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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Measurement of the e + e − → Λ Σ ¯ 0 + c . c . cross sections at s from 2.3094 to 3.0800 GeV

The Born cross sections and effective form factors of the process e + e − → Λ Σ ¯ 0 + c . c . are measured at 14 center-of-mass energy points from 2.3094 to 3.0800 GeV, based on data corresponding to an integrated luminosity of ( 478.5 ± 4.8 ) pb − 1 collected with the BESIII detector. A nonzero Born cross section is observed at the center-of-mass energy of 2.3094 GeV with a statistical significance of more than five standard deviations, and the cross sections at other energies are obtained with improved precision compared to earlier measurements from the Collaboration. The Born cross-section line shape is described better by a shape considering the strong-interaction effects than by a pQCD motivated functional form. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2024

New isomeric transition in Mg 36 : Bridging the N = 20 and N = 28 islands of inversion

Here, we observed a new isomeric γ transition at 168 keV in 36 Mg, with a half-life of T1/2=90⁢($^{+410}_{-50}$) ns. We propose that the observed transition deexcites a new 0 + isomeric state at 833 keV and populates the previously known first 2 + state. The existence of this isomer is consistent with the predictions of the large-scale shell-model calculations of 36 Mg using the sdpf-u-mix interaction. The observed excitation energy of the second 0 + state is caused by the small energy separation between two prolate-deformed configurations where the intruder configuration corresponds to two-neutron excitations from the sd to the pf shell. Within this interpretation, 36 Mg becomes the crossing point between nuclei in which ground state deformed/superdeformed configurations are caused by the dominance of N=20 intruders ( 32,34 Mg) and nuclei where deformed configurations are associated with the breaking of the N=28 closure and a large occupancy of the 1⁢p 3/2 neutron orbit ( 38 Mg and beyond). We found the lack of three-body monopole corrections in other effective interactions results in a predominance of N=20 intruder configurations past 38 Mg incompatible with our observation. We conclude that 36 Mg bridges the N=20 and N=28 islands of inversion, forming the so-called big island of deformation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

When does global attention help: a unified empirical study on atomistic graph learning

Graph neural networks (GNNs) are widely used as surrogates for costly experiments and first-principles simulations to study the behavior of compounds at atomistic scale, and their architectural complexity is constantly increasing to enable the modeling of complex physics. While most recent GNNs combine more traditional message passing neural networks (MPNNs) layers to model short-range interactions with more advanced graph transformers (GTs) with global attention mechanisms to model long-range interactions, it is still unclear when global attention mechanisms provide real benefits over well-tuned MPNN layers due to inconsistent implementations, features, or hyperparameter tuning. We introduce the first unified, reproducible benchmarking framework–built on HydraGNN–that enables seamless switching among four controlled model classes: MPNN, MPNN with chemistry/topology encoders, GPS-style hybrids of MPNN with global attention, and fully fused localglobal models with encoders. Using seven diverse open-source datasets for benchmarking across regression and classification tasks, we systematically isolate the contributions of message passing, global attention, and encoder-based feature augmentation. Our study shows that encoder-augmented MPNNs form a robust baseline, while fused localglobal models yield the clearest benefits for properties governed by long-range interaction effects. We further quantify the accuracycompute trade-offs of attention, reporting its overhead in memory. Together, these results establish the first controlled evaluation of global attention in atomistic graph learning and provide a reproducible testbed for future model development.

Equivariant graph neural networks↗
DOE OSTI2023

Synergistic effects of climate warming and atmospheric nutrient deposition on the alpine lake ecosystem in the south-eastern Tibetan Plateau during the Anthropocene

