an iterative structure−activity relationship campaign, we discovered an unexpected improvement in both biochemical and cellular potency through the incorporation of an ortho substituent within the P3 benzamide. X-ray crystallography revealed that incorporation of the ortho substituent caused a subtle but important binding enhancement of the P1 glutamate group within the M Pro S1 pocket. While incorporation of the ortho substituent improved the potency, the off-target selectivity against a panel of cysteine proteases and cell activity remained suboptimal. Further scanning of the P2 core revealed that incorporation of the 3.1.0 proline could address these issues and afford compound 22e, a highly potent and cellularly active M Pro inhibitor.

COVID-19 infection↗
DOE OSTI2026

Peak2Patch: High-Fidelity Functional Group Identification through Attention-Based Fusion of Infrared and Mass Spectra

Identifying molecular structure based on spectroscopic readings is a key task in a variety of chemical and biological applications. Common spectroscopy techniques, such as Infrared (IR) Spectroscopy and Mass Spectrometry (MS), provide detailed information on the structure of molecular compounds but nonetheless require expert-level knowledge to decode. Machine learning has emerged as a potential solution for automating structure prediction from chemical spectra; however, current approaches generally focus on single sensor modalities, neglecting to leverage the complementary information contained within differing spectra. In this paper, we introduce Peak2Patch, a novel approach to fusion-enhanced prediction of functional groups from IR and mass spectra. First, we perform a detailed comparison of backbone networks for encoding both sparse mass spectra and dense IR spectra and demonstrate the superior performance of transformer neural networks over current state-of-the-art convolutional neural networks. Second, we evaluate three broad categories of fusion: early (raw feature), middle (deep feature), and late (decision) fusion, demonstrating the potential of a deep feature fusion-based approach. Lastly, we present Peak2Patch, our attention-based fusion scheme, which leverages cross-attention to mix features between encoded tokens of the two modalities. We validate our approach on a publicly available multimodal spectroscopic data set of 790k simulated molecules, demonstrating a large improvement in functional group prediction over both the previous state-of-the-art and our own strong single-modal baselines.

Jacobson, Philip [Sandia National Laboratories (SN↗
DOE OSTI2025

Identification and Mitigation of Inhibitory Substances Contained in High-Salinity Crude Glycerol Generated from Biodiesel Production for Polyhydroxyalkanoate Synthesis by Haloferax mediterranei

High-salinity crude glycerol generated from biodiesel production poses significant challenges to microbial valorization due to inhibitory ingredients that severely limit microbial growth. This study identified and mitigated inhibitory substances contained in high-salinity glycerol sludge to enable its conversion to polyhydroxyalkanoates (PHAs) by the extreme halophilic archaeon Haloferax mediterranei. The long-chain fatty acids (LCFAs) were consistently identified as the primary inhibitors by liquid chromatography−mass spectrometry, Fourier transform infrared spectroscopy, and ultraviolet−visible spectroscopy. Acid precipitation at pH 2 efficiently removed these LCFAs, substantially reducing the required feedstock dilution from 23 to 3 times, improving PHA titer by 40%. Furthermore, this dilution reduction also increased the feedstock salinity utilization, achieving a 46% reduction in external salt supplementation for H. mediterranei growth. In contrast, overliming and arrested anaerobic digestion were confirmed to be ineffective in inhibitor removal. This study provides deep insights into inhibitor chemistry and presents acid precipitation as an effective pretreatment strategy for waste valorization of highsalinity crude glycerol.

LC-MS↗
DOE OSTI2026

Identification of Solid-Electrolyte Interphase Species by Joint Characterization of Li-Ion Battery Chemistry by Mass Spectrometry and Electrochemical Reaction Networks

