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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Soybean rust‐resistant and tolerant varieties identified through the Pan‐African Trial Network

Abstract BACKGROUND The global demand for soybeans is increasing rapidly, with projections indicating an escalation of 70–80 million metric tons over the next decade. Sub‐Saharan Africa (SSA) contributes significantly to this growth, with soybean production increasing by 6.8% per year, outpacing the global average increase of 4.7%. Despite the expansion, soybean productivity in Africa remains less than half of the global average. This yield gap is largely due to diseases and pests, such as soybean rust, which can be particularly severe. Effective management of soybean rust depends on several factors, including resistant cultivars. However, there has been limited information on the rust‐resistance levels of African cultivars. To address this gap, the Pan‐African Trial network conducted soybean varietal trials across diverse locations. RESULT Analyzing data from 370 individual trials conducted between 2015–2022, the network identified 81 cultivars with sufficient rust‐resistance data. Six cultivars including, Black Hawk, Dundee, Egret, Heron, Ibis, and Peka 06 were found to be resistant, and 12 were classified as tolerant. CONCLUSION This research is a significant step forward in improving soybean productivity in Africa, and further assessments are being undertaken to address other crop production challenges in the region. © 2025 Society of Chemical Industry.

Favoretto, Vitor Rampazzo [Cenex Harvest States (C↗
DOE OSTI2025

Identifying Nuclear Data Correlated Through Predicting Bias in Integral Experiments via Applying Principal Component Analysis to Random Forest

ABSTRACT Nuclear data (ND) are the input data for neutron‐transport simulations to answer questions related to nuclear technologies. Subsets of ND, here > 20,000 data points, are validated with respect to thousands of criticality experiments that represent various applications on a small scale. The aim of validation with these experiments is to find errors in ND or methods. The key challenge here is that several hundreds of ND are used to simulate one integral value. Hence, one cannot clearly identify what ND are leading to bias in criticality measurements. In fact, a mistake in one nuclear‐data observable can be compensated with an error in another, and the predicted criticality value would still be predicted in agreement with experimental data. Random forest (RF) was previously employed to predict bias in criticality measurements using sensitivities of simulated criticality experiments to ND. The SHapley Additive exPlanations (SHAP) metric was then applied to attribute the importance of each ND experiment and observable to bias prediction. This, however, did not highlight what ND were jointly related to predicting bias. This is important as it could inform us about where compensating errors in ND could hide. We tackle this shortcoming here by first decomposing the ND sensitivities to integral‐experiment simulations into principal components. Then we use principal component projections to predict bias via the RF and SHAP. The SHAP values and principal components are employed to reconstruct detailed SHAP values for each ND observable. We demonstrate that these extended SHAP bias predictions are more robust, less noisy, and more efficient. In addition, we show that this approach accounts for covariance in ND sensitivities and automates the identification of where compensating errors could hide in ND.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Characterizing half‐a‐degree difference: a review of methods for identifying regional climate responses to global warming targets

The Paris Agreement long‐term global temperature goal refers to two global warming levels: well below 2°C and 1.5°C above preindustrial. Regional climate signals at specific global warming levels, and especially the differences between 1.5°C and 2°C, are not well constrained, however. In particular, methodological challenges related to the assessment of such differences have received limited attention. This article reviews alternative approaches for identifying regional climate signals associated with global temperature limits, and evaluates the extent to which they constitute a sound basis for impacts analysis. Four methods are outlined, including comparing data from different greenhouse gas scenarios, sub‐selecting climate models based on global temperature response, pattern scaling, and extracting anomalies at the time of each global temperature increment. These methods have rarely been applied to compare 2°C with 1.5°C, but some demonstrate potential avenues for useful research. Nevertheless, there are methodological challenges associated with the use of existing climate model experiments, which are generally designed to model responses to different levels of greenhouse gas forcing, rather than to model climate responses to a specific level of forcing that targets a given level of global temperature change. Novel approaches may be required to address policy questions, in particular: to differentiate between half degree warming increments while accounting for different sources of uncertainty; to examine mechanisms of regional climate change including the potential for nonlinear responses; and to explore the relevance of time‐lagged processes in the climate system and declining emissions, and the resulting sensitivity to alternative mitigation pathways. WIREs Clim Change 2017, 8:e457. doi: 10.1002/wcc.457 This article is categorized under: Assessing Impacts of Climate Change > Scenario Development and Application

