Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “dynamic structure factor”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Probing Surface Plasmon Dynamics in Periodic Nanostructures through Ultrafast Electron Microscopy

Surface plasmon polaritons (SPPs) can be manipulated to localize and guide light in subwavelength distances, enabling them to find applications in a wide range of areas, from sensing to quantum computing. Among several methods of SPP excitation, periodic arrays of nano- and microstructures are of particular interest, as they enable engineering SPP properties through structural parameters. Here, in this study, using the photon-induced near-field electron microscopy (PINEM) technique, we investigated the mode formation, coupling, interference, and decay of SPPs in square and hexagonal arrays of circular nanoholes under both visible and near-infrared excitation. Polarization-resolved analysis revealed the key factors governing SPP localization and interference patterns, showing that the periodicity and symmetry of the array primarily determine the SPP interference patterns and their orientation, while pump polarization mainly modulates their intensity. Time-resolved PINEM measurements demonstrated the spatial dependence of the SPP temporal characteristics. In addition, cathodoluminescence (CL) spectroscopy was employed to examine the intrinsic plasmonic characteristics of the structure. Finite difference time domain (FDTD) simulations showed strong agreement with both PINEM and CL measurements on the spatial and spectral behavior of SPPs. Understanding the spatiotemporal dynamics of SPPs on nanostructures beyond the diffraction limit is crucial for optimizing plasmonic structures for advanced photonic and quantum technologies.

Plasmonics↗
DOE OSTI2024

Deep-Learning Interatomic Potential Connects Molecular Structural Ordering to the Macroscale Properties of Polyacrylonitrile

Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic stereochemistry resulting from nonselective radical polymerization. As such, an accurate, fundamental understanding of governing interactions among PAN molecular units is indispensable for advancing the design principles of final products at reduced processability costs. While ab initio molecular dynamics (AIMD) simulations can provide the necessary accuracy for treating key interactions in polar polymers, such as dipole–dipole interactions and hydrogen bonding, and analyzing their influence on the molecular orientation, their implementation is limited to small molecules only. Herein, we show that the neural network interatomic potentials (NNIPs) that are trained on the small-scale AIMD data (acquired for oligomers) can be efficiently employed to examine the structures and properties at large scales (polymers). NNIP provides critical insight into intra- and interchain hydrogen-bonding and dipolar correlations and accurately predicts the amorphous bulk PAN structure validated by modeling the experimental X-ray structure factor. Furthermore, the NNIP-predicted PAN properties, such as density and elastic modulus, are in good agreement with their experimental values. Overall, the trend in the elastic modulus is found to correlate strongly with the PAN structural orientations encoded in the Hermans orientation factor. In conclusion, this study enables the ability to predict the structure–property relations for PAN and analogues with sustainable ab initio accuracy across scales.

36 MATERIALS SCIENCE↗
DOE OSTI2026

QCD Theory and Transverse Momentum Dependent Physics

A major challenge for QCD theorists is to understand how to different categories of QCD physics contribute to a single observable. This is the goal of QCD factorization theory. Today, QCD physicists are tasked with describing increasingly detailed properties of transverse momentum dependent particle distributions, including their shapes and normalizations, their variation with energy and other kinematical variables, the identity of particles involved, correlations with spin, and the relationship between transverse momentum distributions in different processes. Embedded within that broad effort are specific fundamental questions at the heart of QCD theory. Namely, how does one give precise, quantitative meaning to quark and gluon concepts in hard scattering events, and what are the limits of those concepts? In the past two decades, there has been a renaissance in TMD QCD, with a broad spectrum of motivations driving the study of TMD observables. TMD QCD studies are in a transformative stage; subfields that traditionally progressed nearly independently of one other, such as hadronic structure studies and the study of perturbative QCD gluon radiation in high-energy collisions, are becoming intertwined. It is thus becoming increasingly clear that unified approaches -- those that incorporate all the relevant phenomena into a single formalism rooted in QCD fundamentals -- will be critical to the mission to expand the current understanding of QCD nuclear/hadronic dynamics. TMD QCD factorization theorems and their generalizations will be central to these efforts. The factorization theorems, in addition, provide the practical prescription for applying specific calculations to phenomenology. The goal of this project was to unify and push the boundaries of traditional TMD research and prepare it for use in future phenomenological applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Supramolecular Motion Enables Chondrogenic Bioactivity of a Cyclic Peptide Mimetic of Transforming Growth Factor-β1

