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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

GenMod: A generative modeling approach for spectral representation of PDEs with random inputs

Here, we propose a method for quantifying uncertainty in high-dimensional PDE systems with random parameters, where the number of solution evaluations is small. Parametric PDE solutions are often approximated using a spectral decomposition based on polynomial chaos expansions. For the class of systems we consider (i.e., high dimensional with limited solution evaluations) the coefficients are given by an underdetermined linear system in a regression formulation. This implies additional assumptions, such as sparsity of the coefficient vector, are needed to approximate the solution. Here, we present an approach where we assume the coefficients are close to the range of a generative model that maps from a low to a high dimensional space of coefficients. Our approach is inspired be recent work examining how generative models can be used for compressed sensing in systems with random Gaussian measurement matrices. Using results from PDE theory on coefficient decay rates, we construct an explicit generative model that predicts the polynomial chaos coefficient magnitudes. The algorithm we developed to find the coefficients, which we call GenMod, is composed of two main steps. First, we predict the coefficient signs using Orthogonal Matching Pursuit. Then, we assume the coefficients are within a sparse deviation from the range of a sign-adjusted generative model. This allows us to find the coefficients by solving a nonconvex optimization problem, over the input space of the generative model and the space of sparse vectors. We obtain theoretical recovery results for a Lipschitz continuous generative model and for a more specific generative model, based on coefficient decay rate bounds. We examine three high-dimensional problems and show that, for all three examples, the generative model approach outperforms sparsity promoting methods at small sample sizes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Learning stochastic dynamics with statistics-informed neural network

We introduce a machine-learning framework named statistics-informed neural network (SINN) for learning stochastic dynamics from data. This new architecture was theoretically inspired by a universal approximation theorem for stochastic systems, which we introduce in this paper, and the projection-operator formalism for stochastic modeling. Here, we devise mechanisms for training the neural network model to reproduce the correct statistical behavior of a target stochastic process. Numerical simulation results demonstrate that a well-trained SINN can reliably approximate both Markovian and non-Markovian stochastic dynamics. We demonstrate the applicability of SINN to coarse-graining problems and the modeling of transition dynamics. Furthermore, we show that the obtained reduced-order model can be trained on temporally coarse-grained data and hence is well suited for rare-event simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Multi-frequency progressive refinement for learned inverse scattering

Interpreting scattered acoustic and electromagnetic wave patterns is a computational task that enables remote imaging in a number of important applications, including medical imaging, geophysical exploration, sonar and radar detection, and nondestructive testing of materials. However, accurately and stably recovering an inhomogeneous medium from far-field scattered wave measurements is a computationally difficult problem, due to the nonlinear and non-local nature of the forward scattering process. We design a neural network, called Multi-Frequency Inverse Scattering Network (MFISNet), and a training method to approximate the inverse map from far-field scattered wave measurements at multiple frequencies. We consider three variants of MFISNet, with the strongest performing variant inspired by the recursive linearization method — a commonly used technique for stably inverting scattered wavefield data — that progressively refines the estimate with higher frequency content. MFISNet outperforms past methods in regimes with high-contrast, heterogeneous large objects, and inhomogeneous unknown backgrounds.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

An explicit, energy-conserving particle-in-cell scheme

We present an explicit temporal discretization of particle-in-cell schemes for the non-relativistic Vlasov equation that results in exact energy conservation when combined with an appropriate spatial discretization. The scheme is inspired by a simple, second-order explicit scheme that conserves energy exactly in the Eulerian context. We show that direct translation to particle-in-cell does not result in strict conservation, but derive a simple correction based on an analytically solvable optimization problem that recovers conservation. While this optimization problem is not guaranteed to have a real solution for every particle, we provide a correction that makes imaginary values extremely rare and still admits $\mathcal{O}$(10 –12 ) fractional errors in energy for practical simulation parameters. We present the scheme in both electrostatic – where we use the Ampère formulation – and electromagnetic contexts. With an electromagnetic field solve, the field update is most naturally linearly implicit, but the more computationally intensive particle update remains fully explicit. Here, we also show how the scheme can be extended to use the fully explicit leapfrog and pseudospectral analytic time-domain (PSATD) field solvers. The scheme is tested on standard kinetic plasma problems, confirming its conservation properties.

