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Results for “surface complexation modeling”

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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Rapid simulations of hyperspectral near-field images of three-dimensional heterogeneous surfaces

The scattering-type scanning near-field optical microscope (s-SNOM) has emerged as a powerful tool for resolving nanoscale inhomogeneities in laterally heterogeneous samples. However, most analytical models used to predict the scattering near-field signals are assuming homogenous landscapes (bulk materials), resulting in inconsistencies when applied to samples with more complex configurations. In this work, we combine the point-dipole model (PDM) to the finite-element method (FEM) to account for the lateral and vertical heterogeneities while keeping the computation time manageable. Full images, spectra, or hyperspectral line profiles can be simulated by calculating the self-consistent dipole radiation demodulated at higher harmonics of the tip oscillation, mimicking real experimental procedures. Using this formalism, we clarify several important yet puzzling experimental observations in near-field images on samples with rich typography and complex material compositions, heterostructures of two-dimensional material flakes, and plasmonic antennas. The developed method serves as a basis for future investigations of nano-systems with nontrivial topography.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Two human antibodies to a meningococcal serogroup B vaccine antigen enhance binding of complement Factor H by stabilizing the Factor H binding site

Microbial pathogens bind host complement regulatory proteins to evade the immune system. The bacterial pathogen Neisseria meningitidis, or meningococcus, binds several complement regulators, including human Factor H (FH). FH binding protein (FHbp) is a component of two licensed meningococcal vaccines and in mice FHbp elicits antibodies that inhibit binding of FH to FHbp, which defeat the bacterial evasion mechanism. However, humans vaccinated with FHbp develop antibodies that enhance binding of FH to the bacteria, which could limit the effectiveness of the vaccines. In the present study, we show that two vaccine-elicited antibody fragments (Fabs) isolated from different human subjects increase binding of complement FH to meningococcal FHbp by ELISA. The two Fabs have different effects on the kinetics of FH binding to immobilized FHbp as measured by surface plasmon resonance. The 1.7- and 2.0-Å resolution X-ray crystal structures of the Fabs in complexes with FHbp illustrate that the two Fabs bind to similar epitopes on the amino-terminal domain of FHbp, adjacent to the FH binding site. Superposition models of ternary complexes of each Fab with FHbp and FH show that there is likely minimal contact between the Fabs and FH. Collectively, the structures reveal that the Fabs enhance binding of FH to FHbp by altering the conformations and mobilities of two loops adjacent to the FH binding site of FHbp. In addition, the 1.5 Å-resolution structure of one of the isolated Fabs defines the structural rearrangements associated with binding to FHbp. The FH-enhancing human Fabs, which are mirrored in the human polyclonal antibody responses, have important implications for tuning the effectiveness of FHbp-based vaccines.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

New Insights from Legacy Seismic Data regarding Basalt Elevations and Variability on the Hanford Site

Migration of groundwater contaminants in the Gable Gap area of the Hanford Site in southeastern Washington State is strongly influenced by the distribution and permeability of basalts that lie beneath an unconfined aquifer. Locally, folding and faulting of the Columbia River Basalt associated with the Yakima fold and thrust belt followed by erosion due to the Lake Missoula floods resulted in a complex basalt surface that represents either an impermeable lower boundary to the unconfined aquifer system or localized regions of increased permeability that potentially promote communication between the unconfined aquifer system and deeper, confined aquifer systems. Paleo-channels carved into the basalt by floodwaters are thought to provide preferential flow paths for groundwater contaminants. In 2011, a seismic landstreamer campaign was carried out to image the basalt surface and produced pre-stack depth migrated p-wave reflection images. The reflection images identified two large troughs that may represent paleo-channels and several areas of possible faulting. Here, the streamer data are re-analyzed using refraction travel-time and Rayleigh wave dispersion analyses to obtain images of compressional and shear wave velocities within the suprabasalt sediment sections and the upper basalt surface. The combined interpretation of reflection and seismic velocity images shows complexity in the basalt velocity and elevation, which varies by 50 m or more within the study area. These results, along with other ongoing geophysical investigations, will be used to inform the site geologic model and potentially guide placement of future boreholes needed to quantify vertical flow between the confined and unconfined aquifers.