Transforming growth factor (TGF)-β1 is a multifunctional protein that is essential in many cellular processes that include fibrosis, inflammation, chondrogenesis, and cartilage repair. In particular, cartilage repair is important to avoid physical disability since this tissue does not have the inherent capacity to regenerate beyond full development. We report here on supramolecular coassemblies of two peptide amphiphile molecules, one containing a TGF-β1 mimetic peptide, and another which is one of two constitutional isomers lacking bioactivity. Using human articular chondrocytes, we investigated the bioactivity of the supramolecular copolymers of each isomer displaying either the previously reported linear form of the mimetic peptide or a novel cyclic analogue. Based on fluorescence depolarization and 1 H NMR spin–lattice relaxation times, we found that coassemblies containing the cyclic compound and the most dynamic isomer exhibited the highest intracellular TGF-β1 signaling and gene expression of cartilage extracellular matrix components. Here, we conclude that control of supramolecular motion is emerging as an important factor in the binding of synthetic molecules to receptors that can be tuned through chemical structure.

Antigens↗
DOE OSTI2020

Hydrodynamics of countercurrent flows in a structured packed column: effects of initial wetting and dynamic contact angle

The structured packed column has gained considerable attention as a prospective technology to mitigate carbon dioxide emissions from thermal power plants. The computational flow investigation of such systems is a multiscale problem, and local hydrodynamics plays a key role in overall column efficiency. Accordingly, multiphase flow studies in the representative elementary unit (REU) of a packed column can offer insights into the local flow behavior such as flow pattern, interfacial area, etc. The interfacial area plays a key role in the overall interphase mass transfer and thereby in separation efficiency. The predicted effective area matches well with corresponding ones in experiments for Mellapak 250.Y packings. The contact angle (i.e., solid substrate behavior) is one of the critical factors that dictates wettings, and thereby the interfacial area. The dynamic contact angle (DCA) was considered in order to explore the effects of contact angle hysteresis on the interfacial area. DCA has more pronounced impact on the interfacial area for a solvent possessing a higher value of surface tension and equilibrium contact angle. The interfacial area shows temporal undulation and does not achieve a pseudo steady state due to the capillary force. In contrary, the interfacial area gets a net value for a solvent having low surface tension value. Further, specification of the static contact angle at the solid substrate shows the least value of the interfacial area. We also extensively studied the effect of the initial sheet condition (dry vs wet) on the interfacial area. The initially wetted sheets show slightly higher value of the interfacial area as compared to the initially dry sheets at a fixed flow rate for a solvent having high surface tension value. Wetting hysteresis decreases with increasing liquid loads. On the other hand, wetting hysteresis does not occur for a solvent having value of low surface tension and equilibrium contact angle.

structured packings, dynamic contact angle, interf↗
DOE OSTI2020

The energetics and dynamics of confinement in flexible frameworks and molecular confinement

This project is part of an ongoing and long-term collaboration among Alexandra Navrotsky (who moved recently from UC Davis to Arizona State University), Nancy Ross at Virginia Tech, and Brian Woodfield at Brigham Young University (BYU) on the thermodynamics and lattice dynamics of nanophase and porous materials. Navrotsky emphasizes energetics of formation and guest molecule sorption by ambient and high temperature calorimetry, Woodfield contributes cryogenic heat capacity measurements, and Ross uses inelastic neutron scattering, X-ray and neutron diffraction, and high pressure studies to probe lattice dynamics and phase transitions. Porous frameworks form the chemical and structural basis for critical technologies in separations, catalysis, nuclear waste containment and biomedical applications. Hundreds of zeolites and metal organic frameworks (MOFs) have been synthesized, and their ability to separate and store hydrogen, methane and carbon dioxide has been investigated both experimentally and theoretically. Nevertheless, a fundamental and systematic molecular-level understanding of the thermodynamic and structural factors governing the stability and guest-host interactions in these materials lags behind focused studies of specific systems. Because the guest molecules interact with each other and with the host framework, molecular confinement is a finely balanced and complex phenomenon. The ability of the guest molecules to bind and diffuse through the pores is determined by the nature of the host framework which, in turn, responds to the nature and concentration of guest molecules and to pressure and temperature. The work explores how framework flexibility, tailored by structure, composition, temperature and pressure, is a general phenomenon, similar in nature but variable in extent, in both zeolites and MOFs and is part of a free energy landscape in which framework-guest interactions, pressure, and temperature result in changes in framework geometry and, in some cases, phase transitions. These subtle and/or pronounced changes in lattice geometry, energetics, and dynamics can play a decisive role in confinement and in differentiating the binding of molecules of similar size.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Latent Mechanisms of Polarization Switching from In Situ Electron Microscopy Observations

