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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Anisotropic Motions of Fibrils Dictated by Their Orientations in the Lamella: A Coarse-Grained Model of a Plant Cell Wall

Plant cell walls are complex systems that exhibit the characteristics of both rigid and soft material depending on their external perturbations. The three main polymeric components in a plant primary cell wall are cellulose fibrils, hemicellulose, and pectins. These components interact in a hierarchical fashion giving rise to mesoscale structural features such as cellulose bundles, lamella stacking, and so on. Although several studies have focused on understanding these unique structural features, a clear picture linking them to cell wall mechanics is still lacking. As a first step toward this goal, a phenomenological model of plant cell wall has been developed in this work by using available experimental data to investigate the underlying connections between mesoscale structural features and the motions of fibrils during deformation. In this model cellulose fibrils exhibit motions such as angular reorientations and kinking upon forced stretching. Furthermore, these motions are dependent on the orientation of fibrils with respect to the stretch direction, i.e., fibrils that are at an angle to the stretch direction exhibit predominant angular reorientations, while fibrils transverse to the stretch direction undergo kinking as a result of transverse compression. Varying the chain length of pectin had negligible effects on these motions. One of the main contributions from this work is the development of a simple model that can be easily fine-tuned to test other hypotheses and extended to include additional experimental knowledge about the structural aspects of cell walls in the future.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Molecular Simulation of Covalent Bond Dynamics in Liquid Silicon

Many atomic liquids can form transient covalent bonds reminiscent of those in the corresponding solid states. These directional interactions dictate many important properties of the liquid state, necessitating a quantitative, atomic-scale understanding of bonding in these complex systems. A prototypical example is liquid silicon, wherein transient covalent bonds give rise to local tetrahedral order and consequent nontrivial effects on liquid-state thermodynamics and dynamics. To further understand covalent bonding in liquid silicon, and similar liquids, we present an ab initio-simulation-based approach for quantifying the structure and dynamics of covalent bonds in condensed phases. Through the examination of structural correlations among silicon nuclei and maximally localized Wannier function centers, we develop a geometric criterion for covalent bonds in liquid Si. We use this to monitor the dynamics of transient covalent bonding in the liquid state and estimate a covalent bond lifetime. We compare covalent bond dynamics to other processes in liquid Si and similar liquids and suggest experiments to measure the covalent bond lifetime.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Capturing Surface Coverage Effects in Heterogeneous Catalysis

Adsorbate–adsorbate lateral interactions at relevant surface coverages have a significant effect on chemical kinetics, thereby influencing the activity of a heterogeneous catalyst. Coverage-dependent kinetic and thermodynamic parameters therefore must be included in studies of such complex systems to properly predict the turnover frequencies and kinetic trends. Thus, it becomes extremely important to accurately capture the strength of lateral interactions between neighboring species under realistic reaction conditions. In this Perspective, we discuss the various existing computational and experimental methods for determining adspecies coverage and configurational effects. The choice of the tools and methods employed in such studies depends on factors such as time, length scales, computational cost, the presence of solvents, and reaction conditions. The applications of each method and the respective challenges are also discussed here. As a result, we discuss the recent developments and future of the state-of-the-art for inclusion of surface coverage and configuration into a holistic picture for accurate predictions of catalytic behavior.

09 BIOMASS FUELS↗
DOE OSTI2023

An Efficient Reactive Force Field without Explicit Coordination Dependence for Studying Caustic Aluminum Chemistry

Reactive force fields (RFFs) are an expedient approach to sample chemical reaction paths in complex systems, relative to density functional theory. However, there is continued need to improve efficiencies, specifically in systems that have slow transverse degrees of freedom, as in highly viscous and superconcentrated solutions. Here, we present an RFF that is differentiated from current models (e.g., ReaxFF) by omitting explicit dependence on the atom coordination and employing a small parameter set based on Lennard-Jones, Gaussian, and Stillinger–Weber potentials. In this study, the model was parametrized from AIMD simulation data and is used to model aluminate reactivity in sodium hydroxide solutions with extensive validation against experimental radial distribution functions, computed free energy profiles for oligomerization, and formation energies. In conclusion, the model enables simulation of early stage Al(OH) 3 nucleation which has significant relevance to industrial processing of aluminum and has a computational cost that is reduced by 1 order of magnitude relative to ReaxFF.

