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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Adsorption Hysteresis Under Control: Tuning Host–Guest Interactions via a Genetic Algorithm

Mesoporous adsorbent materials offer a large volumetric capacity; however, cyclic adsorption/desorption processes in these systems often suffer from hysteresis and may require a significant pressure swing to access this capacity. To mitigate hysteresis, a proposed strategy is to include nucleation sites on the walls of the mesoporous material to facilitate droplet and bubble formation, lowering the free energy barriers to the respective phase transitions. It is unclear, however, what combination of adsorbate− adsorbent interactions and spatial patterning would be beneficial for a given application, considering that improvements to some sorption properties may come at the expense of other attributes. To understand these interconnected observables, we examine two model systems, planar-slit and cylindrical pores with tunable interaction sites, using GPU-accelerated transition matrix Monte Carlo simulations. The simulations provide a free energy map of the pressure−adsorption space in a matter of minutes, which we use to track adsorption isotherm characteristics as a function of adsorbent properties. We then leverage the rapid acquisition of simulation data to construct a genetic algorithm to iteratively modify interaction sites of the slit-pore wall to minimize the hysteresis of this system without sacrificing uptake. We find that the adsorption branch of the isotherm is easily modulated via the average host−guest interaction strength, but desorption is only adjustable if there is a suitable bubble nucleation site. Within the context of a slit-pore system, we identify relative interaction strengths and patch sizes required to gain control over both branches of the hysteresis loop.

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

Shrub Expansion Can Counteract Carbon Losses From Warming Tundra

Arctic warming is causing substantial compositional, structural, and functional changes in tundra vegetation including shrub and tree-line expansion and densification. However, predicting the carbon trajectories of the changing Arctic is challenging due to interacting feedbacks between vegetation composition and structure, and surface characteristics. We conduct a sensitivity analysis of the current-date to 2100 projected surface energy fluxes, soil carbon pools, and CO 2 fluxes to different shrub expansion rates under future emission scenarios (intermediate—RCP4.5, and high—RCP8.5) using the Arctic-focused configuration of E3SM Land Model (ELM). We focus on Trail Valley Creek (TVC), an upland tundra site in the western Canadian Arctic, which is experiencing shrub densification and expansion. We find that shrub expansion did not significantly alter the modeled surface energy and water budgets. However, the carbon balance was sensitive to shrub expansion, which drove higher rates of carbon sequestration as a consequence of higher shrubification rates. Thus, at low shrub expansion rates, the site would become a carbon source, especially under RCP8.5, due to higher temperatures, which deepen the active layer and enhance soil respiration. At higher shrub expansion rates, TVC would become a net CO 2 sink under both Representative Concentration Pathway scenarios due to higher shrub productivity outweighing temperature-driven respiration increase. Our simulations highlight the effect of shrub expansion on Arctic ecosystem carbon fluxes and stocks. We predict that at TVC, shrubification rate would interact with climate change intensity to determine whether the site would become a carbon sink or source under projected future climate.

Yazbeck, Theresia [The Ohio State Univ., Columbus,↗
DOE OSTI2025

Developing machine learning for heterogeneous catalysis with experimental and computational data

Machine learning techniques have emerged as a useful tool for identifying complex patterns and correlations in large datasets, such as associating catalyst performance to its physicochemical properties. In the heterogeneous catalysis communities, machine learning models have mostly been developed using high-throughput quantum chemistry calculations, with only a few case studies resulting in experimentally validated catalyst improvements. This limited success may be due to the use of simplified catalyst structures in computational studies and the lack of comprehensive experimental datasets. In this Review, we bring together studies integrating high-throughput approaches and machine learning for the advancement of solid heterogeneous catalysis, leveraging both experimental and computational data. We systematically analyze trends in the field, based on the descriptors used as model input and output; the materials, devices, or reactions investigated; the dataset size; and the overall achievements. Furthermore, for models reporting unitless R 2 values, we compare the performances based on these mentioned trends.