James, Rachel↗
DOE OSTI2023

Identifying Fungal Secondary Metabolites and Their Role in Plant Pathogenesis

Pathogenic fungi are the main infectious agents of plants. Secondary metabolites produced by these fungi, also recognized as natural products, are key mediators of plant-fungal interactions. Knowledge on the biosynthesis of these metabolites, the accessibility to fungal genome sequences, and the development of gene disruption techniques open up opportunities to identify many more of these metabolites both in vitro and in planta. This methodology chapter gives a detailed systematic approach aiming to discover new natural products from phytopathogenic fungi and characterize their role in triggering plant cell death and plant disease. This approach takes advantage of the global regulation of fungal secondary metabolite production by regulatory proteins reported in various fungal species.

Tannous, Joanna↗
DOE OSTI2017

Identifying a new particle with jet substructures

Here, we investigate a potential of determining properties of a new heavy resonance of mass O(1)TeV which decays to collimated jets via heavy Standard Model intermediary states, exploiting jet substructure techniques. Employing the Z gauge boson as a concrete example for the intermediary state, we utilize a "merged jet" defined by a large jet size to capture the two quarks from its decay. The use of the merged jet bene ts the identification of a Z-induced jet as a single, reconstructed object without any combinatorial ambiguity. We also find that jet substructure procedures may enhance features in some kinematic observables formed with subjet four-momenta extracted from a merged jet. This observation motivates us to feed subjet momenta into the matrix elements associated with plausible hypotheses on the nature of the heavy resonance, which are further processed to construct a matrix element method (MEM)-based observable. For both moderately and highly boosted Z bosons, we demonstrate that the MEM in combination with jet substructure techniques can be a very powerful tool for identifying its physical properties. Finally, we discuss effects from choosing different jet sizes for merged jets and jet-grooming parameters upon the MEM analyses.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Identifying quenched jets in heavy ion collisions with machine learning

Measurements of jet substructure in ultra-relativistic heavy ion collisions suggest that the jet showering process is modified by the interaction with the quark-gluon plasma. Modifications of the hard substructure of jets can be explored with modern data-driven techniques. In this study, a machine learning approach to the identification of quenched jets is designed. Jet showering processes are simulated with a jet quenching model JEWEL and a non-quenching model PYTHIA 8. Sequential substructure variables are extracted from the jet clustering history following an angular-ordered sequence and are used in the training of a neural network built on top of a long short-term memory network. We show that this approach successfully identifies the quenching effect in the presence of the large uncorrelated background of soft particles created in heavy-ion collisions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Anisotropic flow and flow fluctuations of identified hadrons in Pb–Pb collisions at $ \sqrt{s_{\textrm{NN}}}$ = 5.02 TeV

The first measurements of elliptic flow of π ± , K ± , p+$\bar{p}$, K$_{S}^{0}$, Λ+$\bar{Λ}$, $\phi$, Ξ – +$\barΞ$ + , and Ω – +$\barΩ$ + using multiparticle cumulants in Pb–Pb collisions at $\sqrt{s_{NN}}$= 5.02 TeV are resented. Results obtained with two- (v 2 {2}) and four-particle cumulants (v 2 {4}) are shown as a function of transverse momentum, p T , for various collision centrality intervals. Combining the data for both v 2 {2} and v 2 {4} also allows us to report the first measurements of the mean elliptic flow, elliptic flow fluctuations, and relative elliptic flow fluctuations for various hadron species. These observables probe the event-by-event eccentricity fluctuations in the initial state and the contributions from the dynamic evolution of the expanding quark–gluon plasma. The characteristic features observed in previous p T -differential anisotropic flow measurements for identified hadrons with two-particle correlations, namely the mass ordering at low p T and the approximate scaling with the number of constituent quarks at intermediate p T , are similarly present in the four-particle correlations and the combinations of v 2 {2} and v 2 {4}. In addition, a particle species dependence of flow fluctuations is observed that could indicate a significant contribution from final state hadronic interactions. The comparison between experimental measurements and CoLBT model calculations, which combine the various physics processes of hydrodynamics, quark coalescence, and jet fragmentation, illustrates their importance over a wide p T range.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Non-linear flow modes of identified particles in Pb-Pb collisions at $ \sqrt{s_{\mathrm{NN}}} $ = 5.02 TeV