The formation and stability of the solid-electrolyte interphase (SEI) play central roles in determining the long-term performance and safety of modern electrochemical energy storage systems. Despite decades of research, the SEI’s heterogeneous, dynamic, and multiphase nature has defied comprehensive molecular-level characterization, creating a critical knowledge gap that limits rational battery design. In this work, we introduce a computational−experimental framework that integrates high-throughput quantum chemistry calculations, data-driven electrochemical reaction networks (eCRNs), stochastic algorithms, and laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry (LDI-FTICR-MS) to unravel SEI formation in carbonatebased electrolytes without imposing predefined mechanisms. We constructed the most comprehensive eCRN to date, spanning over 10,000 species and 209 million reactions. Through stochastic network analysis, we successfully recovered 27 species that were previously reported in the literature and predicted 28 novel SEI species nearly doubling our scientific knowledge in this area. Each new species was rigorously confirmed through advanced mass spectral analysis of its distinct molecular and isotopic signatures. We kinetically refined the formation pathways for a select set of both previously reported and novel SEI products, revealing kinetically feasible elementary reaction mechanisms with activation barriers below 1 eV. This computational−experimental approach deepens our molecular-level understanding of SEI chemistry by resolving which species form and through which decomposition mechanisms they emerge. Such knowledge provides the foundation necessary to connect electrolyte composition to the resulting SEI components, a critical step toward a more informed electrolyte development in next-generation lithium-based batteries.

25 ENERGY STORAGE↗
DOE OSTI2026

Employing Machine Learning for New Particle Formation Identification and Mechanistic Analysis: Insights From a Six‐Year Observational Study in the Southern Great Plains

We present a supervised machine learning (ML) framework to automatically identify new particle formation (NPF) events and analyze key atmospheric factors associated with their occurrence and growth. We applied ML to detect NPF events using start time and particle concentrations across size ranges, while identifying atmospheric variables including ambient temperature, relative humidity, solar radiation intensity (SRI), wind speed, wind direction, boundary layer height, total organics, sulfate, nitrate, total surface area concentration, sulfur dioxide, and turbulent kinetic energy (TKE). We analyzed a 6-year data set from the Atmospheric Radiation Measurement at the Southern Great Plains (SGP) site in Oklahoma, USA. Using long-term ground-based measurements, we identified NPF events and applied Random Forest Classifiers, which achieved 90%–95% prediction accuracy. Feature importance analysis highlighted SRI, relative humidity, and ambient temperature as the most influential variables, contributing normalized importances of 28%, 17%, and 10%. Partial Dependence Plots (PDPs) indicated that higher SRI and lower relative humidity were critical in promoting NPF formation at SGP. Seasonally, NPF events were more frequent in winter (42.1%) and spring (35.5%), and least in summer (4.0%). Particle growth rates also exhibited a seasonal variation, with the lowest in winter (below 2 nm hr −1 ) and highest in late spring and early summer (exceeding 5 nm hr −1 ). Temperature, turbulent kinetic energy, and aerosol properties were the primary factors of growth rate variability. This study advances predictive modeling of NPF, offers insights for future campaign deployments, and demonstrates the effectiveness of ML in understanding the formation and growth of atmospheric aerosols.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Geophysical Impacts and Spectroscopic Identification of a Hydrous Iron Sulfate on Icy Worlds

Over geologic time-scales, large volumes of exogenic sulfur ions from Io's plasma torus have been supplied to the surface of Europa and Ganymede, which, combined with recent interpretations of orbiter images, dynamical modeling, and surface-subsurface exchange, suggests further sulfur transport into the interior of the icy worlds. These observations motivate mixed-phase spectral modeling for interpreting orbiter spectroscopy data and determination of hydration states of candidate surface materials including hydrous sulfates. In this work, we present a combined experimental and theoretical study of the low temperature and high pressure vibrational spectral signature of the iron-sulfate monohydrate endmember, szomolnokite (FeSO 4 ·H 2 O). By employing synchrotron Fourier-transform infrared spectroscopy (FTIR) in the diamond anvil cell up to 23 GPa and down to 20 K, we explore the extreme range of pressure-temperature domains relevant to icy environments throughout our solar system and beyond. Combined with our density-functional theory quantum-mechanics molecular dynamics results, we demonstrate that experimentally observed infrared features in the O-H stretching region commonly associated with nH 2 O (n > 1) hydration states can be attributed to a pure monohydrate without the need for pressure-induced exsolved ice, other coexisting hydrous iron sulfates, or strong overtone and combination modes. We further discuss the possibility of lateral variations in density and shear properties on icy worlds associated with temperature variations and the high-pressure phases of kieserite group monohydrated sulfates.

Geosciences↗