Computational chemistry↗
DOE OSTI2026

Fragmentation dynamics of CS 2 dications and trications following S 2p ionization

Here, we present the results from a detailed study of the fragmentation dynamics of CS$_2^{2+}$ and CS$_2^{3+}$, formed in intense femtosecond soft x-ray pulses above the sulfur 2p edge, primarily through single core photoionization from the S 2p site, and subsequent Auger–Meitner decay(s). By combining three-dimensional velocity map imaging with covariance analysis, we determine the relative momenta of the ions produced in each two- and three-body fragmentation channel, at significantly higher ion count rates than conventional coincidence measurements. We shed new light on the wide range of fragmentation channels observed from the CS 2 dication and trication, including channels that involve ionization-induced bond formation and fragmentations producing undetected neutral cofragments. In the latter case, a “native frames” approach is used to isolate contributions from concerted and sequential fragmentations and extract dynamical information about each step of a concerted fragmentation process. While dications often fragment sequentially, the trication is dominated by concerted fragmentation. The main trication fragmentation channel into S + + C + + S + can be well-approximated by classical Coulombic simulations of the ground-state geometry distribution, reflecting both the nature of the trication potential energy surface and the rapid multiple ionization prior to substantial structural dynamics. This study demonstrates ways in which fundamental insights into the fragmentation dynamics of polycations following x-ray ionization may be extracted, which will be beneficial to future studies that employ time-resolved x-ray Coulomb explosion imaging to study ultrafast photochemistry.

Allum, Felix [Deutsches Elektronen-Synchrotron (DE↗
DOE OSTI2025

Cross‐Cultural Validation of the Binge Eating Disorder Screener‐7 ( BEDS ‐7) Across 42 Countries

ABSTRACT Objective This study aimed to evaluate the reliability and validity of the Binge Eating Disorder Screener‐7 (BEDS‐7) across 42 countries and 26 languages, assessing its reliability and validity as a screening tool for binge‐eating disorder (BED) in diverse cultural contexts. Specifically, it sought to enhance early recognition of BED symptoms in primary care settings globally, contributing to a standardized framework for assessing BED. Method The International Sex Survey, a cross‐sectional online study, was conducted in 42 countries and 26 languages. A diverse community sample of 82,243 participants, aged 18 years or older, completed the BEDS‐7 and measures of sexuality, mental health, substance use, and sociodemographic characteristics. Confirmatory factor analyses and tests of measurement invariance were employed to evaluate the reliability and validity of the BEDS‐7 across languages, countries, genders, and sexual orientations. Results The BEDS‐7 demonstrated scalar factorial invariance across languages and countries, indicating consistent factor loadings and item intercepts. In contrast, the screener showed residual invariance across gender and sexual orientation groups, supporting its robustness across these demographics. Kruskal–Wallis tests revealed significant differences in BED symptoms across languages, countries, genders, and sexual orientations, with the highest BED scores observed among queer, pansexual, and gender‐diverse individuals. The BEDS‐7 also demonstrated adequate reliability (Cronbach's alpha > 0.80) and moderate criterion validity. Discussion The findings provide further evidence of the reliability and validity of the BEDS‐7 as a potential screening tool for identifying probable cases of BED globally, facilitating early intervention in primary care settings.

Gewirtz‐Meydan, Ateret [School of Social Work, Fac↗
DOE OSTI2025

Accurate and efficient predictions of keyhole dynamics in laser materials processing using machine learning-aided simulations