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

How Cooperatively Folding Are Homopolymer Molecular Knots?

Detailed thermodynamic analysis of complex systems with multiple stable configurational states allows for insight into the cooperativity of each individual transition. In this work, we derive a heat capacity decomposition comprising contributions from each individual configurational state, which together sum to a baseline heat capacity, and contributions from each state-to-state transition. We apply this analysis framework to a series of replica exchange molecular dynamics simulations of linear and 1-1 coarse-grained homo-oligomer models, which fold into stable, configurationally well-defined molecular knots, in order to better understand the parameters leading to stable and cooperative folding of these knots. We find that a stiff harmonic backbone bending angle potential is key to achieving knots with specific 3D structures. Tuning the backbone equilibrium angle in small increments yields a variety of knot topologies. Populations of different knotted states as a function of temperature can also be manipulated by tuning the backbone torsion stiffness or by adding side-chain beads. We find that sharp total heat capacity peaks for the homo-oligomer knots are largely due to a coil-to-globule transition rather than a cooperative knotting step. Furthermore, in some cases, the cooperativity of globule-to-knot and coil-to-globule transitions is comparable, suggesting that highly cooperative folding to knotted structures can be achieved by refining the model parameters or adding sequence specificity.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Quantifying the Dynamics of Protein Self-Organization Using Deep Learning Analysis of Atomic Force Microscopy Data

The dynamics of protein self-assembly on the inorganic surface and the resultant geometric patterns are visualized using high-speed atomic force microscopy. The time dynamics of the classical macroscopic descriptors such as 2D fast Fourier transforms, correlation, and pair distribution functions are explored using the unsupervised linear unmixing, demonstrating the presence of static ordered and dynamic disordered phases and establishing their time dynamics. Here, the deep learning (DL)-based workflow is developed to analyze detailed particle dynamics and explore the evolution of local geometries. Finally, we use a combination of DL feature extraction and mixture modeling to define particle neighborhoods free of physics constraints, allowing for a separation of possible classes of particle behavior and identification of the associated transitions. Overall, this work establishes the workflow for the analysis of the self-organization processes in complex systems from observational data and provides insight into the fundamental mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Incorporating Coverage-Dependent Reaction Barriers into First-Principles-Based Microkinetic Models: Approaches and Challenges

Mean-field microkinetic models (MKMs) are appealing for their relatively facile construction, computational tractability, and high-throughput catalyst screening capabilities. As such, they will continue to be a valuable tool for materials design in heterogeneous catalysis even as the field aims to describe more complex systems. Numerous prior reports have provided the groundwork for constructing first-principles-based MKMs, including the analysis of strategies for incorporating lateral interactions into thermodynamic parameters (e.g., adsorption energies). Yet, there remains a need for concerted dialogue on methods for calculating and incorporating coverage-dependent kinetic parameters into MKMs. In this Perspective, we assess strategies for doing so, including the corresponding key physical implications and computational challenges. Here, we emphasize that decoupling thermodynamic and kinetic parameters within MKMs can violate thermodynamic consistency and risk unphysical solutions. For some reactions and catalyst materials, scaling relationships can predict coverage-dependent activation energies, but there are several exceptions evident in the literature, indicating that this approach is not universally applicable and that the field could benefit from research aimed at elucidating the limitations. Conducting high-coverage transition state searches is a rigorous but computationally costly strategy, and the effects of various methods for mitigating this cost on resulting energetics have yet to be broadly explored and validated. The goal of this Perspective is to generate discussion on and inspire focused research into the physical relevance of approaches for describing coverage-dependent reaction barriers in MKMs, including the development of computationally tractable methodologies, to advance the applicability of MKMs across diverse reaction chemistries and conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Polarization-Insensitive Medium-Switchable Holographic Metasurfaces