Alpine lakes on the Tibetan Plateau are highly sensitive to global change and have been recognized as the sentinel of climate warming. However, anthropogenic impacts in populated area are migrating to these remote areas via transporting particulate nutrients by atmospheric deposition. Whether warming and nutrient deposition would impose additive or synergistic effects on the lake ecosystem remains largely unknown. Here, we present multi-proxy (sediment pigment and geochemistry) records during the past two centuries at the Cuoqia Lake in the southeast Tibetan Plateau. We found that the lake exhibited rapid ecological changes since 1980 AD characterized by an increase in primary productivity due to algal proliferation, with more rapid growth of green algae and diatoms. These findings are in concert with many other lakes (e.g., Moon Lake and Shade Co) in the same area, suggesting a consistent pattern of ecosystem evolution at the region scale. Statistical analyses suggested that nutrient deposition and climate warming were strongly associated with the variations in primary productivity and algae composition, exerting both individual and interactive effects. In addition, scenario analyses with a well-established process-based ecosystem model further revealed that the two factors not only individually, but also synergistically promoted the algal proliferation and community succession. Such synergy is evident in that the effect of lake warming would be more pronounced under higher nutrient deposition scenario, which is potentially due to higher temperature-driven mineralization in warmer conditions, and higher efficiency of nutrient utilization under enhanced light availability attributing to declining ice thickness and duration in cold seasons. Overall, our study proposes the existence and quantifies the synergistic impacts of climate warming and anthropogenic activities in driving the ecological changes in remote alpine lakes on the Tibetan Plateau. The lake ecological consequences driven by individual factor would be worsen by such synergy, so that we cannot predict the lake ecosystem trajectory in the future based on each factor separately, and more efforts than previously expected would be needed for the lake restoration and management.

Zhang, Can↗
DOE OSTI2021

Pyrolysis temperature and soil depth interactions determine PyC turnover and induced soil organic carbon priming

Pyrogenic organic carbon (PyC) is a complex, heterogeneous class of thermally altered organic substrates, but its dynamics and how its behavior changes with soil depth remain poorly understood. We conducted a laboratory incubation study to investigate the interactive effects of pyrolysis temperature and soil depth on the turnover of PyC compared to its precursor wood and native SOC (NSOC). We incubated dual-labeled (13C and 15N) jack pine pyrogenic organic matter produced at 300 °C (PyC300), 450 °C (PyC450), and their precursor pine wood in a fine-loamy, mixed-conifer forest soil for 745 days. A mixture of surface (0–10 cm) and subsurface (50–70 cm) forest soils, with and without labeled biomass were incubated in the dark at 55% soil water field capacity and 25 °C. Total 13C from PyC and wood mineralized as 13C-CO2 (as % of C added to soil) declined with an increase in pyrolysis temperature as follows: 54 ± 7.7% for wood, 3.1 ± 0.2% for PyC300, and 0.94 ± 0.08% for PyC450. Furthermore, after 2 years, soil depth interacted with pyrolysis temperature to affect C turnover, with total wood C losses significantly declining from 70.6% in surface soils to 37.5% in subsurface soil, while total losses of PyC300 and PyC450 were unaffected by differences between surface and subsurface soils. Wood induced negative priming (i.e., decreased mineralization rates) in surface soil at days 3 and 60, while PyC300 induced positive priming (i.e., increased mineralization rates) in subsurface soil at day 60. After 2 years, unlabeled NSOC losses increased from 9.2 ± 0.8% of NSOC in unamended treatments to 16.5 ± = 2.6% of NSOC with PyC450 additions. Our results suggest that PyC pyrolyzed at a given temperature can mineralize at similar rates between soil depths, and high amounts of PyC450 in subsurface soils can stimulate NSOC losses. These findings indicate that soil depth imposes critical controls on PyC dynamics belowground.

13C-labeling↗
DOE OSTI2025

Expediting field-effect transistor chemical sensor design with neuromorphic spiking graph neural networks