The keyhole phenomenon has been widely observed in laser materials processing, including laser welding, remelting, cladding, drilling, and additive manufacturing. Keyhole-induced defects, primarily pores, dramatically affect the performance of final products, impeding the broad use of these laser-based technologies. The formation of these pores is typically associated with the dynamic behavior of the keyhole. So far, the accurate characterization and prediction of keyhole features, particularly keyhole depth, as a function of time, has been a challenging task. In situ characterization of keyhole dynamic behavior using the synchrotron X-ray technique is informative but complicated and expensive. Current simulations are generally hindered by their poor accuracy and generalization abilities in predicting keyhole depths due to the lack of accurate laser absorptance data. In this study, we develop a machine learning-aided simulation method that accurately predicts keyhole dynamics, especially in keyhole depth fluctuations, over a wide range of processing parameters. In two case studies involving titanium and aluminum alloys, we achieve keyhole depth prediction with a mean absolute percentage error of 10 %, surpassing those simulated using the ray-tracing method with an error margin of 30 %, while also reducing computational time. This exceptional fidelity and efficiency empower our model to serve as a cost-effective alternative to synchrotron experiments. Our machine learning-aided simulation method is affordable and readily deployable for a large variety of materials, opening new doors to eliminate or reduce defects for a wide range of laser materials processing techniques.

Computational fluid dynamics↗
DOE OSTI2025

Process intensification in hydrothermal liquefaction of biomass: A review

Hydrothermal liquefaction (HTL) presents a promising pathway for converting wet biomass resources into biofuels, offering significant advantages over conventional methods. However, numerous challenges must be addressed for HTL scale-up, including energy provision for the endothermic process, heat and mass transfer limitations, slurry concentration and pumpability challenges, char and coke formation, and continuous phase separation. This review explores key strategies such as autothermal HTL, which improves process efficiency and reduces external energy requirements by coupling exothermic and endothermic reactions within the same reactor, thereby simplifying reactor design and reducing operational costs. Additionally, multistage HTL processes are highlighted for their ability to optimize biocrude quality and yield by fractionating biomass conversion stages, resulting in higher energy returns on investment and better-quality biocrude. Solvothermal HTL and integration techniques for aqueous phase are also discussed. Furthermore, the HTL patent landscape is discussed to provide insights into current technological advancements. This review aims to offer a comprehensive understanding of process intensification in HTL, highlighting innovative solutions to enhance the efficiency and scalability of the process for sustainable biofuel production.

Biocrude oil↗
DOE OSTI2026

Structural Analysis of 23S rRNA Methylating Enzyme Cfr Reveals RNA-Binding Determinants for Methylation Regiospecificity and Antibiotic Resistance

The 23S rRNA methylating enzyme Cfr, found in pathogens including Staphylococcus aureus, Clostridium difficile, Escherichia coli, and Klebsiella pneumoniae, confers resistance to phenicols, lincosamides, oxazolidinones (including linezolid), pleuromutilins, and streptogramins A (the PhLOPS A phenotype). Cfr catalyzes methylation of the C8 position of the A2503 base in 23S rRNA, the recognition site of the above antibiotic classes. Along with the RlmN housekeeping enzyme, Cfr can also promote methylation of the C2 position of the same base. The molecular and structural basis of Cfr’s dual substrate specificity is not known, which hinders our ability to design Cfr-targeting inhibitors necessary to curb PhLOPS A resistance. Here, we present the first crystal structure of Cfr and a detailed analysis of its possible interactions with rRNA. Using structure-guided mutagenesis, mass spectrometry analysis of in cellulo 23S rRNA methylated species, and in cellulo resistance studies, we identify the key amino acids essential for Cfr methylation and multidrug resistance activity. In particular, we found that Cfr’s Q329 residue is important for C8- specific methylation. These data provide a framework for further studies of the biochemistry, structure, and inhibition of this important resistance determinant.

AlphaFold↗
DOE OSTI2025

Promoting Reversible Anionic Redox in Sodium-Ion Cathodes by Doping and Phase Control

Important efforts are underway to harness anionic redox to obtain high-energy Na-ion cathodes. Previously, we identified disruptive dopants in Na–Mn–O that induced reversible oxygen redox. Here, we perform detailed mechanistic studies to understand why these dopants are effective. First, we confirm that no transition metals (TMs) are being oxidized─it is indeed oxygen redox. We also identify that reversible TM migration occurs in the P2 phase where reversible anionic redox occurs, while the migration is irreversible in the distorted P′2 phase. Structural control over the anionic redox is highly significant, but we further elucidate the role of the disruptive dopants. Localized oxygen holes are identified as the source of the reversible anionic redox, and these are deemed to remain stable due to the dopants minimizing the interactions between oxygens to prevent their dimerization. Furthermore, these important contributions to understanding anionic redox will help realize viable high-energy Na-ion batteries.