The pT-differential non-linear flow modes, v 4,22 , v 5,32 , v 6,33 and v 6,222 for π ± , K ± , K$^0_S$ , $p + \bar{p}, Λ + \bar{Λ}$ and ϕ-meson have been measured for the first time at $\sqrt{s_{NN}}$ = 5.02 TeV in Pb-Pb collisions with the ALICE detector at the Large Hadron Collider. The results were obtained with a multi-particle technique, correlating the identified hadrons with reference charged particles from a different pseudorapidity region. These non-linear observables probe the contribution from the second and third order initial spatial anisotropy coefficients to higher flow harmonics. All the characteristic features observed in previous p T -differential anisotropic flow measurements for various particle species are also present in the non-linear flow modes, i.e. increase of magnitude with increasing centrality percentile, mass ordering at low p T and particle type grouping in the intermediate p T range. Hydrodynamical calculations (iEBE-VISHNU) that use different initial conditions and values of shear and bulk viscosity to entropy density ratios are confronted with the data at low transverse momenta. These calculations exhibit a better agreement with the anisotropic flow coefficients than the non-linear flow modes. These observations indicate that non-linear flow modes can provide additional discriminatory power in the study of initial conditions as well as new stringent constraints to hydrodynamical calculations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Higher harmonic flow coefficients of identified hadrons in Pb-Pb collisions at $\sqrt{s_{\mathrm{NN}}}=2.76 $ TeV

The elliptic, triangular, quadrangular and pentagonal anisotropic flow coefficients for π ± , K ± and p + $\bar{p}$ in Pb-Pb collisions at √s NN =2.76 TeV were measured with the ALICE detector at the Large Hadron Collider. The results were obtained with the Scalar Product method, correlating the identified hadrons with reference particles from a different pseudorapidity region. Effects not related to the common event symmetry planes (non-flow) were estimated using correlations in pp collisions and were subtracted from the measurement. The obtained flow coefficients exhibit a clear mass ordering for transverse momentum (p T ) values below ≈ 3 GeV/c. In the intermediate p T region (3 < p T < 6 GeV/c), particles group at an approximate level according to the number of constituent quarks, suggesting that coalescence might be the relevant particle production mechanism in this region. The results for p T < 3 GeV/c are described fairly well by a hydrodynamical model (iEBE-VISHNU) that uses initial conditions generated by A Multi-Phase Transport model (AMPT) and describes the expansion of the fireball using a value of 0.08 for the ratio of shear viscosity to entropy density (η/s), coupled to a hadronic cascade model (UrQMD). Finally, expectations from AMPT alone fail to quantitatively describe the measurements for all harmonics throughout the measured transverse momentum region. However, the comparison to the AMPT model highlights the importance of the late hadronic rescattering stage to the development of the observed mass ordering at low values of p T and of coalescence as a particle production mechanism for the particle type grouping at intermediate values of p T for all harmonics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Studying strangeness and baryon production mechanisms through angular correlations between charged Ξ baryons and identified hadrons in pp collisions at $\sqrt{s}$ = 13 TeV

The angular correlations between charged Ξ baryons and associated identified hadrons (pions, kaons, protons, Λ baryons, and Ξ baryons) are measured in pp collisions at $\sqrt{s}$ = 13 TeV with the ALICE detector to give insight into the particle production mechanisms and balancing of quantum numbers on the microscopic level. In particular, the distribution of strangeness is investigated in the correlations between the doubly-strange Ξ baryon and mesons and baryons that contain a single strange quark, K and Λ. As a reference, the results are compared to Ξπ and Ξp correlations, where the associated mesons and baryons do not contain a strange valence quark. These measurements are expected to be sensitive to whether strangeness is produced through string breaking or in a thermal production scenario. Furthermore, the multiplicity dependence of the correlation functions is measured to look for the turn-on of additional particle production mechanisms with event activity. The results are compared to predictions from the string-breaking model P YTHIA 8, including tunes with baryon junctions and rope hadronisation enabled, the cluster hadronisation model H ERWIG 7, and the core-corona model E POS-LHC . While some aspects of the experimental data are described quantitatively or qualitatively by the Monte Carlo models, no model can match all features of the data. These results provide stringent constraints on the strangeness and baryon number production mechanisms in pp collisions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