In situ scanning transmission electron microscopy enables observation of the domain dynamics in ferroelectric materials as a function of externally applied bias and temperature. The resultant data sets contain a wealth of information on polarization switching and phase transition mechanisms. However, identification of these mechanisms from observational data sets has remained a problem due to a large variety of possible configurations, many of which are degenerate. Here, an approach based on a combination of deep learning-based semantic segmentation, rotationally invariant variational autoencoder (VAE), and non-negative matrix factorization to enable learning of a latent space representation of the data with multiple real-space rotationally equivalent variants mapped to the same latent space descriptors is introduced. By varying the size of training sub-images in the VAE, the degree of complexity in the structural descriptors is tuned from simple domain wall detection to the identification of switching pathways. Importantly, this yields a powerful tool for the exploration of the dynamic data in mesoscopic electron, scanning probe, optical, and chemical imaging. Moreover, this work adds to the growing body of knowledge of incorporating physical constraints into the machine and deep-learning methods to improve learned descriptors of physical phenomena.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A review of fluids under nanoconfinement: Reactivity, geomechanics, phase transitions, and flow

Due to its many industrial applications, the physics associated with the nanoconfinement of fluids has garnered great attention from the scientific community in roughly the last fifteen years. One pioneer of that research has been the Multi-Scale Fluid-Solid Interactions in Architected and Natural Materials (MUSE) Energy Frontier Research Center, which produced more than 50 journal articles from 2018 to 2023. We structure the findings of MUSE into four categories and contextualize the work with the broader literature. Here, the four categories are (1) the dependence of chemical reactions on the degree of confinement, which given the lower freedom of motion of molecules yet improved availability of reaction sites generally lead to enhancement from bulk reactions; (2) the mechanical properties of the porous matrix, which are dependent on the heterogeneity of the system in terms of both composition and structure; (3) the phase transitions under nanoconfinement, which are influenced by the pore size and morphology, surface chemistry, and substrate composition; and (4) the physical controls of slip flow, which demonstrate the importance of several factors, such as wettability. These aspects of nanoconfinement are becoming clearer, but there are other elements such as mathematical theory that are still being developed.

58 GEOSCIENCES↗
DOE OSTI2023

Crystallographic and temperature effects in low-energy collisions for plasma–material interactions

The interaction of plasma with materials is critical for the fundamental understanding of non-equilibrium processes and their wide application. Recent achievements in fusion energy research emphasize the importance of this problem. Because modelling and predicting plasma–material interactions (PMI) require considering a tremendous number of single plasma particle–surface interaction events, this type of modelling was, until recently, possible only within a binary collision approximation (BCA). The BCA approach considers materials as uniform temperature-insensitive media. The research presented here addresses the PMI problem within an atomistic-based approach for the first time at a statistically significant level. Approximately 10 5 molecular dynamics trajectories were generated for 100 eV deuterium ions interacting with a tungsten surface. The research demonstrated the critical importance of incorporating the discrete lattice structure of matter into the model. Deuterium penetration depth and fraction of backscattered deuterium ions strongly depend on the surface orientation, the impact incident ion directions, and the ions’ initial positions. On average, the BCA-based calculations underestimate the penetration depth by a factor of two or more, and the fraction of backscattered atoms is overestimated by a factor of two or more. As a result, increasing the material temperature from 500 to 3000 K reduces penetration depth by about 30% and increases the fraction of backscattered atoms by 250%.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Impact of Meteorological Factors on the Mesoscale Morphology of Cloud Streets during a Cold-Air Outbreak over the Western North Atlantic