Impurities↗
DOE OSTI2025

Permafrost, Peatland, and Cropland Regions Are Key to Reconciling North American Carbon Sink Estimates

Persistent discrepancies between bottom-up, terrestrial biosphere models (TBMs), and top-down, atmospheric inversions, have made it difficult to quantify the magnitude of the North American terrestrial carbon sink. Previous studies have compared aggregated continent-scale estimates of carbon fluxes from TBMs and inversions for all of North America, but this provides limited insights into finer-scale mismatches that contribute to the overall discrepancies. Here we evaluate agreement between TBM and inversion carbon flux estimates at 1° × 1° resolution to provide more direct insights into where models disagree and what underlying factors drive discrepancies. We find that the additional carbon uptake estimated by inversions, in just 16% of the area of North America, is large enough to account for the discrepancy between TBMs and inversions across the whole continent. The majority of these differences occur in permafrost, peatland, and cropland regions. In these regions, we find a higher likelihood of potential biases in the weaker sink estimates from TBMs, suggesting that the stronger sink implied by inversions is more likely to be realistic. However, the current observational coverage is insufficient for fully assessing the causes of discrepancies or the magnitude of biases in either approach. Encouragingly, improved representation of agricultural processes in a TBM led to better agreement with inversions in croplands. Efforts to accurately model cropland dynamics will help improve agreement between TBMs and inversions. Overall, this work presents a clear path for reconciling the discrepancies between inversion and TBM estimates of the North American carbon sink that have persisted for two decades.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Loosely bound composite dark matter

We investigate loosely bound composite states made of dark matter, where the binding energy for constituent particles is considerably less than the constituent mass. We focus on models of nuclear and molecular dark matter, where constituents are separated by length scales larger than the inverse constituent mass, just like nuclei and atoms in the Standard Model. The cosmology, structure, and interactions at underground experiments are described. We find that loosely bound composites can have a very large cross section for scattering with nuclei that scales with nucleon number like ∼ A 4 . For some couplings, these composites produce extremely soft (≪ keV) individual atomic recoils while depositing a large amount of total recoil energy (≫ keV) in a single passage through a detector, implying an interesting new class of signatures for low threshold direct detection.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Single-Crystal Diffuse Neutron Scattering Study of the Dipole-Octupole Quantum Spin-Ice Candidate Ce 2⁢ Zr 2 ⁢O 7 : No Apparent Octupolar Correlations Above 𝑇 = 0.05 K

