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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 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A comparative study of calibration techniques for finite strain elastoplasticity: Numerically-exact sensitivities for FEMU and VFM

Accurate identification of material parameters is crucial for predictive modeling in computational mechanics. Here, the two primary approaches in the experimental mechanics community for calibration from full-field digital image correlation data are known as finite element model updating (FEMU) and the virtual fields method (VFM). In VFM, the objective function is a squared mismatch between internal and external virtual work or power. In FEMU, the objective function quantifies the weighted mismatch between model predictions and corresponding experimentally measured quantities of interest. It is minimized by iteratively updating the parameters of an FE model. While FEMU is seen as more flexible, VFM is commonly used instead of FEMU due to its considerably greater computational expense. However, comparisons between the two methods usually involve approximations of gradients or sensitivities with finite difference schemes, thereby making direct assessments difficult. Hence, in this study, we compare VFM and FEMU in the context of numerically-exact sensitivities obtained through local sensitivity analyses and the application of automatic differentiation software. To this end, we conduct a series of test cases to assess both methods under practical challenges using a finite strain elastoplasticity model.

Automatic differentiation↗
DOE OSTI2023

Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons

Neural networks (NNs) are currently changing the computational paradigm on how to combine data with mathematical laws in physics and engineering in a profound way, tackling challenging inverse and ill-posed problems not solvable with traditional methods. However, quantifying errors and uncertainties in NN-based inference is more complicated than in traditional methods. This is because in addition to aleatoric uncertainty associated with noisy data, there is also uncertainty due to limited data, but also due to NN hyperparameters, overparametrization, optimization and sampling errors as well as model misspecification. Although there are some recent works on uncertainty quantification (UQ) in NNs, there is no systematic investigation of suitable methods towards quantifying the total uncertainty effectively and efficiently even for function approximation, and there is even less work on solving partial differential equations and learning operator mappings between infinite-dimensional function spaces using NNs. In this work, we present a comprehensive framework that includes uncertainty modeling, new and existing solution methods, as well as evaluation metrics and post-hoc improvement approaches. Further, to demonstrate the applicability and reliability of our framework, we present an extensive comparative study in which various methods are tested on prototype problems, including problems with mixed input-output data, and stochastic problems in high dimensions. In the Appendix, we include a comprehensive description of all the UQ methods employed. Further, to help facilitate the deployment of UQ in Scientific Machine Learning research and practice, we present and develop in [1] an open-source Python library (github.com/Crunch-UQ4MI/neuraluq), termed NeuralUQ, that is accompanied by an educational tutorial and additional computational experiments.

11 physics-informed neural networks↗
DOE OSTI2020

Hydrogen Bonding Optimizes Singlet Fission in Carboxylic Acid Functionalized Anthradithiophene Films

The rate of singlet fission, the process of generating two triplet excitons with photoexcitation of one singlet exciton, depends on a combination of singlet/triplet energy balance and intermolecular coupling. In this work, we perform carboxylic acid functionalization of anthradithiophene (ADT) derivatives that result in hydrogen bonds that drive molecular orientation and strong electronic coupling of polycrystalline ADT thin films, leading to ultrafast singlet fission without significant enthalpic driving force. ADT with a single carboxylic acid group exhibits weak intermolecular coupling and slow and inefficient singlet fission, much like the parent ADT, and substitution of different alkylsilyl solubilizing groups has little effect. However, the addition of two carboxylic acid groups on either end of the long axis favors significant coupling and crystallinity in as-deposited thin films that increase the effective singlet fission rate by roughly three orders of magnitude. The properties of the triplet pair, particularly its propensity to form long-lived independent triplets, are also influenced by the degree of long-range intermolecular coupling. The enhancement of intermolecular coupling specific to singlet fission using the ubiquitous cyclic hydrogen bonding motif could impact triplet pair utilization schemes in a variety of contexts.

