Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Identifiability”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Identifying biomass yield potential of tetra, hexa, and octoploid prairie cordgrass populations grown on marginal lands

As the demand for renewable energy sources with reduced carbon emissions to the atmosphere increases, bioenergy crops remain an attractive alternative to fossil fuels. Another appeal of bioenergy crops is the potential utilization of marginal lands not suitable for food crops. Prairie cordgrass is a perennial warm season polyploid grass species being considered as a bioenergy crop that also exhibits a high tolerance for salinity and flooding. The ploidy level of prairie cordgrass has previously been suggested to correlate with agronomic traits. However, little is known about the agricultural performance of prairie cordgrass on marginal lands and if a correlation of ploidy level to stress tolerance exists. In this experiment, the productivity of tetraploids, hexaploids, and octoploids was investigated when grown on three different marginal sites including waterlogged, saline, and low-nutrient gravelly soils. Here we found that prairie cordgrass populations annually produced dry biomass on a wet marginal site comparable to normal cropland. However, the productivity was remarkedly decreased at both a salt-affected site and a limestone mine reclamation site. We were unable to conclude that different ploidy levels affected the biomass yield of prairie cordgrass grown under abiotic stress. However, there was a differential response of populations to various types of marginal sites. Finally, these results indicate potential genetic resources for developing breeding targets and identifying mechanisms of abiotic stress tolerance. Moreover, we highlight the need to generate diverse ploidy levels of prairie cordgrass in the same genetic backgrounds for future testing.

09 BIOMASS FUELS↗
DOE OSTI2022

Identifying Hidden Li–Si–O Phases for Lithium‐Ion Batteries via First‐Principle Thermodynamic Calculations

SiO–based materials are promising alloys and conversion‐type anode materials for lithium‐ion batteries and are recently found to be excellent dendrite‐proof layers for lithium‐metal batteries. However, only a small fraction of the Li–Si–O compositional space has been reported, significantly impeding the understanding of the phase transition mechanisms and the rational design of these materials both as anodes and as protection layers for lithium‐metal anodes. Herein, we identify three new thermodynamically stable phases within the Li–Si–O ternary system (Li 2 SiO 5 , Li 4 SiO 6, and Li 4 SiO 8 ) in addition to the existing records via first‐principle calculations. The electronic structure simulation shows that Li 2 SiO 5 and Li 4 SiO 8 phases are metallic in nature, ensuring high electronic conductivity required as electrodes. Moduli calculations demonstrate that the mechanical strength of Li–Si–O phases is much higher than that of lithium metal. The diffusion barriers of interstitial Li range from 0.1 to 0.6 eV and the interstitial Li hopping serves as the dominating diffusion mechanism in the Li–Si–O ternary systems compared with vacancy diffusion. These findings provide a new strategy for future discovery of improved alloying anodes for lithium‐ion batteries and offer important insight towards the understanding of the phase transformation mechanism of alloy‐type protection layers on lithium‐metal anodes.

Qu, Jiale↗
DOE OSTI2020

Aqueous Diels–Alder reactions for thermochemical storage and heat transfer fluids identified using density functional theory

Thermal storage and transfer fluids have important applications in industrial, transportation, and domestic settings. Current thermal fluids have relatively low specific heats, often significantly below that of water. However, by introducing a thermochemical reaction to a base fluid, it is possible to enhance the fluid's thermal properties. Here, density functional theory (DFT) is used to screen Diels–Alder reactions for use in aqueous thermal fluids. From an initial set of 52 reactions, four are identified with moderate aqueous solubility and predicted turning temperature near the liquid region of water. These reactions are selectively modified through 60 total functional group substitutions to produce novel reactions with improved solubility and thermal properties. Among the reactions generated by functional group substitution, seven have promising predicted thermal properties, significantly improving specific heat (by as much as 30.5%) and energy storage density (by as much as 4.9%) compared to pure water.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Novel compound heterozygous CPLANE1 variants identified in a Chinese family with Joubert syndrome