The adoption of metasurfaces has led to revolutionary advances in holography due to improved compactness, integrability, and performance. Switchable meta-holograms projecting different replay field images in a controllable manner are highly desirable. Still, existing technologies generally rely on the use of polarized light and additional optics to facilitate switching. Consequently, the potential benefits afforded through the use of metasurfaces are limited both by the system complexity and a fixed relationship between the optical input and output. In this manuscript, we demonstrate polarization-insensitive metasurfaces encoding arbitrary and independent holograms, which can be switched between by changing the refractive index of the infiltration medium while maintaining identical illumination conditions. Finally, by sidestepping the requirements for high-performance light sources, switching optics, or delicate alignment, this approach points toward ultracompact and cost-effective switchable meta-holograms for various practical applications, such as holographic image projection, eye-perceptible sensors, optical information storage, processing, and security.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Protein Electric Fields Enable Faster and Longer-Lasting Covalent Inhibition of β-Lactamases

The widespread design of covalent drugs has focused on crafting reactive groups of proper electrophilicity and positioning towards targeted amino-acid nucleophiles. In this work, we found that environmental electric fields projected onto a reactive chemical bond, an overlooked design element, play essential roles in the covalent inhibition of TEM-1 beta-lactamase by avibactam. Using the vibrational Stark effect, the magnitudes of the electric fields that are exerted by TEM active sites onto avibactam’s reactive C=O were measured and demonstrate an electrostatic gating effect that promotes bond formation yet relatively suppresses the reverse dissociation. These results suggest new principles of covalent drug design and off-target site prediction. Unlike shape and electrostatic complementary which address binding constants, electrostatic catalysis drives reaction rates, essential for covalent inhibition, and deepens our understanding of chemical reactivity, selectivity, and stability in complex systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Chemical Bonding and the Role of Node-Induced Electron Confinement

The chemical bond is the cornerstone of chemistry, providing a conceptual framework to understand and predict the behavior of molecules in complex systems. However, the fundamental origin of chemical bonding remains controversial and has been responsible for fierce debate over the past century. Here, in this study, we present a unified theory of bonding, using a separation of electron delocalization effects from orbital relaxation to identify three mechanisms [node-induced confinement (typically associated with Pauli repulsion, though more general), orbital contraction, and polarization] that each modulate kinetic energy during bond formation. Through analysis of a series of archetypal bonds, we show that an exquisite balance of energy-lowering delocalizing and localizing effects are dictated simply by atomic electron configurations, nodal structure, and electronegativities. The utility of this unified bonding theory is demonstrated by its application to explain observed trends in bond strengths throughout the periodic table, including main group and transition metal elements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Short-Wave Infrared Upconverting Nanoparticles

Optical technologies enable real-time, noninvasive analysis of complex systems but are limited to discrete regions of the optical spectrum. While wavelengths in the short-wave infrared (SWIR) window (typically, 1700-3000 nm) should enable deep subsurface penetration and reduced photodamage, there are few luminescent probes that can be excited in this region. Here, we report the discovery of lanthanide-based upconverting nanoparticles (UCNPs) that efficiently convert 1740 or 1950 nm excitation to wavelengths compatible with conventional silicon detectors. Screening of Ln3+ ion combinations by differential rate equation modeling identifies Ho3+/Tm3+ or Tm3+ dopants with strong visible or NIR-I emission following SWIR excitation. Experimental upconverted photoluminescence excitation (U-PLE) spectra find that 10% Tm3+-doped NaYF4 core/shell UCNPs have the strongest 800 nm emission from SWIR wavelengths, while UCNPs with an added 2% or 10% Ho3+ show the strongest red emission when excited at 1740 or 1950 nm. Mechanistic modeling shows that addition of a low percentage of Ho3+ to Tm3+-doped UCNPs shifts their emission from 800 to 652 nm by acting as a hub of efficient SWIR energy acceptance and redistribution up to visible emission manifolds. Parallel experimental and computational analysis shows rate equation models are able to predict compositions for specific wavelengths of both excitation and emission. These SWIR-responsive probes open a new IR bioimaging window, and are responsive at wavelengths important for vision technologies.