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

Anion Exchange Ionomers: Impact of Chemistry on Thin-Film Properties

Ionomer thin-films (i.e., 20–100 nm) on supports serve as model systems to understand ionomer-catalyst interfacial behavior as well as the confinement-driven deviation in properties from bulk membranes. While ionomer thin-films have been examined for proton exchange ionomers, the thin-film properties of anion exchange ionomers (AEIs) remain largely unexplored. More importantly, delineating the convoluted impact of chemistry and confinement on thin-film morphology and hydration is of interest to advancing the field on functional ionic interfaces. In this work, these aspects are studied by using AEIs of different backbones (perfluorinated, aliphatic, and aromatic) and side chains (various lengths, and single versus dual functional groups). Quartz-crystal microbalance and spectroscopic ellipsometry are used to analyze density and coupled with calculated free volume fraction of thin-films to provide insights on their gas transport properties. AEI side-chain's chemical character plays a key role in how confinement modulates hydration (in thin-film versus bulk). Overall, the results elucidate the effects of backbone, side-chain chemistry versus anion/cation type in the confinement-driven changes in thin-film morphology and swelling. This study also provides new insights for tuning AEI transport functionalities at interfaces via chemistry, which can benefit the design and development of electrode-ionomers for alkaline membrane-based energy systems.

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

The boundary entropy function for interface conformal field theories

In 1+1 dimensional conformal field theory with a boundary the boundary contribution to the entanglement entropy is determined by a single number g effectively counting the boundary degrees of freedom. In contrast, in 1+1 dimensional interface CFTs the corresponding quantity is a non-trivial function depending on the position of the interval relative to the interface, giving access to much more detailed information about the defect. In this work we determined this g -function in several examples using holography and derive some of its basic properties from holography and strong subadditivity.

AdS-CFT correspondence↗
DOE OSTI2022

Density functional thermodynamic description of spin, phonon and displacement degrees of freedom in antiferromagnetic-to-paramagnetic phase transition in YNiO 3

This work herein demonstrates a direct density functional description of the finite-temperature thermodynamic properties of solids exhibiting phase transitions through positional and spin symmetry breaking degrees of freedom. A classic example addressed here is the rare-earth (R) nickelates RNiO 3 where the ground state is characterized by crystallographic and magnetic (e.g., antiferromagnetic) long-range order (LRO), whereas the higher temperature paramagnetic phase manifests a range of local spin and positional symmetry breaking motifs with short-range order (SRO). Unlike time-dependent simulations of spin and positional degrees of freedom, in the present work, phases are described via a superposition of static configurations constructed by populating a periodic base lattice supercell allowing for the formation of energy lowing distribution of positional and spin local motifs. The thermal populations of the configurations in such a superposition phase are obtained from the energy-minimized Density Functional Theory (DFT)-calculated partition functions at different temperatures. This approach offers flexible inclusion of different physical contributions to the free energy, such as elastic, electronic and phonon free energies, all obtained from the same underlying DFT total energy calculations of periodic structures. The thermodynamic and magnetic properties of both LRO and SRO crystallographic and spin phases, including antiferromagnetic (AFM) to paramagnetic (PM) Néel phase transition in YNiO3 are studied. Including spin and phonon contributions, we find a DFT-calculated Néel temperature to be 144 K in satisfactory agreement with the experimental value of 145 K; whereas omitting the phonon contribution, one obtains a Néel temperature of 81 K. We present phonon contributions to the DFT-calculated temperature-dependent SRO, heat capacities, and the polymorphous distribution of nonzero local magnetic moments in the PM phase. This approach thus extends to finite temperatures the symmetry-broken DFT description of both the AFM and PM phases, demonstrating that a thermodynamic superposition approach based on symmetry broken configurations evaluated by a mean-field like DFT is sufficient to obtain a consistent description of the thermal physics of the AFM, PM phases and their interconversion in 3d oxides illustrated by YNiO 3 .

36 MATERIALS SCIENCE↗
DOE OSTI2018

Mechanistic insights into hydrodeoxygenation of phenol on bimetallic phosphide catalysts

Catalytic hydrodeoxygenation (HDO) of phenolics is a necessary step for upgrading bio-oils to transportation fuels. Bimetallic catalysts offer the potential of increased activities and selectivities for desired products. Adding non-metallic elements, such as phosphorous, allows for charge distribution between the metal and nonmetal atoms, which improves Lewis acid character of catalytic surfaces. This work utilizes experimental and density functional theory (DFT) based calculations to identify potential C–O bond cleavage pathways and product selectivities for HDO reactions on FeMoP, RuMoP, and NiMoP catalysts. Our work demonstrates that FeMoP catalyst favors direct deoxygenation pathway due to a lower activation energy barrier for C–O bond cleavage whereas RuMoP and NiMoP catalysts promote ring hydrogenation first, followed by the cleavage of C–O bond. The Bader charge analysis indicates that for these catalytic systems Moδ+ site bears a large positive charge which acts as a Lewis acid site for HDO reactions. Altogether, we find that trends in the experimental product selectivities are in good agreement with that predicted with DFT calculations.