Energy conservation↗
DOE OSTI2022

Bimetallic NiCo boride nanoparticles confined in a MXene network enable efficient ambient ammonia electrosynthesis

Ambient electrocatalytic nitrogen fixation is an emerging technology for green ammonia synthesis, but the absence of optimized, stable and performant catalysts can render its practical application challenging. Herein, bimetallic NiCo boride nanoparticles confined in MXene are shown to accomplish high-performance nitrogen reduction electrolysis. Taking advantage of the synergistic effect in specific compositions with unique electronic d and p orbits and typical architecture of rich nanosized particles embedded in the interconnected conductive network, the synthesized MXene@NiCoB composite demonstrates extensive improvements in nitrogen molecule chemisorption, active area exposure and charge transport. As a result, optimal NH 3 yield rate of 38.7 μg h -1 mgcat. -1 and Faradaic efficiency of 6.92% are acquired in 0.1 M Na 2 SO 4 electrolyte. Moreover, the great catalytic performance can be almost entirely maintained in the cases of repeatedly-cycled and long-term electrolysis. Theoretical investigations reveal that the nitrogen reduction reaction on MXene@NiCoB catalyst proceeds according to the distal pathway, with a distinctly-reduced energy barrier relative to the Co 2 B counterpart. In conclusion, this work may inspire a new route towards the rational catalyst design for the nitrogen reduction reaction.

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

Spectroscopic and computational investigations of organometallic complexation of group 12 transition metals by methanobactins from Methylocystis sp. SB2

Methanotrophic bacteria catalyze the aerobic oxidation of methane to methanol using Cu-containing enzymes, thereby exerting a modulating influence on the global methane cycle. To facilitate the acquisition of Cu ions, some methanotrophic bacteria secrete small modified peptides known as “methanobactins,” which strongly bind Cu and function as an extracellular Cu recruitment relay, analogous to siderophores and Fe. In addition to Cu, methanobactins form complexes with other late transition metals, including the Group 12 transition metals Zn, Cd, and Hg, although the interplay among solution-phase configurations, metal interactions, and the spectroscopic signatures of methanobactin-metal complexes remains ambiguous. In this study, the complexation of Zn, Cd, and Hg by methanobactin from Methylocystis sp. strain SB2 was studied using a combination of absorbance, fluorescence, extended x-ray absorption fine structure (EXAFS) spectroscopy, and time-dependent density functional theory (TD-DFT) calculations. We report changes in sample absorbance and fluorescence spectral dynamics, which occur on a wide range of experimental timescales and characterize a clear stoichiometric complexation dependence. Mercury L3-edge EXAFS and TD-DFT calculations suggest a linear model for Hg--S coordination, and TD-DFT suggests a tetrahedral model for Zn 2+ and Cd 2+ . We observed an enhancement in the fluorescence of methanobactin upon interaction with transition metals and propose a mechanism of complexation-hindered isomerization drawing inspiration from the wild-type Green Fluorescent Protein active site. Collectively, our results represent the first combined computational and experimental spectroscopy study of methanobactins and shed new light on molecular interactions and dynamics that characterize complexes of methanobactins with Group 12 transition metals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Visualizing the gas channel of a monofunctional carbon monoxide dehydrogenase