58 GEOSCIENCES↗
DOE OSTI2020

The TRANSCEND Consortium - In-situ Identification of Surface Corrosion Products on Spent Nuclear Fuels - 20276

The management of spent nuclear fuel is a major ongoing concern for the UK owing to the cessation of reprocessing operations at Sellafield and the large, complex inventory arising from Magnox, AGR, PWR and prototype reactors. Retrieval and relocation operations for legacy fuels are imminent and therefore, any models that enhance our understanding of fuel evolution will help mitigate the risks associated with fuel storage and disposal. The TRANSCEND Consortium on nuclear waste management comprises four work packages, within this current paper we provide a summary overview of progress to date and illustrative results from Theme 3: Spent Nuclear Fuels. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Hydration Mechanisms in Nanoparticle Interaction and Surface Energetics

Work on the project advanced molecular level understanding, prediction and control of nanoscale hydration in salt solutions under electric stimuli or ionic patterning. Permeation of nanoporous electrodes at preset voltage underlies the function of ultracapacitors. Transitory regulation of wetting in nanoporous media by electric field spans an array of applications in materials, energy storage, and separation sciences. The physically related modulation of nanoparticle solubility by surface charges can significantly extend the range of the nanomaterial applications, improve processing techniques, and potentially alleviate environmental concerns. Pore/solution equilibria, and activated kinetics of liquid gating by nanoconfined electrolytes are challenging problems at the forefront of experimental and theoretical research. Addressing these problems from a molecular perspective required the development of state-of-the-art simulation algorithms in statistical mechanics to capture complex processes in open systems under electric control. Parallel studies of wetting and dispersibility of polar and ionizing particles aim to uncover predictive relations between electrowetting, chemical functionalization, and geometry of nanomaterial particles. By nonequilibrium dynamic modeling, we elucidated electrolyte flow in nanochanels and associated electrokinetic energy conversion. Research on the program provided training opportunities for the next generation of scientists in computational chemistry. Insights, and methods from the project implicate broad segment of researchers in energy and nanosciences, materials and surface chemistry.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

1 H chemical shift anisotropy: a high sensitivity solid-state NMR dynamics probe for surface studies?

We report dynamics play significant roles in chemistry and biochemistry-molecular motions impact both large- and small-scale chemical reactions in addition to biochemical processes. In many systems, including heterogeneous catalysts, the characterization of dynamics remains a challenge. The most common approaches involve the solid-state NMR measurement of anisotropic interactions, in particular 2 H quadrupolar coupling and 1 H–X dipolar coupling, which generally require isotope enrichment. Due to the high sensitivity of 1 H NMR, 1 H chemical shift anisotropy (CSA) is a particularly enticing, and underexplored, dynamics probe. We carried out 1 H CSA and 1 H– 13 C dipolar coupling measurements in a series of model supported complexes to understand how 1 H CSA can be leveraged to gain dynamic information for heterogeneous catalysts. Mathematical descriptions are given for the dynamic averaging of the CSA tensor, and its dependence on orientation and asymmetry. The variability of the orientation of the tensor in the molecular frame, in addition to its magnitude and asymmetry, negatively impacts attempts to extract quantitative dynamic information. Nevertheless, 1 H CSA measurements can reveal useful qualitative insights into the motions of a particularly dilute site, such as from a surface species.

1H chemical shift anisotropy↗
DOE OSTI2023

Fluorine spillover for ceria- vs silica-supported palladium nanoparticles: A MD study using machine learning potentials

Supported metallic nanoparticles play a central role in catalysis. However, predictive modeling is particularly challenging due to the structural and dynamic complexity of the nanoparticle and its interface with the support, given that the sizes of interest are often well beyond those accessible via traditional ab initio methods. With recent advances in machine learning, it is now feasible to perform MD simulations with potentials retaining near-density-functional theory (DFT) accuracy, which can elucidate the growth and relaxation of supported metal nanoparticles, as well as reactions on those catalysts, at temperatures and time scales approaching those relevant to experiments. Furthermore, the surfaces of the support materials can also be modeled realistically through simulated annealing to include effects such as defects and amorphous structures. We study the adsorption of fluorine atoms on ceria and silica supported palladium nanoparticles using machine learning potential trained by DFT data using the DeePMD framework. We show defects on ceria and Pd/ceria interfaces are crucial for the initial adsorption of fluorine, while the interplay between Pd and ceria and the reverse oxygen migration from ceria to Pd control spillover of fluorine from Pd to ceria at later stages. In contrast, silica supports do not induce fluorine spillover from Pd particles.

