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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Learning Planar Ising Models Software

Learning Planar Ising Models is a software package written in Matlab for learning relationships among variable in a dataset using graphical models. The software package implements a generally-applicable algorithm for learning planar Ising models from any multivariate dataset. The code provides an algorithm for learning the best planar Ising model to approximate an arbitrary collection of binary random variables (possibly from sample data). Given the set of all pairwise correlations among variables, we select a planar graph and optimal planar Ising model defined on this graph to best approximate that set of correlations. The software includes demonstrations of the algorithm in simulations and for applications on publicly available datasets. Details of the algorithm, demonstration simulations, and applications are given in Johnson, et al; 2016. Reference: Johnson, J. K., Oyen, D., Chertkov, M., and Netrapalli, P. (2016). Learning planar Ising models. Journal of Machine Learning Research.

Oyen, Diane↗
DOE OSTI2016

Learning planar Ising models

Inference and learning of graphical models are both well-studied problems in statistics and machine learning that have found many applications in science and engineering. However, exact inference is intractable in general graphical models, which suggests the problem of seeking the best approximation to a collection of random variables within some tractable family of graphical models. In this paper, we focus on the class of planar Ising models, for which exact inference is tractable using techniques of statistical physics. Based on these techniques and recent methods for planarity testing and planar embedding, we propose a greedy algorithm for learning the best planar Ising model to approximate an arbitrary collection of binary random variables (possibly from sample data). Given the set of all pairwise correlations among variables, we select a planar graph and optimal planar Ising model defined on this graph to best approximate that set of correlations. Finally, we demonstrate our method in simulations and for two applications: modeling senate voting records and identifying geo-chemical depth trends from Mars rover data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Extraction of Furfural and Furfural/5-Hydroxymethylfurfural from Mixed Lignocellulosic Biomass-Derived Feedstocks

We present a combined in silico and experimental study on the extraction of furfural and 5-hydroxymethylfurfural (HMF) in aqueous–organic biphasic systems. We predict the liquid–liquid equilibria and furfural/HMF partition coefficients of over 2200 water-organic biphasic systems using the multiscale COSMO-RS model and measure experimentally single-component (furfural) and mixture (furfural and HMF) partition coefficients at room and dehydration reaction-relevant temperatures in 28 solvents. We find the experimental data to be within a factor of 2 from the COSMO-RS predictions. Even though furfural and HMF have chemical similarity, the slight differences in molecular structure render the separation of furfural easier by the supply of more solvents of higher partition coefficient for extraction. We leverage this molecular difference and experimentally demonstrate that with an additional extraction step, using dichloromethane or toluene, we can selectively extract furfural from furfural-HMF mixtures, which can coexist in lignocellulosic biomass dehydration products, despite their partition coefficients being generally correlated. We complement solvent selection criteria for biphasic lignocellulosic biomass processes with a simple mass balance extraction model for determining volume ratios in multistage extraction. Lastly, the molecular nature of the preferential furfural extraction is rationalized using COSMO-RS σ-profile analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Supramolecular assemblies of polybenzimidazole and aromatic polycarboxylic acids with superior mechanical and H 2 /CO 2 separation properties

Aromatic polycarboxylic acids complex with imidazoles, forming supramolecular assemblies. Herein, we demonstrate that polybenzimidazole (PBI) can be augmented by aromatic polycarboxylic acids, including phthalic acid (PA), trimesic acid (TMA), and pyromellitic acid (PMA), and the resulting supramolecular assemblies exhibit attractive H 2 /CO 2 separation performance for precombustion carbon capture. The acid doping decreases the free volume and gas permeability but increases H 2 /CO 2 selectivity, which can be correlated with the molar ratio of the protons to PBI repeating units in the assemblies. Increasing the temperature decreases the H 2 /CO 2 selectivity because of weakened interactions. When challenged with model gas mixtures, a supramolecular assembly based on TMA shows H 2 /CO 2 separation properties superior to state-of-the-art materials and above Robeson's upper bound. Furthermore, this study elucidates the fruitful harnessing of dynamic non-covalent bonds to design robust and highly selective membranes for industrial gas separations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Assessing the Performance of Exchange‐Correlation Density Functionals in Describing the Iron‐Catalyzed Ammonia Synthesis System