Carbon monoxide dehydrogenase (CODH) plays an important role in the processing of the one–carbon gases carbon monoxide and carbon dioxide. In CODH enzymes, these gases are channeled to and from the Ni-Fe-S active sites using hydrophobic cavities. In this work, we investigate these gas channels in a monofunctional CODH from Desulfovibrio vulgaris, which is unusual among CODHs for its oxygen-tolerance. By pressurizing D. vulgaris CODH protein crystals with xenon and solving the structure to 2.10 Å resolution, we identify 12 xenon sites per CODH monomer, thereby elucidating hydrophobic gas channels. We find that D. vulgaris CODH has one gas channel that has not been experimentally validated previously in a CODH, and a second channel that is shared with Moorella thermoacetica carbon monoxide dehydrogenase/acetyl-CoA synthase (CODH/ACS). This experimental visualization of D. vulgaris CODH gas channels lays groundwork for further exploration of factors contributing to oxygen-tolerance in this CODH, as well as study of channels in other CODHs. We dedicate this publication to the memory of Dick Holm, whose early studies of the Ni-Fe-S clusters of CODH inspired us all.

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

Bioinspired oxidation of benzyl alcohol: The role of environment and nuclearity of the catalyst evaluated by multivariate analysis

Inspired by copper-containing enzymes such as galactose oxidase and catechol oxidase, in which distinct coordination environments and nuclearities lead to specific catalytic activities, we summarize here the catalytic properties of dinuclear and mononuclear copper species towards benzyl alcohol oxidation using a multivariate statistical approach. Here, the new dinuclear [Cu 2 (μ-L 1 )(μ-pz)] 2+ (1) is compared against the mononuclear [CuL 2 Cl] (2), where (L 1 ) - and (L 2 ) - are the respective deprotonated forms of 2,6-bis((bis(pyridin-2-ylmethyl)amino)methyl)-4-methylphenol, and 3-((bis(pyridin-2-ylmethyl)amino)methyl)-2-hydroxy-5-methylbenzaldehyde and (pz) - is a pyrazolato bridge. Copper(II) perchlorate (CP) is used as control. The catalytic oxidation of benzyl alcohol is pursued, aiming to assess the role of the ligand environment and nuclearity. The multivariate statistical approach allows for the search of optimal catalytic conditions, considering variables such as catalyst load, hydrogen peroxide load, and time. Species 1, 2 and CP promoted selective production of benzaldehyde at different yields, with only negligible amounts of benzoic acid. Under normalized conditions, 2 showed superior catalytic activity. This species is 3.5-fold more active than the monometallic control CP, and points out to the need for an efficient ligand framework. Species 2 is 6-fold more active than the dinuclear 1, and indicates the favored nuclearity for the conversion of alcohols into aldehydes.

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

A constitutive framework for rocks undergoing solid dissolution

Here, we formulate a time-dependent damage theory for rocks subjected to mechanical deformation and solid dissolution. The constitutive description is inspired by the transition state theory, which states that the rate of dissolution is a function of the reactive surface area measured through the crack density in the volume. We use a gradient-enhanced damage framework in which damage depends on the deformation of the material as well as on the amount of solid mass dissolved over time. The gradient-enhanced formulation is characterized by a three-field variational formulation with the solid displacement, nonlocal equivalent strain, and nonlocal rate of solid dissolution as the basic state variables. Traditionally, time-independent damage theories have only allowed damage to increase with increasing external load. In the proposed framework, the degree of damage may increase due to solid dissolution even when the external load is held fixed. In this way, solid dissolution is viewed as a process that is responsible for bringing about rate-dependent effects such as creep and stress-relaxation, which are two common features of geomaterial behavior.

42 ENGINEERING↗
DOE OSTI2023

A statistical mechanics framework for polymer chain scission, based on the concepts of distorted bond potential and asymptotic matching