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

Predicting a Sharp Decline in Selectivity for Catalytic Esterification of Alcohols from van der Waals Interactions

Controlling the selectivity of catalytic reactions is a critical aspect of improving energy efficiency in the chemical industry; thus, predictive models are of key importance. Herein the performance of a heterogeneous, nanoporous Au catalyst is predicted for the complex catalytic self-coupling of the series of C 2 -C 4 alkyl alcohols, based solely on the known kinetics of the elementary steps of the catalytic cycle for methanol coupling, using scaling methods augmented by density functional theory. Notably, a sharp decrease in selectivity for ester formation with increasing molecular weight to favor the aldehyde due to van der Waals interactions of reaction intermediates with the surface was predicted and subsequently verified quantitatively by experiment. Furthermore, the agreement between theory and experiment clearly demonstrates the efficacy of this approach for building a predictive model of catalytic behavior for a homologous set of reactants using a small set of experimental information.

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

Predicting a Sharp Decline in Selectivity for Catalytic Esterification of Alcohols from van der Waals Interactions

Controlling the selectivity of catalytic reactions is a critical aspect of improving energy efficiency in the chemical industry; thus, predictive models are of key importance. Herein the performance of a heterogeneous, nanoporous Au catalyst is predicted for the complex catalytic self-coupling of the series of C 2 –C 4 alkyl alcohols, based solely on the known kinetics of the elementary steps of the catalytic cycle for methanol coupling, using scaling methods augmented by density functional theory. Notably, a sharp decrease in selectivity for ester formation with increasing molecular weight to favor the aldehyde due to van der Waals interactions of reaction intermediates with the surface was predicted and subsequently verified quantitatively by experiment. Further, the agreement between theory and experiment clearly demonstrates the efficacy of this approach for building a predictive model of catalytic behavior for a homologous set of reactants using a small set of experimental information.

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

Variational encoder geostatistical analysis (VEGAS) with an application to large scale riverine bathymetry

Estimation of riverbed profiles, also known as bathymetry, plays a vital role in many applications, such as safe and efficient inland navigation, prediction of bank erosion, land subsidence, and flood risk management. The high cost and complex logistics of direct bathymetry surveys, i.e, depth imaging, have encouraged the use of indirect measurements such as surface flow velocities. However, estimating high-resolution bathymetry from indirect measurements is an inverse problem that can be computationally challenging. Here, we propose a reduced-order model (ROM) based approach that utilizes a variational autoencoder (VAE), a type of deep neural network with a narrow layer in the middle, to compress bathymetry and flow velocity information and accelerate bathymetry inverse problems from flow velocity measurements. In our application, the shallow-water equations (SWE) with appropriate boundary conditions (BCs), e.g., the discharge and/or the free surface elevation, constitute the forward problem, to predict flow velocity. Then, ROMs of the SWEs are constructed on a nonlinear manifold of low dimensionality through a variational encoder and the bathymetry inversion problem is derived on the low-dimensional latent space in a Hierarchical Bayesian setting. Further, the reformulation allows variational inference with a small number (e.g., $\mathscr{O}$ (100) of ROM runs and efficient uncertainty quantification. We have tested our inversion approach on a one-mile reach of the Savannah River, GA, USA. Once the neural network is trained (offline stage), the proposed technique can perform the inversion operation orders of magnitude faster than traditional inversion methods that are commonly based on linear projections, such as principal component analysis (PCA), or the principal component geostatistical approach (PCGA). Furthermore, tests show that the algorithm can estimate the bathymetry with good accuracy even with sparse flow velocity measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

General Kinetic Model for pH Dependence of Proton-Coupled Electron Transfer: Application to an Electrochemical Water Oxidation System

The pH dependence of proton-coupled electron transfer (PCET) reactions, which are critical to many chemical and biological processes, is a powerful probe for elucidating their fundamental mechanisms. Herein, a general, multichannel kinetic model is introduced to describe the pH dependence of both homogeneous and electrochemical PCET reactions. According to this model, a weak pH dependence can arise from the competition among multiple sequential and concerted PCET channels involving different forms of the redox species, such as protonated and deprotonated forms, as well as different proton donors and acceptors. The contribution of each channel is influenced by the relative populations of the reactant species, which often depend strongly on pH, leading to complex pH dependence of PCET apparent rate constants. This model is used to explain the origins of the experimentally observed weak pH dependence of the electrochemical PCET apparent rate constant for a ruthenium-based water oxidation catalyst attached to a tin-doped In2O3 (ITO) surface. The weak pH dependence is found to arise from the intrinsic differences in the rate constants of participating channels and the dependence of their relative contributions on pH. This model predicts that the apparent maximum rate constant will become pH-independent at higher pH, which is confirmed by experimental measurements. Our analysis also suggests that the dominant channels are electron transfer at lower pH and sequential PCET via electron transfer followed by fast proton transfer at higher pH. Furthermore, this work highlights the importance of considering multiple competing channels simultaneously for PCET processes.