Abstract Joubert syndrome (JS) and JS‐related disorders (JSRD) are a group of neurodevelopmental diseases that share the “molar tooth sign” on axial brain magnetic resonance imaging (MRI), accompanied by cerebellar vermis hypoplasia, ataxia, hypotonia, and developmental delay. To identify variants responsible for the clinical symptoms of a Chinese family with JS and to explore the genotype–phenotype associations, we conducted a series of clinical examinations, including blood tests, brain MRI scans, ultrasound imaging, and ophthalmologic examination. Genomic DNA was extracted from the peripheral blood of the six‐person family, and the pathogenic variants were detected by whole‐exome sequencing (WES) and verified by Sanger sequencing. WES revealed two novel compound heterozygous variants in CPLANE1 : c.1270C>T (p.Arg424*) in exon 10 and c.8901C>A (p.Tyr2967*) in exon 48 of one child, inherited from each parent. Both variants were absent in ethnically matched Chinese control individuals and were either absent or present at very low frequencies in public databases, suggesting that these variants could be the pathogenic triggers of the JS phenotype. Notably, these CPLANE1 sequence variants were related to the pathogenesis of autosomal recessive JS in this study. The newly discovered variants expand the mutation spectrum of CPLANE1 , which assists in understanding the molecular mechanism underlying JS and improving the recognition of genetic counseling, particularly for families with a history of autosomal recessive JS.

Zhang, Cheng↗
DOE OSTI2021

Identifying crystallographic faces of the fluorites urania and thoria with rotational polarized Raman spectroscopy

Crystallite orientation identification is invaluable, but is often limited to small area identification or requires a large area sample. Nondestructive optical methods such as polarized Raman spectroscopy, in contrast, can be used to completely map a variety of sample sizes, but their potential is not yet fully realized. Here, we report a systematic study of polarized Raman scattering of high-quality, hydrothermally grown, single crystals of urania and thoria. The peak intensity variations for as-grown major crystal planes, post-growth polished crystal planes, and a post-growth polished non-crystallographic plane are directly linked to crystallographic orientation and crystal rotation, and agree with computed models. In particular, the parallel polarized peak intensity results are directly correlated with metal–oxygen–metal chains in the fluorite structure and can be used to determine both orientation and rotational alignment of a given crystal face if sufficiently small rotational steps are applied. These results are structure based, being applicable to the larger fluorite phase space, which is useful for optical, semiconductor, nuclear, and solid oxide fuel cell industries. Further, these results suggest that Raman spectroscopy can identify non-crystallographic orientations that are not discernable by traditional means.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Frank–Kasper Phases Identified in PDMS- b -PTFEA Copolymers with High Conformational Asymmetry

In the search for the formation of Frank-Kasper phases from diblock copolymer self-assembly, a series of compositionally asymmetric poly(dimethylsiloxane)- b -poly(2,2,2-triflouroethyl acrylate)s (PDMS- b -PTFEAs) are synthesized to produce PDMS-rich phases with PDMS volume fractions ( f PDMS ) ranging from 0.746 to 0.869. As determined by small-angle X-ray scattering analysis, the Frank-Kasper sigma and C14 phases are identified at f PDMS = 0.796 and 0.851, respectively, plausibly due to high conformational asymmetry (ϵ ≈ 2.20) between the two blocks. Intriguingly, the σ phase develops during heating from a short-range liquid-like packing (LLP) state, whereas the C14 phase is achieved at room temperature, which are both followed by a disordering at higher temperatures. Finally, based on thermal experiments from a super cooled disordered state, the findings further provide compelling evidence of an LLP-hexagonally packed cylinder-σ transition and a direct pathway to the C14 phase during heating from an LLP state.

42 ENGINEERING↗
DOE OSTI2017

Quantitative proteomics identifies altered O-GlcNAcylation of structural, synaptic and memory-associated proteins in Alzheimer's disease: Brain protein O-GlcNAcylation in Alzheimer's disease

Protein modification by O-linked beta-N-acetylglucosamine (O-GlcNAc) is emerging as an important factor in the pathogenesis of sporadic Alzheimer’s disease. Herein we report the most comprehensive, quantitative proteomics analysis for protein O-GlcNAcylation in post-mortem human brains with and without Alzheimer’s using isobaric tandem mass tags labeling, chemoenzymatic photocleavage enrichment and liquid chromatography coupled to mass spectrometry. A total of 1,850 O-GlcNAc peptides covering 1,094 O-GlcNAcylation sites were identified from 530 proteins in the human brain. 128 O-GlcNAc peptides covering 78 proteins were altered significantly in Alzheimer’s brain as compared to controls (q<0.05). Moreover, alteration of the O-GlcNAc peptide abundance could be attributed more to O-GlcNAcylation level than to protein level changes. The altered O-GlcNAcylated proteins belong to several structural and functional categories, including synaptic proteins, cytoskeleton proteins, and memory-associated proteins. These findings suggest that dysregulation of O-GlcNAcylation of multiple brain proteins may be involved in the development of sporadic Alzheimer’s disease.

59 BASIC BIOLOGICAL SCIENCES↗
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 &straightphi;-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↗