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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Nanostructure‐Derived Antireflectivity in Leafhopper Brochosomes

Understanding how insect‐derived biomaterials interact with light has led to new advances and interdisciplinary insights in entomology and physics. Leafhoppers are insects that coat themselves with highly ordered biological nanostructures known as brochosomes. Brochosomes are thought to provide a range of protective properties to leafhoppers, such as hydrophobicity and antireflectivity, which has inspired the development of synthetic brochosomes that mimic their structures. Despite recent progress, the high antireflective properties of brochosome structures are not fully understood. Herein, a combination of experiments and computational modeling is used to understand the structure‐, material‐, and polarization‐dependent optical properties of brochosomes modeled on the geometries found in three leafhopper species. The results qualitatively represent that light interference interaction with nanostructures naturally occurring in brochosomes is responsible for the spectral tuning and the asymmetric line shape of the reflectance spectra. Whereas prior work has focused on the computational modeling of idealized pitted particles, this work shows that light–matter interactions with brochosome structures can be tuned by varying the geometry of their cage‐like nanoscale features and by changing the arrangement of multiparticle assemblies. Broadly, this work establishes principles for the guided design of new optically active materials inspired by these unique insect nanostructures.

Banerjee, Progna↗
DOE OSTI2021

Defying Thermodynamics: Stabilization of Alane Within Covalent Triazine Frameworks for Reversible Hydrogen Storage

The highly unfavorable thermodynamics of direct aluminum hydrogenation can be overcome by stabilizing alane within a nanoporous bipyridine-functionalized covalent triazine framework (AlH 3 @CTF-bipyridine). This material and the counterpart AlH 3 @CTF-biphenyl rapidly desorb H 2 between 95 and 154°C, with desorption complete at 250°C. Sieverts measurements, 27 Al MAS NMR and 27 Al{ 1 H} REDOR experiments, and computational spectroscopy reveal that AlH 3 @CTF-bipyridine dehydrogenation is reversible at 60°C under 700 bar hydrogen, >10 times lower pressure than that required to hydrogenate bulk aluminum. DFT calculations and EPR measurements support an unconventional mechanism whereby strong AlH 3 binding to bipyridine results in single-electron transfer to form AlH 2 (AlH 3 ) n clusters. Here the resulting size-dependent charge redistribution alters the dehydrogenation/rehydrogenation thermochemistry, suggesting a novel strategy to enable reversibility in high-capacity metal hydrides.

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

Defying Thermodynamics: Stabilization of Alane Within Covalent Triazine Frameworks for Reversible Hydrogen Storage

Metastable metal hydrides such as AlH 3 have many attractive features as hydrogen storage media, but generally require complex reaction schemes for regeneration following H 2 release. Here in this paper, we demonstrate that the highly unfavorable thermodynamics of direct aluminum hydrogenation can be overcome by stabilizing alane within a nanoporous bipyridine-functionalized Covalent Triazine Framework (AlH 3 @CTF-bipyridine). This material and the counterpart AlH 3 @CTF-biphenyl rapidly desorb H 2 between 95 and 154 °C, with desorption complete at 250 °C. Sieverts measurements, 27 Al MAS NMR and 27 Al{ 1 H} REDOR experiments, and computational spectroscopy reveal that AlH 3 @CTF-bipyridine dehydrogenation is reversible at 60 °C under 700 bar hydrogen, >10 times lower pressure than that required to hydrogenate bulk aluminum. DFT calculations and EPR measurements support an unconventional mechanism whereby strong AlH 3 binding to bipyridine results in single-electron transfer to form AlH 2 (AlH 3 ) n clusters. The resulting size-dependent charge redistribution alters the dehydrogenation/rehydrogenation thermochemistry, suggesting a novel strategy to enable reversibility in high-capacity metal hydrides.

Coordination Chemistry↗
DOE OSTI2023

Orbital Engineering Mediated by Cation Conjugation in Luminescent Uranyl–Organic Hybrid Materials

A series of compounds of the form [HAr]2[UO2X4] is reported here, wherein Ar is systematically varied between pyridine (1-X), quinoline (2-X), acridine (3-X), 2,5-dimethylpyrazine (4-X), quinoxaline (5-X), and phenazine (6-X), and X = Cl or Br. With greater conjugation in the organic cation, a larger quenching in uranyl luminescence is observed in the solid state. Supporting our luminescence experiments with computation, we map out the potential energy diagrams for the singlet and triplet states of both the [HAr]+ cations and [UO2Cl4]2– anion in the crystalline state, and of the assembly. The distinct energy transfer pathways in each compound are discussed.