The availability of hydrogeologic data associated with areas identified by the US Geological Survey as experiencing potentially induced seismicity resulting from subsurface injection

A critical need exists for site-specific hydrogeologic data in order to determine potential hazards of induced seismicity and to manage risk. By 2015, the United States Geological Survey (USGS) had identified 17 locations in the USA that are experiencing an increase in seismicity, which may be potentially induced through industrial subsurface injection. These locations span across seven states, which vary in geological setting, industrial exposure and seismic history. Comparing the research across the 17 locations revealed patterns for addressing induced seismicity concerns, despite the differences between geographical locations. Most induced seismicity studies evaluate geologic structure and seismic data from areas experiencing changes in seismic activity levels, but the inherent triggering mechanism is the transmission of hydraulic pressure pulses. This research conducted a systematic review of whether data are available in these locations to generate accurate hydrogeologic predictions, which could aid in managing seismicity. After analyzing peer-reviewed research within the 17 locations, this research confirms a lack of site-specific hydrogeologic data availability for at-risk areas. Commonly, formation geology data are available for these sites, but hydraulic parameters for the seismically active injection and basement zones are not available to researchers conducting peer-reviewed research. Obtaining hydrogeologic data would lead to better risk management for injection areas and provide additional scientific evidential support for determining a potentially induced seismic area.

58 GEOSCIENCES↗
DOE OSTI2018

Using stable isotopes (δD, δ{sup 18}O, δ{sup 34}S and {sup 87}Sr/{sup 86}Sr) to identify sources of water in abandoned mines in the Fengfeng coal mining district, northern China

With depleted coal resources or deteriorating mining geological conditions, some coal mines have been abandoned in the Fengfeng mining district, China. Water that accumulates in an abandoned underground mine (goaf water) may be a hazard to neighboring mines and impact the groundwater environment. Groundwater samples at three abandoned mines (Yi, Er and Quantou mines) in the Fengfeng mining district and the underlying Ordovician limestone aquifer were collected to characterize their chemical and isotopic compositions and identify the sources of the mine water. The water was HCO{sub 3}·SO{sub 4}-Ca·Mg type in Er mine and the auxiliary shaft of Yi mine, and HCO{sub 3}·SO{sub 4}-Na type in the main shaft of Quantou mine. The isotopic compositions (δD and δ{sup 18}O) of water in the three abandoned mines were close to that of Ordovician limestone groundwater. Faults in the abandoned mines were developmental, possibly facilitating inflows of groundwater from the underlying Ordovician limestone aquifers into the coal mines. Although the Sr{sup 2+} concentrations differed considerably, the ratios of Sr{sup 2+}/Ca{sup 2+} and {sup 87}Sr/{sup 86}Sr and the {sup 34}S content of SO{sub 4}{sup 2−} were similar for all three mine waters and Ordovician limestone groundwater, indicating that a close hydraulic connection may exist. Geochemical and isotopic indicators suggest that (1) the mine waters may originate mainly from the Ordovician limestone groundwater inflows, and (2) the upward hydraulic gradient in the limestone aquifer may prevent its contamination by the overlying abandoned mine water. The results of this study could be useful for water resources management in this area and other similar mining areas.

58 GEOSCIENCES↗
DOE OSTI2023

Effective data sampling strategies and boundary condition constraints of physics-informed neural networks for identifying material properties in solid mechanics

Material identification is critical for understanding the relationship between mechanical properties and the associated mechanical functions. However, material identification is a challenging task, especially when the characteristic of the material is highly nonlinear in nature, as is common in biological tissue. In this work, we identify unknown material properties in continuum solid mechanics via physics-informed neural networks (PINNs). To improve the accuracy and efficiency of PINNs, we develop efficient strategies to nonuniformly sample observational data. We also investigate different approaches to enforce Dirichlet-type boundary conditions (BCs) as soft or hard constraints. Finally, we apply the proposed methods to a diverse set of time-dependent and time-independent solid mechanic examples that span linear elastic and hyperelastic material space. The estimated material parameters achieve relative errors of less than 1%. As such, this work is relevant to diverse applications, including optimizing structural integrity and developing novel materials.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Comparing results of X-ray diffraction, µ-Raman spectroscopy and neutron diffraction when identifying chemical phases in seized nuclear material, during a comparative nuclear forensics exercise