Improving the sensitive and selective detection of analytes in a variety of applications requires accelerating the rational design of field-effect transistor (FET) chemical sensors. Achieving high-performance detection relies on identifying optimal probe materials that can effectively interact with target analytes, a process traditionally driven by chemical intuition and time-consuming trial-and-error methods. To address the difficulties in probe screening for FET sensor development, this work presents a methodology that combines neuromorphic machine learning (ML) architectures, specifically a hybrid spiking graph neural network (SGNN), with an enriched dataset of physicochemical properties through semi-automated data extraction using large language models. Achieving a classification accuracy of 0.89 in predicting sensor sensitivity categories, the SGNN model outperformed traditional ML techniques by leveraging its ability to capture both global physicochemical properties and sparse topological features through a hybrid modeling framework. Next-generation sensor design was informed by the actionable insights into the connections between material properties and sensing performance offered by the SGNN framework. Through virtual screening for the detection of per- and polyfluoroalkyl substances (PFAS) as a use case, the effectiveness of the SGNN model was further validated. Density functional theory simulations confirmed graphene as a promising active material for PFAS detection as suggested by the SGNN framework. By bridging gaps in predictive modeling and data availability, this integrated approach provides a strong foundation for accelerating advancements in FET sensor design and innovation.

Ferreira, Rodrigo Pires [Univ. of Chicago, IL (Uni↗
DOE OSTI2022

Post-inflationary dark matter bremsstrahlung

Dark matter may only interact with the visible sector efficiently at energy scales above the inflaton mass, such as the Planck scale or the grand unification scale. In such a scenario, the dark matter is mainly produced out of equilibrium during the period of reheating, often referred to as UV freeze-in. Here, we evaluate the abundance of the dark matter generated from bremsstrahlung off the inflaton decay products assuming no direct coupling between the inflaton and the dark matter. This process generally dominates the production of dark matter for low reheating temperatures where the production through the annihilations of particle in the thermal plasma becomes inefficient. We find that the bremsstrahlung process dominates for reheating temperatures T RH ≲ 10 10 GeV, and produces the requisite density of dark matter for a UV scale ≃ 10 16 GeV. As examples, we calculate numerically the yield of the dark matter bremsstrahlung through gravitation and dimension-6 vector portal effective interactions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Precipitation and nitrogen application stimulate soil nitrous oxide emission

Precipitation and nitrogen (N) fertilization are the two most important drivers for soil nitrous oxide (N 2 O) emission. However, the effects of changes in N fertilization and precipitation patterns (i.e., precipitation intensity and frequency) on N 2 O emissions in agricultural fields are still unclear. In this study, we simulated soil N 2 O emission under different precipitation patterns (6 precipitation intensities, and 12 precipitation frequencies by either merging or splitting precipitation events) and N fertilization rates (low, typical, and high N fertilization) in a cornfield using the DeNitrification-DeComposition model. The model was parameterized and validated using meteorological data and N experimental measurements in Nashville, Tennessee, USA. Results showed that soil water filled pore space (WFPS) and simulated soil N 2 O emission increased as precipitation intensity increased. Less frequent but high intensity precipitation treatments reduced the soil WFPS by 25.2% and stimulated soil N 2 O emission by 45.3%, while more frequent but low intensity precipitation treatments increased soil WFPS by 9.0% and reduced soil N 2 O emission by 23.9%. Compared to typical N fertilization, the sensitivity of soil N 2 O emission to precipitation was higher under high N than low N fertilization treatments, and the response ratios were 50.0% and 40.1%, respectively. There was significant interactive effect of precipitation intensity and N fertilization on soil N 2 O emission. Furthermore, these findings improved our understanding of precipitation and N impacts on soil N 2 O emissions and provided useful knowledge for irrigation and N fertilizer management in agriculture to mitigate greenhouse gas emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Spin and Orbital Effects on Asymmetric Exchange Interaction in Polar Magnets: M(IO 3 ) 2 (M = Cu and Mn)