To design increasingly tough, resilient, and fatigue-resistant elastomers and hydrogels, the relationship between controllable network parameters at the molecular level (bond type, non-uniform chain length, entanglement density, etc.) to macroscopic quantities that govern damage and failure must be established. Many of the most successful constitutive models for elastomers have been rooted in statistical mechanical treatments of polymer chains. Typically, such constitutive models have used variants of the freely jointed chain model with rigid links. However, since the free energy state of a polymer chain is dominated by enthalpic bond distortion effects as the chain approaches its rupture point, bond extensibility ought to be accounted for if the model is intended to capture chain rupture. To that end, a new bond potential is supplemented to the freely jointed chain model (as derived in the u FJC framework of Buche and Silberstein (2021) and Buche et al. (2022)), which we have extended to yield a tractable, closed-form model of single chain behavior that should be amenable to continuum-level constitutive model development. Inspired by the asymptotically matched u FJC model response in both the low/intermediate chain force and high chain force regimes, a simple, quasi-polynomial bond potential energy function is derived. This bond potential exhibits harmonic behavior near the equilibrium state and anharmonic behavior for large bond stretches tending to a characteristic energy plateau (akin to the Lennard-Jones and Morse bond potentials). Using this bond potential, approximate yet highly-accurate analytical functions for bond stretch and chain force dependent upon chain stretch are established. Then, using this polymer chain model, a stochastic thermal fluctuation-driven chain rupture framework is developed. This framework is based upon a force-modified tilted bond potential that accounts for distortional bond potential energy, allowing for the derivation and subsequent calculation of the dissipated chain scission energy. Here, the cases of rate-dependent and rate-independent scission are accounted for throughout the rupture framework. The impact of Kuhn segment number on chain rupture behavior is also investigated. The model is fit to single chain mechanical response data collected from atomic force microscopy tensile tests for validation and to glean deeper insight into the molecular physics taking place. Due to their analytical nature, this polymer chain model and the associated rupture framework can in the future be implemented in finite element models accounting for fracture and fatigue in polydisperse elastomer networks.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Strongly nonlinear wave propagation in elasto-plastic metamaterials: Low-order dynamic modeling

Nonlinear elastic metamaterials are known to support a variety of dynamic phenomena that enhance our capacity to manipulate elastic waves. Since these properties stem from complex, subwavelength geometry, full-scale dynamic simulations are often prohibitively expensive at scales of interest. Prior studies have therefore utilized low-order effective medium models, such as discrete mass-spring lattices, to capture essential properties in the long-wavelength limit. While models of this type have been successfully implemented for a wide variety of nonlinear elastic systems, they have predominantly considered dynamics depending only on the instantaneous kinematics of the lattice, neglecting history-dependent effects, such as wear and plasticity. Here, to address this limitation, the present study develops a lattice-based modeling framework for nonlinear elastic metamaterials undergoing plastic deformation. Due to the history- and rate-dependent nature of plasticity, the framework generally yields a system of differential-algebraic equations whose computational cost is significantly greater than an elastic system of comparable size. We demonstrate the method using several models inspired by classical lattice dynamics and continuum plasticity theory and explore means to obtain empirical plasticity models for general geometries, thereby gaining insight into the influence of microstructural plasticity on effective material performance, which can be used to improve the design of nonlinear mechanical metamaterials.

Dynamic simulation↗
DOE OSTI2026

A finite viscoelastic constitutive model for low to high strain rate response of elastomers with application of strain rate-induced glass transition

Amorphous elastomers exhibit significant rate-stiffening and unique viscous flow characteristics across a wide range of strain rates, often undergoing glass transition above a strain rate threshold. We have developed a thermodynamically-consistent and micromechanically-inspired constitutive model for soft elastomeric materials to capture the rate-dependent stress-strain behavior and hysteresis when subjected to low to high strain rates. Here, our proposed constitutive model encapsulates the viscous flow of materials through molecular motion at low strain rates and local rearrangement and alignment of the molecules trying to overcome the intermolecular resistance at high strain rates, essentially covering the glass transition. We applied our constitutive model to uniaxial compression experiments performed at low and high strain rates for polyborosiloxane (PBS) to identify the material parameters, and subsequently, performed numerical simulations of single and multi-cycle compression, stress relaxation, and small amplitude oscillatory tension-compression. Our analyses indicate that the model predicts higher total energy dissipation with increasing strain rate; however, dissipation associated with molecular relaxation decreases (forming a cusp) because, beyond a crossover strain rate, intermolecular rearrangement and alignment become dominant, which is consistent with the onset of the glass transition. For cyclic loading-unloading, we observed that dissipation over a cycle remains constant at low strain rates but decreases non-monotonically at high strain rates before becoming constant, with the peak stress over the cycle becoming higher, which can be interpreted as more loading being carried elastically by the polymer network as the intermolecular rearrangement process occurs. Additionally, our model was able to predict the qualitative nature of the storage modulus and loss modulus in the limit of small strain over a wide range of frequency sweeps.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Unsupervised multimodal fusion of in-process sensor data for advanced manufacturing process monitoring