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

Density Functional Tight Binding Insights into Plasmonic Silver–Platinum Nanoparticles and Alloys for Enhanced Photocatalysis

Developing accurate and efficient Slater-Koster (SK) tight-binding parameter sets is essential for quantum plasmonic studies of alloyed metal nanoparticles, as conventional time dependent density functional theory (TD-DFT) calculations are computationally prohibitive for larger clusters. In this work, we develop and validate density functional tight binding (DFTB) parameter sets for both ground state (GS-SK) and excited state (ES-SK) calculations to study the structural, electronic, and optical properties of silver (Ag), platinum (Pt), and Ag–Pt nanoalloys. Our investigation of the ground state properties demonstrates that the GS-SK parameters enable DFTB to closely reproduce the electronic structures of platinum clusters with diverse sizes and geometries – showing qualitative agreement with DFT for density of states (DOS) profiles and energy levels. The ES-SK parameters accurately describe excited-state properties compared to TD-DFT reference calculations, including the broad, featureless absorption profiles of Pt that are dominated by interband transitions. Using the ES-SK parameters within a real-time TD-DFTB framework, we compute size-dependent optical absorption spectra of Ag, Pt and Ag-Pt nanocubes containing up to 1099 atoms (size ∼4.18 nm). A detailed study of Ag–Pt and Pt-Ag core–shell nanoparticles shows quenching of the Ag plasmon resonance even at monolayer coverage for Ag-Pt, but not for Pt-Ag. We also show how to define submonolayer Ag-core Pt-shell cubic structures that have similar optical properties to those generated experimentally for much larger particles, which offers potential for describing plasmon-enhanced photocatalysis. Collectively, the GS-SK and ES-SK parameter sets provide an accurate, computationally efficient approach for modeling the complex optical and electronic behavior of noble–transition metal nanostructures and their alloys.

SPR↗
DOE OSTI2025

Identification and Classification of Fungal GPCR Gene Families

G protein-coupled receptors (GPCRs) are transmembrane proteins crucial for signal transduction in eukaryotes, responding to diverse extracellular signals. Researchers have found and systematically summarized 14 distinct types of GPCRs in fungi but their distribution among numerous fungal species remained largely unexamined. Additionally, three families of mammalian homologs (Rhodopsin, Glutamate, and Frizzled) have been found in previous studies, but they are not included in the systematic classification of fungal GPCRs. Our study establishes a unified classification of 17 GPCR classes in fungi, combining 14 fungal and 3 mammalian previously recognized groups, and classifies 28,294 GPCRs across 1357 fungal species, significantly expanding the scale of GPCRs in fungi and demonstrating their broader distribution. We found that mammalian homologs are notably more prevalent in Early Diverging Fungi (EDF), whereas the previous 14 classes are predominantly found in Ascomycota and Basidiomycota. The most abundant class detected in fungi was Pth11-like GPCRs, exclusively found in Pezizomycotina and involved in fungal pathogenicity. Our analysis suggested that Pezizomycotina ancestor possessed an extensive array of Pth11-like GPCRs, but over time, some species underwent considerable reductions in these GPCRs in conjunction with genome contractions. Utilizing a custom-built convolutional neural network (CNN) for the identification of fungal GPCRs, we identified several putative novel fungal GPCRs. Predicted interactions between these prospective new GPCRs and G-alpha proteins, as simulated by AlphaFold Multimer, provided additional support for their functional relevance. In conclusion, our work defines the first large-scale, unified classification of fungal GPCRs, reveals lineage-specific expansions and contractions, and uncovers previously unrecognized GPCR candidates with potential functional roles in fungal signaling.