We report magnetic polar materials feature an astonishing range of physical properties, such as magnetoelectric coupling, chiral spin textures, and related new spin topology physics. This is primarily attributable to their lack of space inversion symmetry in conjunction with unpaired electrons, potentially facilitating an asymmetric Dzyaloshinskii–Moriya (DM) exchange interaction supported by spin–orbital and electron–lattice coupling. However, engineering the appropriate ensemble of coupled degrees of freedom necessary for enhanced DM exchange has remained elusive for polar magnets. Here, we study how spin and orbital components influence the capability of promoting the magnetic interaction by studying two magnetic polar materials, α-Cu(IO 3 ) 2 ( 2 D) and Mn(IO 3 ) 2 ( 6 S), and connecting their electronic and magnetic properties with their structures. The chemically controlled low-temperature synthesis of these complexes resulted in pure polycrystalline samples, providing a viable pathway to prepare bulk forms of transition-metal iodates. Rietveld refinements of the powder synchrotron X-ray diffraction data reveal that these materials exhibit different crystal structures but crystallize in the same polar and chiral P2 1 space group, giving rise to an electric polarization along the b-axis direction. The presence and absence of an evident phase transition to a possible topologically distinct state observed in α-Cu(IO 3 ) 2 and Mn(IO 3 ) 2 , respectively, imply the important role of spin–orbit coupling. Neutron diffraction experiments reveal helpful insights into the magnetic ground state of these materials. While the long-wavelength incommensurability of α-Cu(IO 3 ) 2 is in harmony with sizable asymmetric DM interaction and low dimensionality of the electronic structure, the commensurate stripe AFM ground state of Mn(IO 3 ) 2 is attributed to negligible DM exchange and isotropic orbital overlapping. The work demonstrates connections between combined spin and orbital effects, magnetic coupling dimensionality, and DM exchange, providing a worthwhile approach for tuning asymmetric interaction, which promotes evolution of topologically distinct spin phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

New physics in single resonant top quarks

Abstract Searches for new physics in the top quark sector are of great theoretical interest, yet some powerful avenues for discovery remain unexplored. We characterize the expected statistical power of the LHC dataset to constrain the single production of heavy top partnersTdecaying to a top quark and a photon or a top quark and a gluon. We describe an effective interaction which could generate such production, though the limits apply to a range of theoretical models. We find sensitivity to cross sections in the 10 2 − 10 5 fb range, forTmasses between 300 and 1000 GeV, depending on decay mode.

Physics↗
DOE OSTI2023

Non-isometric codes for the black hole interior from fundamental and effective dynamics

We introduce a new holographic map for encoding black hole interiors by including both fundamental and effective dynamics. This holographic map is constructed by evolving a state in the effective, semiclassical gravity description of the interior backwards in time to pull the degrees of freedom outside the black hole, before evolving forwards in time in the fundamental description. We show this “backwards-forwards” map is equivalent to a post-selection map of the type introduced by Akers, Engelhardt, Harlow, Penington, and Vardhan, and in the case of trivial effective interactions reduces to their model, while providing a suitable generalization when those interactions are nontrivial. We show the map is equivariant with respect to time evolution, and independent of any interactions outside the black hole. This construction includes interactions with an infaller in a way that preserves the unitarity of black hole evolution exactly and does not allow for superpolynomial computational complexity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Statistical framework to assess long-term spatio-temporal climate changes: East River mountainous watershed case study

Abstract Evaluation of long-term temporal and spatial climatic change in mountainous regions is a critical challenge because of the interactive effects of multiple land and climatic factors and processes. Here we present the application of the statistical framework to the assessment of changes of climatic conditions, using data from 17 meteorological stations across the East River watershed near Crested Butte, Colorado, USA, and spanning the period from 1966 to 2021. The framework is developed based on (1) a time-series analysis of daily, monthly, and yearly averaged meteorological parameters (temperature, relative humidity, precipitation, wind speed, etc.), (2) evaluation and time series analysis of potential evapotranspiration (ET o ), actual evapotranspiration (ET), aridity index (AI), standard precipitation index (SPI) and standard precipitation-evapotranspiration index (SPEI), and (3) a temporal-spatial climatic zonation of the studied area based on the hierarchical clustering and PCA analysis of the SPEI, because the SPEI can be considered an integrative characteristic of the changes of climatic conditions. The Budyko model, with the application of the Penman–Monteith equation for the estimation of ET o , was used to determine the ET. The time series analysis of the AI is used to identify the periods with energy limited and water limited conditions. Hierarchical clustering of site locations for the three temporal segments of the SPEI showed a significant temporal-spatial shifts, indicating that dynamic climatic processes drive zonation patterns. Therefore, the watershed climatic zonation requires periodic re-evaluation based on the structural time series analysis of meteorological and water balance data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Macroscale Control of Reactivity using 3D Printed Materials with Intrinsic Catalytic Properties