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

CheKiPEUQ Intro 1: Bayesian Parameter Estimation Considering Uncertainty or Error from both Experiments and Theory**

A common goal is extraction of physico-chemical parameter values such as pre-exponentials and activation energies from experiment. Ever increasing knowledge from experiments and computations is enabling semi-quantitative prior predictions of such values. When prior knowledge of physically realistic ranges is available, a method named Bayesian parameter estimation (BPE) enables more physically realistic parameter estimation relative to unsophisticated fitting by seeking the most probable value when considering together the uncertainties from prior knowledge, experimental data, and approximations in the model. An impediment to widespread use of BPE is a lack of understanding, training, and user-friendly software. Along with this invited publication, a general software package for BPE is being released that is user-friendly and that does not require understanding of the math behind the methodology. Overall, two previously unpublished catalysis science examples are provided along with considerations and guidelines for successful application of BPE. Following this work, BPE can become more widespread to enable extraction of physically meaningful parameter values.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Gas Phase Hydrolysis and Oxo-Exchange of Actinide Dioxide Cations: Elucidating Intrinsic Chemistry from Protactinium to Einsteinium

Gas-phase bimolecular reactions of metal cations with water provide insights into intrinsic characteristics of hydrolysis. For the actinide dioxide cations, actinyl(V) AnO 2 + , melding of experiment and computation provides insights into trends for hydrolysis, as well as for oxo-exchange between actinyls and water that proceeds by a hydrolysis pathway. Here this line of inquiry is further extended into the actinide series with CCSD(T) computations of potential energy surfaces, for the reaction pathway for oxo-exchange through hydrolysis of nine actinyl(V) ions, from PaO 2 + to EsO 2 + . The computed surfaces are in accord with previous experimental results for oxo-exchange, and furthermore predict spontaneous exchange for CmO 2 + , BkO 2 + , CfO 2 + and EsO 2 + , but not for AmO 2 + . Natural Bond Order analysis of the species involved in both hydrolysis and oxo-exchange reveals an inverse correlation between the barrier to hydrolysis and the charge on the actinide centre, q(An). Based on this correlation, it can be concluded that hydrolysis, and related phenomena such as oxo-exchange, become less favourable as the charge on the metal centre decreases. Finally, the new results provide a straightforward rationalization of trends across a wide swathe of the actinide series.

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

${\Lambda}_c^{+}$ polarimetry using the dominant hadronic mode

The polarimeter vector field for multibody decays of a spin-half baryon is intro duced as a generalisation of the baryon asymmetry parameters. Using a recent amplitude analysis of the ${\Lambda}_c^{+}$ → $pK^-π^+$ decay performed at the LHCb experiment, we compute the distribution of the kinematic-dependent polarimeter vector for this process in the space of Mandelstam variables to express the polarised decay rate in a model-agnostic form. The obtained representation can facilitate polarisation measurements of the ${\Lambda}_c^{+}$ baryon and eases inclusion of the ${\Lambda}_c^{+}$ → $pK^-π^+$ decay mode in hadronic amplitude analyses.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Baryon Anticorrelations in PYTHIA

Abstract We present a computational investigation of a problem of hadron collisions from recent years, that of baryon anticorrelations. This is an experimental dearth of baryons near other baryons in phase space, not seen upon examining numerical Monte Carlo simulations. We have addressed one of the best known Monte Carlo codes, , to see what baryon (anti)correlations it produces, where they are originated at the string-fragmentation level in the underlying Lund model, and what simple modifications could lead to better agreement with data. We propose two ad-hoc alterations of the fragmentation code, a “one-baryon” and an “always-baryon” policies that qualitatively reproduce the data behaviour, i.e anticorrelation, and suggest that lacking Pauli-principle induced corrections at the quark level could be the culprit behind the current disagreement between computations and experiment.

Physics↗
DOE OSTI2022

A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures

Abstract Part quality manufactured by the laser powder bed fusion process is significantly affected by porosity. Existing works of process–property relationships for porosity prediction require many experiments or computationally expensive simulations without considering environmental variations. While efforts that adopt real-time monitoring sensors can only detect porosity after its occurrence rather than predicting it ahead of time. In this study, a novel porosity detection-prediction framework is proposed based on deep learning that predicts porosity in the next layer based on thermal signatures of the previous layers. The proposed framework is validated in terms of its ability to accurately predict lack of fusion porosity using computerized tomography (CT) scans, which achieves a F1-score of 0.75. The framework presented in this work can be effectively applied to quality control in additive manufacturing. As a function of the predicted porosity positions, laser process parameters in the next layer can be adjusted to avoid more part porosity in the future or the existing porosity could be filled. If the predicted part porosity is not acceptable regardless of laser parameters, the building process can be stopped to minimize the loss.