Qi, Xiao↗
DOE OSTI2021

Exploring the Potential of Using Carbonyl Sulfide to Track the Urban Biosphere Signal

Abstract Cities are implementing additional urban green as a means to capture CO 2 and become more carbon neutral. However, cities are complex systems where anthropogenic and natural components of the CO 2 budget interact with each other, and the ability to measure the efficacy of such measures is still not properly addressed. There is still a high degree of uncertainty in determining the contribution of the vegetation signal, which furthermore confounds the use of CO 2 mole fraction measurements for inferring anthropogenic emissions of CO 2 . Carbonyl sulfide (OCS) is a tracer of photosynthesis which can aid in constraining the biosphere signal. This study explores the potential of using OCS to track the urban biosphere signal. We used the Sulfur Transport and dEposition Model (STEM) to simulate the OCS concentrations and the Carnegie Ames Stanford Approach ecosystem model to simulate global CO 2 fluxes over the Bay Area of San Francisco during March 2015. Two observation towers provided measurements of OCS and CO 2 : The Sutro tower in San Francisco (upwind from the area of study providing background observations), and a tower located at Sandia National Laboratories in Livermore (downwind of the highly urbanized San Francisco region). Our results show that the STEM model works better under stable marine influence, and that the boundary layer height and entrainment are driving the diurnal changes in OCS and CO 2 at the downwind Sandia site. However, the STEM model needs to better represent the transport and boundary layer variability, and improved estimates of gross primary productivity for characterizing the urban biosphere signal are needed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Light quantum control of persisting Higgs modes in iron-based superconductors

The Higgs mechanism, i.e., spontaneous symmetry breaking of the quantum vacuum, is a cross-disciplinary principle, universal for understanding dark energy, antimatter and quantum materials, from superconductivity to magnetism. Unlike one-band superconductors (SCs), a conceptually distinct Higgs amplitude mode can arise in multi-band, unconventional superconductors via strong interband Coulomb interaction, but is yet to be accessed. Here we discover such hybrid Higgs mode and demonstrate its quantum control by light in iron-based high-temperature SCs. Using terahertz (THz) two-pulse coherent spectroscopy, we observe a tunable amplitude mode coherent oscillation of the complex order parameter from coupled lower and upper bands. The nonlinear dependence of the hybrid Higgs mode on the THz driving fields is distinct from any known SC results: we observe a large reversible modulation of resonance strength, yet with a persisting mode frequency. Together with quantum kinetic modeling of a hybrid Higgs mechanism, distinct from charge-density fluctuations and without invoking phonons or disorder, our result provides compelling evidence for a light-controlled coupling between the electron and hole amplitude modes assisted by strong interband quantum entanglement. Such light-control of Higgs hybridization can be extended to probe many-body entanglement and hidden symmetries in other complex systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Aging power spectrum of membrane protein transport and other subordinated random walks

Single-particle tracking offers detailed information about the motion of molecules in complex environments such as those encountered in live cells, but the interpretation of experimental data is challenging. One of the most powerful tools in the characterization of random processes is the power spectral density. However, because anomalous diffusion processes in complex systems are usually not stationary, the traditional Wiener-Khinchin theorem for the analysis of power spectral densities is invalid. Here, we employ a recently developed tool named aging Wiener-Khinchin theorem to derive the power spectral density of fractional Brownian motion coexisting with a scale-free continuous time random walk, the two most typical anomalous diffusion processes. Using this analysis, we characterize the motion of voltage-gated sodium channels on the surface of hippocampal neurons. Our results show aging where the power spectral density can either increase or decrease with observation time depending on the specific parameters of both underlying processes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Observation of site-selective chemical bond changes via ultrafast chemical shifts