The morphology of heterogeneous catalysts can impact their performance. However, standard manufacturing methods like extrusion or pelleting offer little options for tailoring catalyst shape. Herein, stereolithographic 3D printing is used to produce catalysts with controlled topologies to enhance their performance. A series of magnetic stir-bar compartments (SBC) were 3D printed and tested as catalysts for sucrose hydrolysis. The SBC were printed using acrylic acid (AA) and 1,6-hexanediol diacrylate (HDDA) as acid sites and hydrophobic crosslinking domains, respectively. Variations in the number and tilt direction of the SBC blades produced significant changes in their apparent catalytic activities. These changes resulted from differences in the fraction of active surface effectively interacting with the reactants in solution, as revealed by computational fluid dynamics simulations. Moreover, varying HDDA:AA ratios in SBC regulated reactant-surface interactions to control catalytic activity. Overall, 3D printing catalysts enables quick performance optimization by simultaneously controlling macroscopic structure and molecular composition.

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

Large-eddy simulation study on cycle-to-cycle variation of knocking combustion in a spark-ignition engine

The cycle-to-cycle variation in the knock intensity is commonly encountered under abnormal combustion conditions. The severity of these abnormal combustion events can vary significantly, and the efficiency of engines at high loads is limited in practice by heavy knocking phenomena. Since, a thorough analysis of such recurrent but non-cyclic phenomena via experiments alone becomes highly cumbersome, in the present work, a multi-cycle large-eddy simulation study was performed to quantitatively predict cyclic variability in the combustion process and cyclic knock intensity variability in a direct injection spark-ignition engine. To account for the turbulence-chemistry interaction effects on flame propagation, the G-equation combustion model was used. Detailed chemistry was solved outside the flame front with a toluene primary reference fuel skeletal kinetic mechanism. For both the mild knock and heavy knock conditions, the numerical results were validated against experimental measurements. Based on the simulation results, a correlation analysis was performed considering combustion phasing, peak cylinder pressure and maximum amplitude of pressure oscillation. Furthermore, a detailed three-dimensional spatial analysis illustrated the evolution of auto-ignition kernel development and propagation of pressure waves during knocking combustion for three typical cycles with different knock intensities. In this process, it was found that an early occurrence of auto-ignition in the end gas was prone to high knock intensity. Although multiple auto-ignition kernels were observed in different cycles, the degree of coupling between chemical heat release and pressure waves varied, thereby leading to different maximum amplitude of pressure oscillation values.

42 ENGINEERING↗
DOE OSTI2023

Insights into co-pyrolysis of polyethylene terephthalate and polyamide 6 mixture through experiments, kinetic modeling and machine learning

The non-isothermal pyrolysis of polyethylene terephthalate (PET), polyamide 6 (PA6), and their mixtures was studied in a thermogravimetric analyzer at different heating rates. Temperature of maximum decomposition (T max ) decreased by 25–45 °C and 35–55 °C for the PET:PA6 mixtures (3:1, 1:1, 1:3) compared to PET and PA6, respectively. The kinetic analysis was initially carried out using isoconversional method. However, the dependency of activation energy on conversion was observed for the co-pyrolysis of PET and PA6 that suggested the occurrence of multi-step reactions in the mixtures. Distributed activation energy model (DAEM) was used in this study to describe the multistep reactions occurring during pyrolysis of PET:PA6 mixtures. Here, in this work, a four-parallel reaction DAEM was developed to describe the pyrolysis kinetics of PET:PA6 mixtures. The apparent mean activation energies (E o ) for PET, PA6, and mixtures varied in the range of 244–255, 140–215, and 138–255 kJ mol –1 , respectively. The mass loss profiles of PET and PA6 mixtures were also modeled using artificial neural network (ANN). Out of 155 ANN models, the best prediction was made by ANN511 with R 2 greater than 0.997 for both test and unseen data. The interaction effects observed through TGA experiments and subsequent kinetic analysis were further assessed in terms of product composition using analytical pyrolysis coupled with gas chromatograph/mass spectrometer (Py-GC/MS). Co-pyrolysis of PET and PA6 resulted in the formation of new aromatic compounds with nitrogen-containing functional groups, which were not detected when PET or PA6 were pyrolyzed individually.