Catalysts↗
DOE OSTI2024

Molecular dynamics studies of knotted polymers

Molecular dynamics calculations have been used to explore the influence of knots on the strength of a polymer strand. In particular, the mechanism of breaking 31, 41, 51, and 52 prime knots has been studied using two very different models to represent the polymer: (1) the generic coarse-grained (CG) bead model of polymer physics and (2) a state-of-the-art machine learned atomistic neural network (NN) potential for polyethylene derived from electronic structure calculations. While there is a broad overall agreement between the results on the influence of the pulling rate on chain rupture based on the CG and atomistic NN models, for the simple 31 and 41 knots, significant differences are found for the more complex 51 and 52 knots. Notably, in the latter case, the NN model more frequently predicts that these knots can break not only at the crossings at the entrance/exit but also at one of the central crossing points. The relative smoothness of the CG potential energy surface also leads to stabilization of tighter knots compared to the more realistic NN model.

DelloStritto, Mark (ORCID:0000000206785860)↗
DOE OSTI2026

Wyoming CarbonSAFE Phase III: Site Characterization and Permitting Commercial-Scale Carbon Storage Complex Feasibility Study at Dry Fork Station, Wyoming

This report presents the findings of the technical and non-technical site characterization and permitting activities (“Phase III”) conducted under the Wyoming CarbonSAFE: Accelerating CCUS Commercialization and Deployment at Dry Fork Power Station (DFS) and the Wyoming Integrated Test Center project (“Wyoming CarbonSAFE”). Wyoming CarbonSAFE is part of the U.S. Department of Energy’s (DOE) National Energy Technology Laboratory (NETL) Carbon Storage Assurance Facility Enterprise (“CarbonSAFE”) Initiative. The results of Phase III demonstrate that the Wyoming CarbonSAFE project - referred to as the Northern Powder River Basin Carbon Sequestration Hub (NPRB-CSH) - meets the technical, regulatory, and commercial feasibility requirements necessary to advance toward commercial development and construction. The activities completed under this project Phase make the NPRB-CSH one of the region’s most commercially ready carbon storage sites. Completion of this Phase included the finalization of all site surface and subsurface characterization activities, completion and testing of two Class VI standard wells, 10 draft Class VI permits-to construct to address the future needs of a storage complex, finalized NEPA assessments, transportation and capture FEED studies, and a full commercialization strategy with economic modeling, operation and site closure strategies. The NPRB-CSH meets all requirements to progress to a CarbonSAFE Phase IV program or advance to full commercial operations under the development of a business partner.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2026

Best practices in software development for robust and reproducible geoscientific models based on insights from the Global Carbon Budget's dynamic vegetation models

Computational models play an increasingly vital role in scientific research by enabling the numerical simulation of complex processes. Such models are also fundamental in geosciences. For instance, they offer critical insights into the impacts of global change on the Earth system today and in the future. Beyond their value as research tools, models are also software products and should therefore adhere to certain established software engineering standards. However, scientists are rarely trained as software developers, which can lead to potential deficiencies in software quality like unreadable, inefficient, or erroneous code. The complexity of models, coupled with their integration into broader workflows, also often makes it challenging to reproduce results, evaluate processes, and build upon them. In this paper, we review the state and current practices of the development processes of the state-of-the-art land surface models used by the Global Carbon Budget. We combine the experience of modelers from the respective research groups with the expertise of software engineers from tech companies to outline key principles and tools for improving software quality in research. We explore four main areas: (1) model testing and validation, (2) scientific, technical, and user documentation, (3) version control, continuous integration, and code review, and (4) the portability and reproducibility of workflows. Our review reveals that while modeling communities are incorporating many best practices, significant room for improvement remains in areas such as automated testing, automated documentation, and reproducibility. Therefore, we here identify and promote essential software engineering practices, including numerous examples of practices from within the community that can serve as guidelines for other models and could help streamline processes across the entire community. We conclude with an open-source example implementation of these principles, demonstrating portable and reproducible data flows, a continuous integration setup, and web-based visualizations. This example may serve as a practical resource for model developers, users, and all scientists engaged in scientific programming.