The insulating magnetic pyrochlore Ce 2 ⁢Zr 2 ⁢O 7 has gained attention as a quantum spin-ice candidate with dipole-octupole character that arises from the crystal-electric-field ground-state doublet for the Ce 3+ Kramers ion. This dipole-octupole character permits both spin-ice phases based on magnetic dipoles and those based on more-exotic octupoles. This work reports low-temperature neutron diffraction measurements on single-crystal Ce 2 ⁢Zr 2⁢ O 7 with 𝑄 coverage both at low 𝑄, where the magnetic form factor for dipoles is near maximal, and at high 𝑄, covering the region where the magnetic form factor for Ce 3+ octupoles is near maximal. This study was motivated by recent powder neutron diffraction studies of other Ce-based dipole-octupole pyrochlores, Ce 2 ⁢Sn 2 ⁢O 7 and Ce 2 ⁢Hf 2 ⁢O 7 , which each showed temperature-dependent diffuse diffraction at high 𝑄, interpreted as arising from octupolar correlations. Our measurements use an optimized single-crystal diffuse scattering instrument that allows us to screen against strong Bragg scattering from Ce 2 ⁢Zr 2 ⁢O 7 . The temperature-difference neutron diffraction reveals a low-𝑄 peak consistent with dipolar spin-ice correlations reported in previous work, and an alternation between positive and negative net intensity at higher 𝑄. These features are consistent with our numerical-linked-cluster calculations using pseudospin interaction parameters previously reported for Ce 2 ⁢Zr 2⁢ O 7 , Ce 2 ⁢Sn 2 ⁢O 7 , and Ce 2 ⁢Hf 2 ⁢O 7 . Importantly, neither the measured data nor any of the NLC calculations show evidence for increased scattering at high 𝑄 resulting from octupolar correlations. We conclude that at the lowest attainable temperature for our measurements (𝑇 = 0.05 K), scattering from octupolar correlations in Ce 2 ⁢Zr 2 ⁢O 7 is not present in the neutron diffraction signal on the level of our observation threshold of around 0.1% of the low-𝑄 dipole scattering. We compare these results to those obtained earlier on powder Ce 2 ⁢Sn 2 ⁢O 7 and Ce 2⁢ Hf 2⁢ O 7 , and to low-energy inelastic neutron scattering from single-crystal Ce 2 ⁢Zr 2 ⁢O 7 .

36 MATERIALS SCIENCE↗
DOE OSTI2025

Learning and Controlling Silicon Dopant Transitions in Graphene Using Scanning Transmission Electron Microscopy

A machine learning approach is introduced to determine the transition dynamics of silicon atoms on a single layer of carbon atoms, when stimulated by the electron beam of a scanning transmission electron microscope (STEM). This method is data-centric, leveraging data collected on a STEM. The data samples are processed and filtered to produce symbolic representations, which is used to train a neural network to predict transition probabilities. These learned transition dynamics are then leveraged to guide a single silicon atom throughout the lattice to pre-determined target destinations. Empirical analyses are presented that demonstrate the efficacy and generality of the approach.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Pressure‐Induced Structural and Optoelectronic Modulations in 2D Dion‐Jacobson Hybrid Lead Iodide Perovskites With a Rigid Spacer

2D hybrid organic-inorganic perovskites (HOIPs) are known for their superior chemical and thermal stability over 3D variants, positioning them as promising materials for advanced optoelectronic applications. Applying external high pressure is an effective strategy to modify their structures and tune their optoelectronic properties. This study examines the high-pressure behavior of a Dion-Jacobson type 2D HOIP, DPDAPbI 4 , which incorporates the rigid organic spacer N,N-dimethylphenylene-p-diammonium (DPDA). Utilizing photoluminescence, UV–visible absorption, vibrational spectroscopy, and in situ synchrotron powder X-ray diffraction, an isostructural phase transition is identified near 1.5 GPa, marked by a notable shift in photoluminescence intensity linked to free exciton emission. Synchrotron X-ray diffraction reveals significant anisotropic compression along the c-axis, while structural analysis indicates the rare phenomenon of reduced lead-iodide octahedral distortion below 2 GPa, contrasting with the increased octahedral distortion reported in most previous studies on lead-based 2D HOIPs. Density functional theory calculations elucidate the structural origins and mechanisms driving the pressure-induced phase transition and optoelectronic tuning. These findings underscore the critical interplay between rigid organic spacers and inorganic octahedra in modulating high-pressure optoelectronic properties, offering valuable insights for designing future 2D HOIPs with tailored functionalities.