Density functional theory (DFT) has been widely employed for elucidating mechanistic aspects of heterogeneous catalysis. However, the accuracy of DFT calculations relies heavily on selecting an exchange-correlation (XC) functional that correctly describes the electronic structure of materials involved in the reactions. This study assesses the accuracy of several XC density functionals for modeling the iron-catalyzed ammonia synthesis system. In the assessment of functional accuracy, experimental references are compared to DFT-calculated values for the formation energy of gas-phase ammonia and nitrogen, bulk Fe/Fe-nitride (γ′-Fe 4 N) lattice constants and cohesive/formation energies, and nitrogen and ammonia binding energies on Fe(100), Fe(111), Fe(110), and γ′-Fe 4 N(111). It is observed that the experimental value for each of these descriptors is accurately modeled by at least one functional. RPBE alone provides reliable estimates for both the lattice constant and cohesive energy of Fe and γ′-Fe 4 N. Temperature-programmed desorption experiments led to estimates for N and NH 3 adsorption across several Fe-based facets that are best captured by RPBE. These results highlight the importance of choosing an appropriate XC functional that accurately describes experimental systems and offer insights into effectively modeling the interactions between nitrogen and ammonia on Fe-based surfaces.

adsorption↗
DOE OSTI2024

A new generation of effective core potentials: Selected lanthanides and heavy elements

Here, we construct correlation-consistent effective core potentials (ccECPs) for a selected set of heavy atoms and ƒ-elements that are of significant current interest in materials and chemical applications, including Y, Zr, Nb, Rh, Ta, Re, Pt, Gd, and Tb. As is customary, ccECPs consist of spin orbit averaged relativistic effective potential (AREP) and effective spin-orbit (SO) terms. For the AREP part, our constructions are carried out within a relativistic coupled-cluster framework while also taking into account objective function one-particle characteristics for improved convergence in optimizations. The transferability is adjusted using binding curves of hydride and oxide molecules. We address the difficulties encountered with ƒ-elements, such as the presence of large cores and multiple near-degeneracies of excited levels. For these elements, we construct ccECPs with core valence partitioning that includes 4ƒ-subshell in the valence space. The developed ccECPs achieve an excellent balance between accuracy, size of the valence space, and transferability and are also suitable to be used in plane wave codes with reasonable energy cutoffs.

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

Virtual refrigerant charge sensor for variable-speed heat pumps based on feature selection

The refrigerant charge level in heat pump systems significantly impacts their energy efficiency. Virtual refrigerant charge (VRC) sensing technology has been comprehensively investigated and well-established due to its lower cost compared to physical sensors. However, the previous VRC research often relied on expert judgment and physical reasoning for their variable selection, which can potentially select redundant (or highly correlated) or insignificant features, and it is also primarily focused on single-speed systems. To address these challenges, this study proposes a VRC algorithm for variable-speed heat pumps that selects features through a rigorous feature selection method in combination with physical insights. We also propose a piecewise linear model structure segmented by subcooling temperature to accurately predict charge levels, particularly when subcooling temperatures are substantially low. The proposed algorithm was evaluated using experimental data of a residential R410A heat pump, and the performance was compared with two baseline VRC algorithms. The results are: (1) The proposed algorithm outperforms for the case with subcooling temperature less than 1 °C. (2) The proposed algorithm achieves a tested mean absolute percentage error (MAPE) of 4.23%, and improves the overall accuracy for cooling conditions by approximately 60%, compared with the two baseline algorithms. (3) The proposed algorithm uses two fewer features and improves the accuracy for undercharge cooling conditions by 68.0%, compared with baseline algorithm 2. These improvements enhance prediction accuracy and prevent overfitting, providing a more reliable refrigerant charge level prediction and helping improve the heat pump energy efficiency.

Liang, Chenjiyu↗
DOE OSTI2023

Oligosaccharide production and signaling correlate with delayed flowering in an Arabidopsis genotype grown and selected in high [CO 2 ]

Since industrialization began, atmospheric CO 2 ([CO 2 ]) has increased from 270 to 415 ppm and is projected to reach 800–1000 ppm this century. Some Arabidopsis thaliana (Arabidopsis) genotypes delayed flowering in elevated [CO 2 ] relative to current [CO 2 ], while others showed no change or accelerations. To predict genotype-specific flowering behaviors, we must understand the mechanisms driving flowering response to rising [CO 2 ]. [CO 2 ] changes alter photosynthesis and carbohydrates in plants. Plants sense carbohydrate levels, and exogenous carbohydrate application influences flowering time and flowering transcript levels. We asked how organismal changes in carbohydrates and transcription correlate with changes in flowering time under elevated [CO 2 ]. We used a genotype (SG) of Arabidopsis that was selected for high fitness at elevated [CO 2 ] (700 ppm). SG delays flowering under elevated [CO 2 ] (700 ppm) relative to current [CO 2 ] (400 ppm). We compared SG to a closely related control genotype (CG) that shows no [CO 2 ]-induced flowering change. We compared metabolomic and transcriptomic profiles in these genotypes at current and elevated [CO 2 ] to assess correlations with flowering in these conditions. While both genotypes altered carbohydrates in response to elevated [CO 2 ], SG had higher levels of sucrose than CG and showed a stronger increase in glucose and fructose in elevated [CO 2 ]. Both genotypes demonstrated transcriptional changes, with CG increasing genes related to fructose 1,6-bisphosphate breakdown, amino acid synthesis, and secondary metabolites; and SG decreasing genes related to starch and sugar metabolism, but increasing genes involved in oligosaccharide production and sugar modifications. Genes associated with flowering regulation within the photoperiod, vernalization, and meristem identity pathways were altered in these genotypes. Elevated [CO 2 ] may alter carbohydrates to influence transcription in both genotypes and delayed flowering in SG. Changes in the oligosaccharide pool may contribute to delayed flowering in SG. This work extends the literature exploring genotypic-specific flowering responses to elevated [CO 2 ].