Effective monitoring of manufacturing processes is crucial for maintaining product quality and operational efficiency. Modern manufacturing environments often generate vast amounts of complementary multimodal data, including visual imagery from various perspectives and resolutions, hyperspectral data, and machine health monitoring information such as actuator positions, accelerometer readings, and temperature measurements. However, fusing and interpreting this complex, high-dimensional data presents significant challenges, particularly when labeled datasets are unavailable or impractical to obtain. This paper presents a novel approach to multimodal sensor data fusion in manufacturing processes, inspired by the Contrastive Language-Image Pre-training (CLIP) model. We leverage contrastive learning techniques to correlate different data modalities without the need for labeled data, overcoming limitations of traditional supervised machine learning methods in manufacturing contexts. Our proposed method demonstrates the ability to handle and learn encoders for five distinct modalities: visual imagery, audio signals, laser position (x and y coordinates), and laser power measurements. By compressing these high-dimensional datasets into low-dimensional representational spaces, our approach facilitates downstream tasks such as process control, anomaly detection, and quality assurance. The unsupervised nature of our method makes it broadly applicable across various manufacturing domains, where large volumes of unlabeled sensor data are common. We evaluate the effectiveness of our approach through a series of experiments, demonstrating its potential to enhance process monitoring capabilities in advanced manufacturing systems. This research contributes to the field of smart manufacturing by providing a flexible, scalable framework for multimodal data fusion that can adapt to diverse manufacturing environments and sensor configurations. The proposed method paves the way for more robust, data-driven decision-making in complex manufacturing processes.

Contrastive Learning↗
DOE OSTI2021

Ir(III)-Naphthoquinone complex as a platform for photocatalytic activity

Inspired by the primary events that take place in Photosystem II (PSII), we designed and synthesized a heteroleptic Ir(III) complex featuring an attached naphthoquinone (NQ) as an electron transfer (ET) auxiliary reminiscent of the plastoquinone electron acceptor in PSII. In this design, NQ is covalently attached to the 2,2'-bipyridyl (bpy) ligand of [Ir(dF(CF 3 )ppy) 2 (bpy)][PF 6 ], (dF(CF 3 )ppy = 2-(2,4-difluorophenyl)-5-(trifluoromethyl)pyridine). Following excitation of the photocatalyst ([Ir(dF(CF 3 )ppy) 2 (bpy-NQ)][PF 6 ]), reduced NQ (NQ •– ) was observed in transient absorption spectroscopy. Here, this novel catalyst has potential applications in oxidative and reductive photocatalytic processes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Evolution of the Rate-Limiting step: From Thin Film to Thick Ni-rich Cathodes

Ni-rich NMC (LiNixMnyCo1-x-yO2, x = 0.6) has the attributes of high specific capacity (greater than 180 mAh g-1), high operating voltage (ca. 3.8 V) and low cost, which is therefore deemed as one of the most promising cathode candidates for next-generation high-energy Li-ion batteries (LIBs). However, challenges exist before the large-scale commercialization of Ni-rich cathode such as gas generation, capacity degradation especially at high rate or elevated temperatures. From thin film to densely pack high loading cathode, this work investigates the rate-limiting steps in Ni-rich cathodes e.g., phase structure, electronic and ionic conductivity to understand the reaction kinetics at different scales. This work provides new insights in understanding the electrochemical properties of Ni-rich cathode at different levels to inspire revolutionary ideas to address the fundamental issues of Ni-rich cathode from materials to relevant electrode levels.