We report postfrontal clouds (PFC) are ubiquitous in the marine boundary layer, and their morphology is essential to estimating the radiation budget in weather and climate models. Here we examine the roles of sea surface temperature (SST) and meteorological factors in controlling the mesoscale morphology and evolution of shallow clouds associated with a cold-air outbreak that occurred on 1 March 2020 during phase I of the Aerosol Cloud Meteorology Interactions over the Western Atlantic Experiment (ACTIVATE). Our results show that the simulated PFC structure and ambient conditions by the Weather Research and Forecasting (WRF) Model are generally consistent with observations from GOES-16 and dropsonde measurements. We also examine the thermodynamical and dynamical influences in the cloud mesoscale morphology using WRF sensitivity experiments driven by two meteorological forcing datasets with different domain-mean SST and spatial gradients, which lead to dissimilar values of hydrometeor water path and cloud core fraction. The SST from ERA5 leads to weaker stability and higher inversion height than the SST from FNL does. In addition, the use of large-scale meteorological forcings from ERA5 yields a distinctive time evolution of wind direction shear in the inner domain, which favors the formation and persistence of longer cloud rolls. Both factors contribute to a change in the time evolution of domain-mean water path and cloud core fraction of cloud streets. Our study takes advantage of the simulation driven by the differences between two large-scale forcing datasets to illustrate the importance of SST and wind direction shear in the cloud street morphology in a realistic scenario.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Colloidal Nafion Particles: Are Cylinders Ubiquitous?

Colloidal Nafion morphology plays a critical role in determining the performance of fuel cells and electrolyzers. While small-angle neutron scattering (SANS) studies previously described Nafion in liquid media as dispersed cylinders, the analysis remains nonunique with multiple possible morphological descriptions of the data. Here, using SANS and all-atomistic molecular dynamics, we confirm that Nafion morphology in liquid media differs substantially depending on dispersing agent and dispersion method. H + Nafion dispersed in N-methyl pyrrolidone forms swollen cluster particles with physically cross-linked ionic groups. Scattering profiles from dispersed Nafion membrane have a large structure factor feature not observed for redispersed Nafion D-521. H + Nafion dispersed in water has a highly elongated cylindrical morphology (radius = 10 ± 1.5 Å, height = 358 ± 4.7 Å) with fully dissociated and solvated sulfonic acid groups on the particle wall. In conclusion, these results highlight an important discrepancy between the methods of preparing Nafion dispersions and the use of simplified analysis techniques to describe Nafion morphology.

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

Co-Optimization of Velocity and Charge-Depletion for Plug-In Hybrid Electric Vehicles: Accounting for Acceleration and Jerk Constraints

Abstract Recent advances in vehicle connectivity and automation technologies promote advanced control algorithms that co-optimize the longitudinal dynamics and powertrain operation of hybrid electric vehicles. Typically, a sequential optimization with the vehicle dynamics optimized followed by powertrain optimization is adopted to manage a number of complexities such as the inherent mixed-integer nature of the hybrid powertrain, the numerous state and control variables, the differing time scales of vehicle and powertrain subsystems, time-varying state constraints, and large horizon lengths. Instead, we solve the offline optimization problem in a centralize manner assuming exact knowledge of the lead vehicle's position over the entire trip by applying a discrete-time single shooting-based numerical approach, Discrete Mixed-Integer Shooting (DMIS), including a linearly increasing computational complexity to the problem horizon. In particular, the hierarchical problem structure is exploited to decompose the computationally intensive Hamiltonian minimization step into a set of low-dimensional optimizations. DMIS allows us to compute the direct fuel minimization problem including the vehicle and powertrain dynamics in a centralized manner to its full horizon while systematically tuning weighting factors that penalize passenger discomfort. For the first time, this study reveals that practically implemented sequential optimization exhibits similar fuel optimality as co-optimization when a certain level of passenger comfort is required.

Automation & Control Systems↗
DOE OSTI2020

Damping and mode shape modification for additively manufactured walls with captured powder

Here, this paper describes the fabrication, modeling, and dynamic testing of laser powder bed fusion stainless steel walls with captured powder cores. The purpose of the study is to determine the increased structural damping, or energy dissipation, and mode shape modification caused by the inclusion of the unmelted powder core within the solid walls. It is shown experimentally that the damping increases with larger powder core width and that the damping addition is mode dependent. Damping increases over the solid wall values by factors of 2.9–225 are reported depending on the mode number and core width. It is also seen that the mode shapes are distorted relative to the solid wall results as the core width is increased and wall thickness is decreased. Comparisons with finite element models confirm this trend.

42 ENGINEERING↗
DOE OSTI2023

Molecular Dynamics Investigation of Nanoscale Hydrophobicity of Polymer Surfaces: What Makes Water Wet?