Gregor, Konstantin [Technical Univ. of Munich (Ger↗
DOE OSTI2025

Probing Condensed-Phase Structure and Dynamics in Hierarchical Zeolites and Nanosheets for Catalytic Upgradation of Biomass (Final Report)

Understanding complex reaction pathways in systems governed by multi-scale collective interactions across time and length scales remains a central scientific challenge. This project was guided by the hypothesis that the interplay among oligomers, solvents, and active sites can be tuned by a suitable choice of solvation environment and pore architecture in solid-acid catalysts to direct chemical transformations relevant to biomass conversion. Zeolites and zeolite nanosheets were used as model platforms, allowing for the interaction of macromolecules with the surface of the zeolite nanosheets and with smaller pores that host catalytically active sites. To investigate these coupled phenomena, we employ a multi-scale computational framework that integrates molecular-level descriptions with advanced sampling approaches to capture key physical and chemical interactions. Our work through this project improved fundamental understanding of how reactants and solid-acid catalysts interact in solvent-rich environments, thereby enabling the rational design of catalytic systems that upgrade biomass with enhanced selectivity and energy efficiency. In addition, the project developed advanced sampling methodologies critical for disentangling complex, reactive processes in multi-component catalytic environments.

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

Bayesian inference in band excitation scanning probe microscopy for optimal dynamic model selection in imaging

The universal tendency in scanning probe microscopy (SPM) over the last two decades is to transition from simple 2D imaging to complex detection and spectroscopic imaging modes. The emergence of complex SPM engines brings forth the challenge of reliable data interpretation, i.e., conversion from detected signals to descriptors specific to tip–surface interactions and subsequently to material’s properties. In this work, we implemented a Bayesian inference approach for the analysis of the image formation mechanisms in band excitation SPM. Compared to the point estimates in classical functional fit approaches, Bayesian inference allows for the incorporation of extant knowledge of materials and probe behavior in the form of corresponding prior distribution and return the information on the material functionality in the form of readily interpretable posterior distributions. We explore the nonlinear mechanical behaviors spatially in a classical ferroelectric material, PbTiO 3 . We observe the non-trivial evolution of the Duffing stiffness term and the nonlinearity of the sample surface, determine spatial clustering of the nonlinear response, and perform a Landau analysis on predicting the nonlinear coefficient, which indicates that ferroelectric behavior can be a cause of the observed results. These observations suggest that the spectrum of anomalous behaviors at the ferroelectric domain walls may be broader than previously believed and can extend to non-conventional mechanical properties in addition to static and microwave conductance.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Finite element modeling of electropolishing of radio frequency dipole Nb crab cavity in hydrofluoric-sulfuric acid electrolyte

The superior performance of niobium superconducting radio frequency (SRF) cavities is achieved through meticulous surface treatment, notably via chemical electropolishing, ensuring exceptionally smooth surfaces. While this technique has been extensively employed for cylindrically symmetric structures, addressing more intricate geometries poses significant challenges in achieving uniform polishing and controlled material removal, especially when moving away from retractable cathodes. This presents a multifaceted electrochemical, thermal, and fluid dynamics conundrum. A prime example is the 197 MHz radio frequency dipole (RFD) crabbing cavity proposed for the Electron Ion Collider (EIC) project, exemplifying such complex structures. Our groundbreaking work integrates the localized oxide thickness variation, considering its impact on current distribution and Joule heating, within the framework of multi-physics modeling using the COMSOL® simulation suite. This approach was applied to a comprehensive model of the RFD cavity, allowing us to investigate optimal external cooling water flow conditions necessary for achieving desirable outcomes. In conclusion, this illustrates the increasing utility of such multi-physics codes to greatly aid in designing solutions to challenging processing requirements for increasingly complex accelerator cavities.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Mercury Accumulation in a Stream Ecosystem: Linking Labile Mercury in Sediment Porewaters to Bioaccumulative Mercury in Trophic Webs

Mercury (Hg) deposition and accumulation in the abiotic and biotic environments of a stream ecosystem were studied. This study aimed to link labile Hg in porewater to bioaccumulative Hg in biota. Sediment cores, porewaters, and biota were sampled from four sites along the Fourmile Branch (SC, USA) and measured for total Hg (THg) and methyl-Hg (MHg) concentrations. Water quality parameters were also measured at the sediment–water interface (SWI) to model the Hg speciation. In general, Hg concentrations in porewaters and bulk sediment were relatively high, and most of the sediment Hg was in the solid phase as non-labile species. Surface sediment presented higher Hg concentrations than the medium and bottom layers. Mercury methylation and MHg production in the sediment was primarily influenced by sulfate levels, since positive correlations were observed between sulfate and Hg in the porewaters. The majority of Hg species at the SWI were in non-labile form, and the dominant labile Hg species was complexed with dissolved organic carbon. MHg concentrations in the aquatic food web biomagnified with trophic levels (biofilm, invertebrates, and fish), increasing by 3.31 times per trophic level. Based on the derived data, a modified MHg magnification model was established to estimate the Hg bioaccumulation at any trophic level using Hg concentrations in the abiotic environment (i.e., porewater).

54 ENVIRONMENTAL SCIENCES↗