14 SOLAR ENERGY↗
DOE OSTI2025

The microbiologist's guide to metaproteomics

Metaproteomics is an emerging approach for studying microbiomes, offering the ability to characterize proteins that underpin microbial functionality within diverse ecosystems. As the primary catalytic and structural components of microbiomes, proteins provide unique insights into the active processes and ecological roles of microbial communities. By integrating metaproteomics with other omics disciplines, researchers can gain a comprehensive understanding of microbial ecology, interactions, and functional dynamics. This review, developed by the Metaproteomics Initiative (www.metaproteomics.org), serves as a practical guide for both microbiome and proteomics researchers, presenting key principles, state-of-the-art methodologies, and analytical workflows essential to metaproteomics. Topics covered include experimental design, sample preparation, mass spectrometry techniques, data analysis strategies, and statistical approaches.

bioinformatics↗
DOE OSTI2026

The Zooplankton International Geospatial dataset: A global repository of spatiotemporal freshwater zooplankton community composition data from lakes and reservoirs to support ecological research

Zooplankton transfer substantial energy in aquatic food webs and are used as indicators of environmental change. Syntheses of zooplankton community dynamics globally require datasets that span a wide range of environmental gradients; however, these datasets are limited due to methodological differences across programs, taxonomic inconsistencies, and a lack of standardized metadata. To reconcile these challenges, we created the Zooplankton International Geospatial (ZIG) dataset, which includes original zooplankton, water physical and chemical variables, and lake morphometric data from 311 inland lakes and reservoirs. ZIG includes waterbodies ranging in size from 0.005 to 82,100 km2 and spanning broad latitudinal (−47.26 to 64.90) and longitudinal ranges (−165.04 to 176.53). Temporal coverage for individual waterbodies ranges between 1 and 60 yr with sampling frequency ranging from annually to weekly. With its extensive coverage and content, we consider ZIG to be a cornerstone for future investigations of global scale lake biodiversity change.

Figary, Stephanie [Cornell University, Ithaca, NY]↗
DOE OSTI2025

Highly charged ion approach to measure nuclear charge radii of Fr, Ra, and Rn isotopes for precision measurements

Conventional electron scattering and muonic atom spectroscopy techniques are challenging to apply to heavy ions unless the element has at least one stable or extremely long-lived isotope. To overcome this limitation, a recently introduced method for determining nuclear charge radii relies on extreme-ultraviolet (EUV) spectroscopy of the D 1 line in highly charged Na-like ions. In this work, we present an experimental approach to measure the nuclear charge radii of isotopes of radioactive elements such as Fr, Ra, and Rn using this method at TRIUMF’s Ion Trap for Atomic and Nuclear Science (TITAN) instrument, located at the ISAC radioactive beam facility. We also explore the potential of using Na-like D 2 lines in the soft x-ray region for future measurements.

Electron beam ion trap↗
DOE OSTI2025

Blue Carbon Stocks Along the Pacific Coast of North America Are Mainly Driven by Local Rather Than Regional Factors

Coastal wetlands, including seagrass meadows, emergent marshes, mangroves, and temperate tidal swamps, can efficiently sequester and store large quantities of sediment organic carbon (SOC). However, SOC stocks may vary by ecosystem type and along environmental or climate gradients at different scales. Quantifying such variability is needed to improve blue carbon accounting, conservation effectiveness, and restoration planning. We analyzed SOC stocks in 1,284 sediment cores along >6,500 km of the Pacific coast of North America that included large environmental gradients and multiple ecosystem types. Tidal wetlands with woody vegetation (mangroves and swamps) had the highest mean stocks to 1 m depth (357 and 355 Mg ha −1 , respectively), 45% higher than marshes (245 Mg ha −1 ), and more than 500% higher than seagrass (68 Mg ha −1 ). Unvegetated tideflats, though not often considered a blue carbon ecosystem, had noteworthy stocks (148 Mg ha −1 ). Stocks increased with tidal elevation and with fine (<63 μm) sediment content in several ecosystems. Stocks also varied by dominant plant species within individual ecosystem types. At larger scales, marsh stocks were lowest in the Sonoran Desert region of Mexico, and swamp stocks differed among climate zones; otherwise stocks showed little correlation with ecoregion or latitude. More variability in SOC occurred among ecosystem types, and at smaller spatial scales (such as individual estuaries), than across regional climate gradients. These patterns can inform coastal conservation and restoration priorities across scales where preserving stored carbon and enhancing sequestration helps avert greenhouse gas emissions and maintains other vital ecosystem services.

emergent marsh↗