42 ENGINEERING↗
DOE OSTI2023

Bayesian projection pursuit regression

In projection pursuit regression (PPR), a univariate response variable is approximated by the sum of $M$ “ridge functions,” which are flexible functions of one-dimensional projections of a multivariate input variable. Traditionally, optimization routines are used to choose the projection directions and ridge functions via a sequential algorithm, and $M$ is typically chosen via cross-validation. Here, we introduce a novel Bayesian version of PPR, which has the benefit of accurate uncertainty quantification. To infer appropriate projection directions and ridge functions, we apply novel adaptations of methods used for the single ridge function case ($M$=1), called the Bayesian Single Index Model; and use a Reversible Jump Markov chain Monte Carlo algorithm to infer the number of ridge functions $M$. We evaluate the predictive ability of our model in 20 simulated scenarios and for 23 real datasets, in a bake-off against an array of state-of-the-art regression methods. Finally, we generalize this methodology and demonstrate the ability to accurately model multivariate response variables. Its effective performance indicates that Bayesian Projection Pursuit Regression is a valuable addition to the existing regression toolbox.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Numerical Prediction of Oscillation Behaviors of a Multiphase Core–Shell Droplet During Interfacial Tension Measurement

Interfacial tension between molten high-temperature materials is to be measured using the electrostatic levitation furnace (ELF) and the electromagnetic levitator (ISS-EML) aboard the International Space Station. A levitated compound droplet of a concentric core–shell structure is excited either by an impulsive electromagnetic field or by a superimposed electrostatic field. The oscillation behavior of the compound droplet is analyzed to measure interfacial tension at the interface of the two phases. In support of the space experiments, a computational fluid dynamic model was developed to characterize the oscillation behavior of multiphase core–shell droplet. The developed model predicted the interfacial tension between molten copper-rich and cobalt-rich phases with a difference of 3.3 pct compared to the values reported in literature. The developed model is being utilized to investigate the influence of various test parameters on measured surface tension and also being extended for molten steel–molten slag systems.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Concurrent Precipitation of Nb(C,N) and Metastable M 23 C 6 in Alloy 347H at 700°C and 750°C: Computer Simulations and Comparison to Experiment

Here, we present our results for the concurrent precipitation of metastable M 23 C 6 , Nb(C,N) secondary precipitates, and the Nb(C,N) primary crystals in 347H austenitic stainless steel. For precipitation modeling, we have accounted for the elastic contribution to interfacial energy, and for the Fe-spin-polarization for NbC/Fe and M 23 C 6 /Fe interfacial energy values: for NbC/Fe ~ 0.63 J/m 2 . For M 23 C 6 precipitates, an error function was used to describe the interfacial energy growth with particle size. In precipitation simulations, the average size of the primary Nb(C,N) particles remained ~ 1 μm at 700°C and ~ 0.3 μm at 750°C. The M 23 C 6 precipitates at 750°C dissolved after 120 h (our simulations) compared to 300 h (experiments). The Nb(C,N)/Fe interfacial energy was not affected by the nitrogen additions. With these modifications, reasonable agreement with the available experimental data was obtained, which allows using them in the development of the 2nd-phase particle-informed creep theory.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Realizing efficient operations of Ni-cermet-based fuel cells on hydrocarbons via an in situ self-assembled metal/oxide nano-heterostructured catalyst

Operating Ni-cermet-based fuel cells on hydrocarbons is charming by largely hindered by poor coking tolerance and severe deterioration. Here, in this study, we report an effective metal/oxide nano-heterostructured catalyst with a nominal composition of Pr 0.95 Ru 0.05 O 2-δ (PRO), which is in situ self-assembled to a Pr 0.95 Ru 0.05-x O 2-δ oxide frame coated with Ru metallic nanoparticles (denoted as Ru/PRO) under the operation condition. When applied to the Ni-cermet (Ni-YSZ) anodes, the cells achieve decent peak power densities of 1.784 and 1.870 W cm -2 on CH 4 and C 3 H 8 with only 3% H 2 O at 750 °C, respectively. Moreover, the cells with Ru/PRO-coated anode demonstrate excellent durability when operated on CH 4 for ~ 220 h and C 3 H 8 for ~115 h. It is demonstrated that the Ru/PRO generates hydroxyl species that react with carbon species, followed by the formation of COH intermediates on Ni anode surfaces for removing the coking, as confirmed by experiments and computations.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