This work presents the results for identification of chemical phases obtained by several laboratories as a part of an international nuclear forensic round-robin exercise. In this work powder X-ray diffraction (p-XRD) is regarded as the reference technique. Neutron diffraction produced a superior high-angle diffraction pattern relative to p-XRD. Requiring only small amounts of sample, µ-Raman spectroscopy was used for the first time in this context as a potentially complementary technique to p-XRD. The chemical phases were identified as pure UO 2 in two materials, and as a mixture of UO 2 , U 3 O 8 and an intermediate species U 3 O 7 in the third material.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Side-reaction products identified for photo-nuclear production of 99 Mo

Production of 99 Mo by the 100 Mo (γ, n) 99 Mo reaction through the bremsstrahlung process using an electron accelerator is one of the feasible options currently pursued by several countries. In this work, we report experimental results on identification of side-reaction products after the irradiation of natural and enriched 100 Mo targets. Side-reaction products identified include various Mo, Nb and Zr isotopes. Comparison of experimentally determined reaction production rates with those determined based on theoretical cross-sections will be presented. Moreover, activation products formed due to presence of impurities introduced during the manufacturing of the Mo targets will also be discussed.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

Identifying Regimes During Plasma Catalytic Ammonia Synthesis

Herein, we demonstrate that the performance of mesoporous silica SBA-15 and SBA-15-Ag during plasma ammonia synthesis depends on the plasma conditions. At high power, the mesoporous silica SBA-15 without Ag produces the largest amount of ammonia, but the addition of Ag provides a minor benefit at lower powers. Plasma conditions were analyzed through optical emission spectroscopy using N 2 , N 2 + , and NH molecular bands and H α line. Stark broadening of H α line was used to find electron density, and N 2 molecular bands were used to assess N 2 vibrational excitation, important for plasma nitrogen decomposition. At similar input conditions, reactors with SBA-15 have higher electron density and higher N 2 vibrational temperature. Consistent with higher electron density, SBA-15 reactors have stronger N 2 + emission intensity relative to the neutral N 2 . The addition of Ag results in higher N 2 rotational temperature, possibly due to localized heating. From the materials point of view, SBA-15 is a more robust catalyst with good surface area retention after plasma exposure due to the lack of local heating generated when a metal is in the structure. Furthermore, we identify two possible regimes during ammonia synthesis, a metal and a surface-plasma driven. At lower plasma densities, the addition of metal is beneficial, while at higher power and plasma density, the best performance is achieved without the aid of a metal catalyst.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Identifying the minor set cover of dense connected bipartite graphs via random matching edge sets

Using quantum annealing to solve an optimization problem requires minor embedding a logic graph into a known hardware graph. We introduce the minor set cover (MSC) of a known graph GG : a subset of graph minors which contain any remaining minor of the graph as a subgraph, in an effort to reduce the complexity of the minor embedding problem. Any graph that can be embedded into GG will be embeddable into a member of the MSC. Focusing on embedding into the hardware graph of commercially available quantum annealers, we establish the MSC for a particular known virtual hardware, which is a complete bipartite graph. Furthermore, we show that the complete bipartite graph K N,N has a MSC of N minors, from which K N+1 is identified as the largest clique minor of K N,N . In the case of determining the largest clique minor of hardware with faults we briefly discussed this open question.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Do citations and readership identify seminal publications?

Here, this work presents a new approach for analysing the ability of existing research metrics to identify research which has strongly influenced future developments. More specifically, we focus on the ability of citation counts and Mendeley reader counts to distinguish between publications regarded as seminal and publications regarded as literature reviews by field experts. The main motivation behind our research is to gain a better understanding of whether and how well the existing research metrics relate to research quality. For this experiment we have created a new dataset which we call TrueImpactDataset and which contains two types of publications, seminal papers and literature reviews. Using the dataset, we conduct a set of experiments to study how citation and reader counts perform in distinguishing these publication types, following the intuition that causing a change in a field signifies research quality. Finally, our research shows that citation counts work better than a random baseline (by a margin of 10%) in distinguishing important seminal research papers from literature reviews while Mendeley reader counts do not work better than the baseline.

99 GENERAL AND MISCELLANEOUS↗