G protein-coupled receptors↗
DOE OSTI2025

Lattice Structure and Dynamics of Sparse Molecular Crystals: OsO 4 and RuO 4

OsO 4 and RuO 4 are molecular oxides with unique tetrameric structures and rare +8 oxidation states. Accurately modeling their properties remains challenging for density functional theory (DFT) due to weak intertetramer interactions, which standard functionals fail to capture. Here, in this work, we show that the van der Waals (vdW)-corrected density functional (vdW-DF-optB86b) provides structural parameters that are much closer to experimental values than the standard generalized gradient approximation, with volume predictions that fall within the experimentally observed range. Phonon band structure analysis shows that the inclusion of vdW interactions stabilizes soft phonon modes, highlighting the importance of dispersion corrections for accurate predictions of lattice dynamics. Experimental measurements of the phonon density of states for OsO 4 , obtained via inelastic neutron scattering, demonstrate good agreement with our vdW-DF-optB86b calculations. These results validate OsO 4 and RuO 4 as valuable benchmarks for structural and vibrational calculations via vdW-corrected DFT methods and offer insights for studying the broader class of sparse molecular materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Dual Modification of MOFs Improves Dispersion and Ionic Conductivity of Mixed Matrix Membranes

Nonaqueous redox flow batteries (NARFBs) are a promising class of energy storage devices, but the lack of a chemically stable, conductive membrane that exhibits size-selectivity over redox-active species prevents their broader implementation. Recently, metal–organic frameworks (MOFs) have been implemented into mixed-matrix membranes (MMMs) for NARFBs, but the effects of the MOF linker functionality on membrane properties are not well-understood. In this work, we develop a series of MOF-based MMMs by blending postsynthetically modified variants of UiO-66-NH2 with poly(ethylene-co-vinyl acetate). The modification of UiO-66-NH2 with sulfate groups initially resulted in poor dispersion throughout the MMMs, but when dual-modified with poly(N-isopropylacrylamide), MOF dispersion throughout the MMM was improved, and ionic conductivity was significantly higher than the UiO-66-NH2 MMMs. Furthermore, the dual-modified MMMs demonstrated excellent size-selectivity by blocking redox active species transport. This work demonstrates a synergy between the MOF functional groups to improve MMM properties critical for the development of practical NARFBs.

Carey, Cassidy A.↗
DOE OSTI2023

IDAES-PSE 2.2.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. New features in the 2.2 release include: DiagnosticsToolbox DiagnosticsToolbox (idaes.core.util.model_diagnostics. DiagnosticsToolbox is a new self-guiding toolbox for assisting with diagnosing modeling issues. The toolbox contains a wide range of tools for identifying common modeling issues and conveying these to the user, and more features will be added in the coming months. To try the new toolbox, use the following steps: from idaes.core.util import DiagnosticsToolbox Build an instance of your model Ensure your models has zero degrees of freedom (as best you can) dt = DiagnosticsToolbox(model) dt.report_structural_issues() Follow the prompts Deprecations DegeneracyHunter is being deprecated in favor of the new DiagnosticsToolbox. The features and capabilities of DegeneracyHunter will be moved over to the DiagnositcsToolbox in the coming months. Removal of usage of Pyomo's expr.current due to deprecation upstream. Removed support for radial basis functions in ALAMOpy as these did not work in the current implementation. Those wishing to use radial basis functions should use Pysmo instead. New Capabilities MSContactor model for unit operations involving mass transfer between 2 or more streams, such as membrane, solvent extraction and solids leaching operations. Other Improvements Improved formulation for solubility products with additional parameters for better scaling of terms Better backward compatibility, verification and robustness testing of core models and APIs Added support for Separator models without energy balances

DiagnosticsToolbox↗
DOE OSTI2024

Optimized nuclear energy density functionals including long-range pion contributions