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

NESSi : The N on- E quilibrium S ystems S imulation package

The nonequilibrium dynamics of correlated many-particle systems is of interest in connection with pump–probe experiments on molecular systems and solids, as well as theoretical investigations of transport properties and relaxation processes. Nonequilibrium Green’s functions are a powerful tool to study interaction effects in quantum many-particle systems out of equilibrium, and to extract physically relevant information for the interpretation of experiments. Here, we present the open-source software package NESSi (The Non-Equilibrium Systems Simulation package) which allows to perform many-body dynamics simulations based on Green’s functions on the L-shaped Kadanoff–Baym contour. NESSi contains the library libcntr which implements tools for basic operations on these nonequilibrium Green’s functions, for constructing Feynman diagrams, and for the solution of integral and integro-differential equations involving contour Green’s functions. The library employs a discretization of the Kadanoff–Baym contour into time points and a high-order implementation of integration routines. The total integrated error scales up to $\mathcal{O}(N^{-7})$, which is important since the numerical effort increases at least cubically with the simulation time. A distributed-memory parallelization over reciprocal space allows large-scale simulations of lattice systems. We provide a collection of example programs ranging from dynamics in simple two-level systems to problems relevant in contemporary condensed matter physics, including Hubbard clusters and Hubbard or Holstein lattice models. The libcntr library is the basis of a follow-up software package for nonequilibrium dynamical mean-field theory calculations based on strong-coupling perturbative impurity solvers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Range-wide population assessments for subalpine fir indicate widespread disturbance-driven decline

Subalpine forests in western North America are threatened by rapid climate change, increased activity by endemic and exotic insects and diseases, and changing wildfire regimes. The interactive effects of these stressors have resulted in pronounced population declines in many subalpine tree species; however, a systematic assessment of the status and trends of subalpine forests is lacking. Subalpine fir (Abies lasiocarpa) is a widespread species across the western United States, with documented population declines in many parts of its distribution. Here we use subalpine fir as an initial leverage point to build a more complete understanding of subalpine forest baseline conditions and responses to environmental change. Specifically, we leverage the USDA Forest Service Forest Inventory and Analysis (FIA) database to (1) ask how subalpine fir populations are changing across the species’ distribution in the western US, (2) assess the drivers of recent subalpine fir population trends, and (3) explore whether those changes imply generalized species-wide and/or system-wide decline. We found that subalpine fir abundance and basal area are declining concurrently across ~ 62% of the species’ distribution, and increasing across ~ 19%. Range-wide, we estimated 25.02 ± 2.74 % subalpine fir mortality between 2000 and 2009 and 2010–2019 FIA inventory periods, with higher mortality concentrated in the eastern Oregon Cascades, central Idaho, and parts of southern Colorado. High regeneration density did not predict positive population trajectories, which were instead associated with higher rates of adult recruitment. While the importance of different mortality agents varied substantially between ecoregions, 83.4% of total range-wide mortality was related to fire or biological disturbance. Declining subalpine fir basal area coincided with declines in the basal area of other co-occurring tree species in 39% of subalpine forest area, and with increases in conspecific basal area in 22% of forest area. Fire disturbance was the single largest cause of subalpine fir mortality; however, even where subalpine fir fire mortality was high, mortality among other species was primarily caused by insects. In conclusion, our results suggest that subalpine fir declines across large portions of the western United States are driven by forest disturbance, and that declines in subalpine fir populations may be indicative of negative change in subalpine forest systems broadly.

54 ENVIRONMENTAL SCIENCES↗