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

DESI luminous red galaxy samples for cross-correlations

We present two galaxy samples, selected from DESI Legacy Imaging Surveys (LS) DR9, with approximately 20,000 square degrees of coverage and spectroscopic redshift distributions designed for cross-correlations such as with CMB lensing, galaxy lensing, and the Sunyaev-Zel'dovich effect. The first sample is identical to the DESI Luminous Red Galaxy (LRG) sample, and the second sample is an extended LRG sample with 2–3 times the DESI LRG density. We present the improved photometric redshifts, tomographic binning and their spectroscopic redshift distributions and imaging systematics weights, and magnification bias coefficients. The catalogs and related data products will be made publicly available. The cosmological constraints using this sample and Planck lensing maps are presented in a companion paper. Here, we also make public the new set of general-purpose photometric redshifts trained using DESI spectroscopic redshifts, which are used in this work, for all galaxies in LS DR9.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Selection-bias effects on high-𝑝 𝑇 yield and correlation measurements in oxygen + oxygen collisions

Oxygen + oxygen (O+O) collisions at the BNL Relativistic Heavy Ion Collider (RHIC) and the CERN Large Hadron Collider (LHC) offer a unique experimental opportunity to observe the onset of jet quenching in intermediate relativistic collision systems. As with the smaller proton-nucleus or larger nucleus-nucleus systems, measurements of centrality-selected high-𝑝 T processes in O+O collisions are expected to be sensitive to selection bias effects, which will be necessary to quantify or mitigate before a definitive conclusion on the presence of jet quenching. Using two Monte Carlo heavy-ion event generators, we provide a survey of centrality bias effects on high-𝑝 T yield and correlation measurements. Some highlights of our findings include that (1) bias factors for the accessible kinematic range at RHIC show a nontrivial 𝑝 T dependence, compared to a negligible one at the LHC given the smaller accessible Bjorken-𝑥 range, (2) centrality definitions based on multiplicity are less sensitive to bias effects than those based on the transverse energy, (3) the Angantyr generator gives qualitatively similar but larger-magnitude bias factors than Hijing, and (4) correlation measurements have a much smaller sensitivity to bias effects than do yield measurements. Furthermore, the findings here are intended to guide the experimental design and interpretation of O+O jet quenching and other hard-process measurements.

Hard scattering↗
DOE OSTI2017

Catalysts for selective hydrogenation of furfural derived from the double complex salt [Pd(NH 3 ) 4 ](ReO 4 ) 2 on γ-Al 2 O 3

The double complex salt [Pd(NH3)4](ReO4)2 was employed as precursor of supported bimetallic catalysts for selective hydrogenation of furfural. Direct reduction of [Pd(NH3)4](ReO4)2 on γ-Al2O3 in flowing H2 at 400 °C yields bimetallic nanoparticles 1–2 nm in size that exhibit significant interaction between the metals, as evidenced by temperature-programmed hydride decomposition (complete suppression of β-PdHx formation), extended X-ray absorption fine structure spectroscopy at the Pd K and Re LIII edges (PdRe distance = 2.72 Å), and scanning transmission electron microscopy with energy dispersive X-ray analysis. In contrast, calcination of [Pd(NH3)4](ReO4)2 on γ-Al2O3 at 350 °C in air and subsequent reduction in H2 at 400 °C results in metal segregation and formation of large (>50 nm) supported Pd particles; Re species cover the Pd particles and γ-Al2O3 support. A PdRe 1:2 catalyst prepared by sequential impregnation and calcination using HReO4 and [Pd(NH3)4](NO3)2 has a similar morphology. The catalyst derived by direct reduction of [Pd(NH3)4](ReO4)2 on γ-Al2O3 exhibits remarkably high activity for selective hydrogenation of furfural to furfuryl alcohol (FAL) at 150 °C and 1 atm. Suppression of H2 chemisorption via elimination of Pd threefold sites, as evidenced by CO diffuse-reflectance infrared Fourier transform spectroscopy, correlates with increased FAL selectivity.