Hu, Jiangtao↗
DOE OSTI2023

Degradation and recovery of solid oxide fuel cell performance by control of cathode surface acidity: Case study – Impact of Cr followed by Ca infiltration

Solid oxide fuel cells (SOFC) have attracted attention as clean and efficient energy conversion devices with low emissions. However, several degradation mechanisms limit the electrochemical performance of current SOFCs, with cathode degradation due to Cr-poisoning from metal interconnects particularly problematic. The acidity/basicity of binary additives has been found to be a sensitive descriptor of the oxygen exchange kinetics, indicating that acidic Cr-species/basic Ca-species can be expected to deactivate/activate the cathode surface, respectively. Inspired by recent advances, the feasibility of relative acidity as a tool for reviving degraded SOFCs is demonstrated by neutralizing Cr-poisoned SOFCs by subsequent serial infiltration of Ca-species. Furthermore, a model mixed ionic and electronic conducting oxide, Pr 0.1 Ce 0.9 O 2-δ (PCO), is selected as the cathode material. Area-specific resistances (ASR) of symmetric cells obtained by electrochemical impedance spectroscopy show that Cr-infiltration results in a seven-fold increase in ASR, while subsequent infiltration of Ca-species leads to complete recovery. Performance degradation and recovery are attributed to depressed/enhanced redox properties at the PCO surface, as supported by XPS analysis. Experiments using anode-supported fuel cells show a reduction in peak power density by 26% upon Cr-infiltration, reversed following Ca-infiltration, after which no degradation is observed during subsequent operation for 100 h.

(Pr,Ce)O2-δ↗
DOE OSTI2025

Benchmarking optimization methods for materials research: Gradient descent and Bayesian optimization for lithium-ion battery aging diagnostics

Accurate and efficient parameter estimation is essential for battery diagnostics and aging analysis. Here, in this study, we compare two optimization-based approaches—gradient descent and Bayesian optimization—for extracting parameters from differential voltage analysis in lithium-ion batteries. While these techniques are widely used, their relative strengths and limitations for this application are not well understood. The study evaluates the trade-offs between these methods in terms of result quality, computational cost, and reliability within this specific application. The diagnostic results from our battery data suggest adopting gradient descent as an initial method for rapid and efficient analysis, while employing more stable optimization techniques, such as Bayesian optimization, as a verification step to mitigate potential instability. Comparing the two methods provides information on algorithmic choice, while inspiring further discussions on selecting appropriate techniques for specific research tasks.

Zhao, Ziqing [Boston Univ., MA (United States)] (O↗
DOE OSTI2025

Predicting Si-Anode Calendar Life Using Machine Learning: Correlating Electrolyte Properties and Electrochemical Signals

This study evaluates novel electrolytes tailored for Si-containing anodes to promote calendar-life. Drawing inspiration from advancements in electrolytes for Li-metal cells, the work investigates correlations between predicted electrolyte properties and measured electrochemical performance using several machine-learning models. By leveraging machine learning and advanced modeling techniques, this study aims to establish predictive frameworks that accelerate calendar-aging experiments and inform rational electrolyte design for Si-containing cells. In the present study, fifteen different electrolytes are evaluated in a Si-containing cell using an accelerated calendar-life protocol. For each electrolyte considered, 87 properties (features) from the Advanced Electrolyte Model were produced to identify key property/performance relationships. In this study, the best performing electrolytes were generally those formulations that included non-coordinating fluoroether solvents, and the most predictive features for long-term calendar-life were features related to salt concentration and electrolyte viscosity as well as early capacity, ionic conductivity, and Coulombic efficiency measurements. The framework developed in this study correlating electrolyte properties to measured electrochemical performance is expected to accelerate electrolyte design for Si-containing anodes and ultimately enable high-energy-density, long-life Li-ion batteries.

25 - ENERGY STORAGE↗