Nuclear energy density functionals successfully reproduce properties of nuclei across almost the entire nuclear chart. However, nearly all available functionals are phenomenological in nature and lack a rigorous connection to systematically improvable nuclear forces. This issue might be solved with an energy density functional obtained from first principles. As an intermediate step towards this goal we construct the GUDE family of functionals that is obtained from a hybrid scheme consisting of long-range pion-exchange contributions derived from chiral effective field theory at the Hartree-Fock level and a phenomenological Skyrme part. When including pion contributions beyond next-to-leading order in the chiral expansion, we find significant improvements over a reference Skyrme functional constructed following the same protocol. We analyze the importance of different pion contributions and identify which terms drive the observed improvements. Since pions are incorporated without adding further optimization parameters to the functionals, the improvements can be attributed to the functional form of these terms. Our work therefore suggests that the considered chiral contributions constitute useful ingredients for true energy density functionals. Published by the American Physical Society 2024

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

Performance of Functionally Graded Thermoelectric Materials and Devices: A Review

Direct energy conversion using thermoelectric generators (TEGs) is a research area of growing interest because of its potential for increasing energy efficiency. Bulk thermoelectric modules are used widely in industry as Peltier cooling devices. Currently, only bismuth telluride modules are commercially available for power generation. Significant efforts have been put into exploring promising materials and techniques to improve the figure of merit ( zT ) at laboratory scale (5–20 g). A variety of techniques have been investigated to improve the output and useful temperature range for common industrial TEGs made from bulk polycrystalline materials including segmentation, geometric pinning, and property gradients. However, the improvement in zT at device level (500–1000 g and up) is exceptionally limited. In addition, the thermal degradation of TEGs occurs when cracks form due to thermal stresses that arise from transient heat sources, which consequently lead to a decreased lifetime. Functionally graded material (FGM) thermoelectrics in bulk and polycrystalline form have been developed to mitigate some of these issues by improving the temperature bandwidth, current output range, and lifetime. The present work provides a review of functionally graded TEGs, including their manufacturing, usage and current techniques for improving their performance. Furthermore, this article also provides a pathway to additional research and approaches for improving the efficiency and temperature range, as well as reducing the property degradation of bulk polycrystalline TEGs.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Shape functions and singly differential cross sections of bremsstrahlung at electron energies from 10 eV to 3 MeV for Z = 1–100

This work describes a library of shape functions and singly differential cross sections of bremsstrahlung due to interaction of the electrons with energies from 10 eV to 3 MeV with neutral atoms. The calculations have been performed using an updated version of the set of codes BREMS, which was described in a previous publication. The calculation method is based on the relativistic partial-wave formulation describing interaction of the incident electron with a central potential of the neutral target atom. The interaction potential is obtained from the Kohn–Sham density functional theory. The Dirac equation for the continuum wave function is solved by the power-series method. The mentioned library uses a comparatively dense grid of the incident electron energy (ten points per decade), and it covers all chemical elements with atomic numbers from 1 to 100. The agreement of the present data with the previously-published tabulations is satisfactory.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Performance of Microreactor Test Article with Embedded Sensors During Testing in The Single Primary Heat Extraction and Removal Emulator

The nuclear industry is pursuing microreactors that can be factory assembled and deployed to remote regions for reliable power generation. One class of microreactors uses a monolithic metal core block coupled to heat pipes that use passive heat flow, increasing the surface area for heat transfer without requiring active coolant flow through the reactor core. Additional experimental testing is required to understand the heat rejection limitations of heat pipes and thermal stresses in the monolithic core block due to significant temperature gradients. This report describes the initial characterization and testing of a stainless-steel test article that was fabricated with embedded sensors to measure heat pipe performance limits, as well as spatially distributed temperatures and strains during electrically heated thermal testing to simulate nuclear heating. The electrically heated testing was performed in the Single Primary Heat Extraction and Removal Emulator facility located at Idaho National Laboratory. The ultimate goals of this work are to (1) accurately monitor temperature and strain distributions that result from differential thermal expansion in the test articles and (2) quantify the heat rejection limits of heat pipes as a function of operating temperature and working fluid during steady-state and transient operations. Initial tests focused on the feasibility of using advanced fiber-optic sensors and other sensor technologies to improve the understanding of temperature and strain distributions within the electrically heated experiments. However, these sensing capabilities could benefit the broader microreactor community if the sensors could be used to monitor component and system health during nuclear operations to inform a limited number of microreactor operators, ultimately reducing operation and maintenance costs and moving toward semiautonomous operation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