The concomitant motion of electrons and nuclei on the femtosecond time scale marks the fate of chemical and biological processes. Here we demonstrate the ability to initiate and track the ultrafast electron rearrangement and chemical bond breaking site-specifically in real time for the carbon monoxide diatomic molecule. We employ a local resonant x-ray pump at the oxygen atom and probe the chemical shifts of the carbon core-electron binding energy. We observe charge redistribution accompanying core-excitation followed by Auger decay, eventually leading to dissociation and hole trapping at one site of the molecule. The presented technique is general in nature with sensitivity to chemical environment changes including transient electronic excited state dynamics. This work provides a route to investigate energy and charge transport processes in more complex systems by tracking selective chemical bond changes on their natural timescale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Highest fusion performance without harmful edge energy bursts in tokamak

The path of tokamak fusion and International thermonuclear experimental reactor (ITER) is maintaining high-performance plasma to produce sufficient fusion power. This effort is hindered by the transient energy burst arising from the instabilities at the boundary of plasmas. Conventional 3D magnetic perturbations used to suppress these instabilities often degrade fusion performance and increase the risk of other instabilities. This study presents an innovative 3D field optimization approach that leverages machine learning and real-time adaptability to overcome these challenges. Implemented in the DIII-D and KSTAR tokamaks, this method has consistently achieved reactor-relevant core confinement and the highest fusion performance without triggering damaging bursts. This is enabled by advances in the physics understanding of self-organized transport in the plasma edge and machine learning techniques to optimize the 3D field spectrum. The success of automated, real-time adaptive control of such complex systems paves the way for maximizing fusion efficiency in ITER and beyond while minimizing damage to device components.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Multicationic interactions mitigating lattice strain in sodium layered cathodes

Transition-metal (TM) layered oxides have emerged as the primary cathode choice for sodium-ion batteries (SIBs) due to their high energy density and sustainable chemistry using non-critical elements. However, their anisotropic lattice strain and stress accumulation during (de)sodiation lead to severe structural degradation, yet an intrinsic strain-depressant approach remains elusive. Herein, we propose entropy regulation with zero Li/Co usage to mitigate harmful lattice displacements and enhance the electrochemical performance of sodium layered cathodes. Our findings demonstrate that high entropy design effectively inhibits TMO 6 octahedra distortions upon cycling, as evidenced by hard X-ray absorption spectroscopy, greatly reducing near-surface structural deconstruction and interface side reactions. Furthermore, multicationic interactions driven by configurational entropy thermodynamically mitigate the formation of oxygen defects and strengthen ligand-to-metal coordination. The complementarity inherent in charge compensation within complex systems is unveiled and the restrained lattice parameters deviations without interior volume residuals are successfully achieved. As a result, the multicationic cathode exhibits improved cycling stability and Na + diffusion kinetics in both half and full cells. The cathode chemistries outlined here broaden the prospects for lattice engineering to alleviate bulk fatigue and open up the possibility to develop an economically viable layered oxides with long durability.

Wang, Haoji [Central South University, Changsha (C↗
DOE OSTI2020

Toward nanoscale molecular mass spectrometry imaging via physically constrained machine learning on co-registered multimodal data

Mass spectrometry imaging (MSI) plays a pivotal role in investigating the chemical nature of complex systems that underly our understanding in biology and medicine. Multiple fields of life science such as proteomics, lipidomics and metabolomics benefit from the ability to simultaneously identify molecules and pinpoint their distribution across a sample. However, achieving the necessary submicron spatial resolution to distinguish chemical differences between individual cells and generating intact molecular spectra is still a challenge with any single imaging approach. Here, we developed an approach that combines two MSI techniques, matrix-assisted laser desorption/ionization (MALDI) and time-of-flight secondary ion mass spectrometry (ToF-SIMS), one with low spatial resolution but intact molecular spectra and the other with nanometer spatial resolution but fragmented molecular signatures, to predict molecular MSI spectra with submicron spatial resolution. The known relationships between the two MSI channels of information are enforced via a physically constrained machine-learning approach and directly incorporated in the data processing. We demonstrate the robustness of this method by generating intact molecular MALDI-type spectra and chemical maps at ToF-SIMS resolution when imaging mouse brain thin tissue sections. This approach can be readily adopted for other types of bioimaging where physical relationships between methods have to be considered to boost the confidence in the reconstruction product.

97 MATHEMATICS AND COMPUTING↗