The wettability of a polymer surface–related to its hydrophobicity or tendency to repel water–can be crucial for determining its utility, such as for a coating or a purification membrane. While wettability is commonly associated with the macroscopic measurement of a contact angle between surface, water, and air, the molecular physics that underlie these macroscopic observations are not fully known, and anticipating relative behavior of different polymers is challenging. To address this gap in molecular-level understanding, we use molecular dynamics simulations to investigate and contrast interactions of water with six chemically distinct polymers: polytetrafluoroethylene, polyethylene, polyvinyl chloride, poly(methyl methacrylate), Nylon 66, and polyvinyl alcohol. We show that several prospective quantitative metrics for hydrophobicity agree well with experimental contact angles. Moreover, the behavior of water in proximity to these polymer surfaces can be distinguished with analysis of interfacial water dynamics, extent of hydrogen bonding, and molecular orientation–even when macroscopic measures of hydrophobicity are similar. The predominant factor dictating wettability is found to be the extent of hydrogen bonding between polymer and water, but the precise manifestation of hydrogen bonding and its impact on surface-water structure varies. In the absence of hydrogen bonding, other molecular interactions and polymer mechanics control hydrophobic ordering. Furthermore, these results provide new insights into how polymer chemistry specifically impacts water-polymer interactions and translates to surface hydrophobicity. Such factors may facilitate the design or processing of polymer surfaces to achieve targeted wetting behavior, and presented analyses can be useful in studying the interfacial physics of other systems.

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

Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide

We propose an active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential (GAP). Our active learning scheme consists of an unsupervised machine learning (ML) scheme coupled with a Bayesian optimization technique that evaluates the GAP model. We apply this scheme to a Hafnium dioxide (HfO 2 ) dataset generated from a "melt-quench" ab initio molecular dynamics (AIMD) protocol. Our results show that the active learning scheme, with no prior knowledge of the dataset, is able to extract a configuration that reaches the required energy fit tolerance. Further, molecular dynamics (MD) simulations performed using this active learned GAP model on 6144 atom systems of amorphous and liquid state elucidate the structural properties of HfO 2 with near ab initio precision and quench rates (i.e., 1.0 K/ps) not accessible via AIMD. The melt and amorphous X-ray structural factors generated from our simulation are in good agreement with experiment. In addition, the calculated diffusion constants are in good agreement with previous ab initio studies.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Mechanism of monovalent and divalent ion mobility in Nafion membrane: An atomistic simulation study

Polymer cation exchange membranes (CEMs) are widely used in water treatment processes. The fundamental factors that control the separation and selectivity of cations with different valences in CEMs are not fully understood. In this study, we use atomistic molecular dynamics simulations to investigate the underlying molecular mechanisms that control the mobility of cations with different valences in Nafion membranes. Our results indicate structural differences in binding of monovalent and divalent cations, which gives rise to differences in ion diffusion in Nafion. Monovalent cations are shown to be “territorially” bound, i.e., bound while partially hydrated, to the fixed charge groups whereas divalent cations are shown to be “site” bound, i.e., bound while fully dehydrated, to the charge groups on the polymer. This difference in binding structure gives rise to differences in transport characteristics of cations in Nafion.

Chemistry↗
DOE OSTI2026

Lessons Learned from Ecosystem-Scale Experimental Field Studies (Workshop Report)

Efforts to understand and predict ecosystem responses to environmental change require long-term, large-scale, spatially representative experiments and observations that capture natural variability, test predictive models, and generate transferable knowledge. Such studies are indispensable for unraveling the complexities of terrestrial ecosystems and their responses to disturbances and evolving environmental conditions, while generating the data necessary for developing mechanistic models and predictive tools that inform decision-making processes. Having a rich history of designing and executing large-scale ecosystem experiments, the U.S. Department of Energy’s Environmental System Science program convened a workshop in January 2025 that brought together leaders in the field to distill critical lessons from decades of experience in large-scale experiments. The workshop aimed to (1) provide an ecosystem experiment primer for best practices, thus ensuring a high scientific return on investment for funding agencies, and (2) offer a robust framework for the design and management of future research initiatives. This report synthesizes insights and experiences from workshop participants and is structured to capture the entire research life cycle, from goal setting and design to operations, adaptive management, team dynamics, collaborations, and the often overlooked aspect of decommissioning. By synthesizing decision-making and lessons learned across diverse research approaches, the report aims to provide a template of essential factors to consider when designing successful long-term, large-scale ecosystem experiments.

54 ENVIRONMENTAL SCIENCES↗