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

A Comparative Study of Ethanol Upgrading to Butadiene Over Rare Earth Elements Incorporated in Dealuminated Beta Zeolites

Copper and rare earth element-containing dealuminated Beta zeolites (CuREE/deAlBeta) are promising catalysts for the conversion of ethanol to C 3+ olefins en route to sustainable aviation fuel. Product selectivities over CuREE/deAlBeta depend on the identity of the REE. Here, in this study, a suite of La/deAlBeta catalysts of varied La loading are prepared, and their reactivities, selectivities, and apparent C-C coupling kinetics in the cascade reaction between ethanol and acetaldehyde are reported. Ex situ and in situ titrations to quantify Lewis acidic La sites quantify distinct numbers of sites, suggesting a distribution of site types. Across a series of REE/deAlBeta catalysts (REE = Y, La, Lu, Yb, Gd), the selectivity to secondary products correlates with the pyridine heats of adsorption at REE Lewis acid sites. The turnover frequency (per Lewis acid site) for C–C coupling was similar across the series of REE/deAlBeta catalysts. Y/deAlBeta and La/deAlBeta, which have the most disparate secondary product selectivities, had similar kinetics for C–C coupling. These findings demonstrate the complexity of quantifying active sites in REE/deAlBeta zeolites, and the sensitivity of the selectivity, but not the reactivity for C–C coupling, to the identity of the REE atom in REE/deAlBeta zeolites.

alcohol upgrading↗
DOE OSTI2017

Sputter-deposited WO x and MoO x for hole selective contacts

Here, reactive sputter deposited tungsten and molybdenum oxide (WO x , MoO x ) thin films are tested for their ability to form a hole selective contact for Si wafer based solar cells. A characterization approach based on analyzing the band bending induced in the c-Si absorber and the external and implied open-circuit voltage of test structures was used. It is shown that the oxygen partial pressure allows to tailor the selectivity to some extent and that a direct correlation between induced band bending and hole selectivity exists. Although the selectivity of the sputtered films is inferior to the reference films deposited by thermal evaporation, these results demonstrate a good starting point for further optimizations of sputtered WO x and MoO x towards higher work functions to improve the hole selectivity.

14 SOLAR ENERGY↗
DOE OSTI2018

Enhanced Catalytic Properties of Molybdenum Promoted Mesoporous Cobalt Oxide: Structure-Surface-Dependent Activity for Selective Synthesis of 2-Substituted Benzimidazoles

High-valent molybdenum ions were substituted into the cobalt oxide lattice through a one step, sol-gel method and investigated for selective synthesis of 2-substituted benzimidazoles. Catalyst synthesis involves surfactant assisted soft templating inverse micelle method, which forms mesopores by interconnected intraparticle voids. Substitutional doping of Mo 6+ resulted in materials with modified structural, morphological, surface, and redox properties. The catalytic activity increased with Mo concentration until an optimum amount (3 % Mo incorporation). Modified material shows lattice expansion, increased surface oxygen vacancies, and high surface area, which are responsible for the higher catalytic activity in selective benzimidazole synthesis reaction. A strong correlation between surface properties of the catalyst and the product selectivity was observed and plausible mechanistic and kinetic data are proposed and collected, respectively.

2-substituted benzimidazoles↗
DOE OSTI2023

Coalescing Cation Selectivity Approaches in Ionomers

Inconsistent selectivity measures used in the literature to quantify an ionomer’s preference for one ion over another make it challenging to compare uptake of different ions across studies. Preferential ion-partitioning, which leads to partially exchanged membranes, needs to be studied to gain a fundamental understanding of the nature of ion/membrane interactions as well as to begin to unravel how varying ion fractions, whether or not intentional, may affect the membrane’s properties and thus durability and performance. In this focus review, we use Nafion as a prototypical example to explore partial exchange studies and codify the various literature experiments and theory into a comprehensive whole, enabling formation of ionomer databases. Here, a few representative approaches to selectivity are dissected to identify the points of divergence between them as well as to re-examine the embedded assumptions therein. Lastly, modeling strategies for ion selectivity of the membrane are reviewed to arrive at and correlate fundamental ion properties to ionomer selectivity.

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

Bayesian model averaging for analysis of lattice field theory results

Statistical modeling is a key component in the extraction of physical results from lattice field theory calculations. Although the general models used are often strongly motivated by physics, many model variations can frequently be considered for the same lattice data. Model averaging, which amounts to a probability-weighted average over all model variations, can incorporate systematic errors associated with model choice without being overly conservative. We discuss the framework of model averaging from the perspective of Bayesian statistics, and give useful formulae and approximations for the particular case of least-squares fitting, commonly used in modeling lattice results. In addition, we frame the common problem of data subset selection (e.g. choice of minimum and maximum time separation for fitting a two-point correlation function) as a model selection problem and study model averaging as a straightforward alternative to manual selection of fit ranges. Numerical examples involving both mock and real lattice data are given.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