High-throughput validation of phase formability and simulation accuracy of Cantor alloys

High-throughput methods enable accelerated discovery of novel materials in complex systems such as high-entropy alloys, which exhibit intricate phase stability across vast compositional spaces. Computational approaches, including Density Functional Theory (DFT) and calculation of phase diagrams (CALPHAD), facilitate screening of phase formability as a function of composition and temperature. However, the integration of computational predictions with experimental validation remains challenging in high-throughput studies. In this work, we introduce a quantitative confidence metric to assess the agreement between predictions and experimental observations, providing a quantitative measure of the confidence of machine learning models trained on either DFT or CALPHAD input in accounting for experimental evidence. The experimental dataset was generated via high-throughput in-situ synchrotron X-ray diffraction on compositionally varied FeNiMnCr alloy libraries, heated from room temperature to ~1000 °C. Agreement between the observed and predicted phases was evaluated using either temperature-independent phase classification or a model that incorporates a temperature-dependent probability of phase formation. This integrated approach demonstrates where strong overall agreement between computation and experiment exists, while also identifying key discrepancies, particularly in FCC/BCC predictions at Mn-rich regions to inform future model refinement.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

Neutron spin echo shows pHLIP is capable of retarding membrane thickness fluctuations

In this study, cell membranes are responsible for a range of biological processes that require interactions between lipids and proteins. While the effects of lipids on proteins are becoming better understood, our knowledge of how protein conformational changes influence membrane dynamics remains rudimentary. Here, we performed experiments and computer simulations to study the dynamic response of a lipid membrane to changes in the conformational state of pH-low insertion peptide (pHLIP), which transitions from a surface-associated (SA) state at neutral or basic pH to a transmembrane (TM) α-helix under acidic conditions. Our results show that TM-pHLIP significantly slows down membrane thickness fluctuations due to an increase in effective membrane viscosity. Our findings suggest a possible membrane regulatory mechanism, where the TM helix affects lipid chain conformations, and subsequently alters membrane fluctuations and viscosity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Deterioration of concrete due to ASR: Experiments and multiscale modeling

The process of ASR (Alkali-Silica Reaction) induced expansion and damage in pavement concrete specimens is investigated using laboratory experiments and computational modeling. In the experimental program, the concrete specimens are subject to CS-CPT (climate simulation concrete prism test) to obtain ASR induced expansion with and without external supply of alkali. The dissolution rates of the granodiorite used in the concrete mix and the gel formation rates are determined under concrete-like conditions (pH 13.8, with/without Ca(OH){sub 2} and NaCl) at different temperatures. A micromechanics based computational model with aggregate-scale diffusion and reaction kinetics coupled to an Eigenstrain based micromechanics damage model is developed for the simulation of ASR induced expansion and damage. Data from the experimental program are used to calibrate and validate the computational model. Model predictions show that for the given concrete mixture, ASR induced expansion in the specimen exposed to water is predominantly governed by microcracking in the aggregate, while the expansion in the specimen subjected to external alkali supply is governed by microcracking in both the aggregates and the cement paste.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Latent map Gaussian processes for mixed variable metamodeling

Gaussian processes (GPs) are ubiquitously used in sciences and engineering as metamodels. Standard GPs, however, can only handle numerical or quantitative variables. Here we introduce latent map Gaussian processes (LMGPs) that inherit the attractive properties of GPs and are also applicable to mixed data which have both quantitative and qualitative inputs. The core idea behind LMGPs is to learn a continuous, low-dimensional latent space or manifold which encodes all qualitative inputs. To learn this manifold, we first assign a unique prior vector representation to each combination of qualitative inputs. We then use a low-rank linear map to project these priors on a manifold that characterizes the posterior representations. As the posteriors are quantitative, they can be directly used in any standard correlation function such as the Gaussian or Matern. Hence, the optimal map and the corresponding manifold, along with other hyperparameters of the correlation function, can be systematically learned via maximum likelihood estimation. Through a wide range of analytic and real-world examples, we demonstrate the advantages of LMGPs over state-of-the-art methods in terms of accuracy and versatility. In particular, we show that LMGPs can handle variable-length inputs, have an explainable neural network interpretation, and provide insights into how qualitative inputs affect the response or interact with each other. We also employ LMGPs in Bayesian optimization and illustrate that they can discover optimal compound compositions more efficiently than conventional methods that convert compositions to qualitative variables via manual featurization.

42 ENGINEERING↗