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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Reductive Dynamic and Static Excited State Quenching of a Homoleptic Ruthenium Complex Bearing Aldehyde Groups

A new homoleptic Ru polypyridyl complex bearing two aldehyde groups on each bipyridine ligand, [Ru(dab) 3 ](PF 6 ) 2 , where dab is 4,4′-dicarbaldehyde-2,2′-bipyridine, was synthesized, characterized, and utilized for iodide photo-oxidation studies. In acetonitrile (CH 3 CN) solution, the complex displayed an intense metal-to-ligand charge transfer (MLCT) absorbance maximum at 475 nm (ε = 22,000 M –1 cm –1 ) and an infrared (IR) band at 1712 cm –1 assigned to the pendent aldehyde groups. Visible light excitation in air-saturated solution resulted in room temperature photoluminescence (PL) with a maximum at 675 nm, a quantum yield, ϕ PL = 0.048, and an excited state lifetime, τ ο = 440 ns, from which radiative and nonradiative relaxation rate constants were extracted, k r = 9.1 × 10 4 s –1 and knr = 1.8 × 10 6 s –1 . Pulsed visible light excitation yielded transient UV–vis and IR absorption spectra consistent with an MLCT excited state; relaxation occurred with the maintenance of two isosbestic points in the visible region, and a lifetime that agreed with that measured by time-resolved PL. Cyclic voltammetry studies in a CH 3 CN solution with 0.1 M TBAPF 6 electrolyte revealed a quasi-reversible oxidation, E°(Ru III/II ) = +1.25 V vs. Fc +/0 , and three sequential one-electron reductions at −1.10, −1.25, and −1.54 V vs. Fc +/0 . Here, an excited state reduction potential of E°(Ru *2+/+ ) = +0.89 V vs. Fc +/0 was estimated with the Rehm–Weller expression. Titration of tetrabutylammonium iodide, TBAI, into a CD 3 CN solution of [Ru(dab) 3 ](PF 6 ) 2 resulted in significant shifts in the aldehyde H atom and 3,3′-biypridyl resonances that were analyzed with a 1:1 equilibrium model, from which K eq = 460 M –1 was extracted, increasing to 5800 M –1 when the solvent was changed to acetone-d 6 . Iodide titrations resulted in a significant quenching of the [Ru(dab) 3 ] *2+ lifetime and quantum yield in both CH 3 CN and acetone solvents. In CH 3 CN, the quenching was mainly dynamic and well described by the Stern–Volmer model, from which a quenching rate constant, k q , of 4.5 × 10 10 M –1 s –1 and an equilibrium constant, K eq , of 8.3 × 10 3 M –1 were obtained. In acetone, the static quenching pathway by iodide was greatly enhanced, with a K eq of 1.2 × 10 4 M –1 and a higher k q of 9.2 × 10 10 M –1 s –1 .

Aldehydes↗
DOE OSTI2021

Revealing High-Temperature Reduction Dynamics of High-Entropy Alloy Nanoparticles via In Situ Transmission Electron Microscopy

Understanding the behavior of high-entropy alloy (HEA) materials under hydrogen (H 2 ) environment is of utmost importance for their promising applications in structural materials, catalysis, and energy-related reactions. Herein, the reduction behavior of oxidized FeCoNiCuPt HEA nanoparticles (NPs) in atmospheric pressure H-2 environment was investigated by in situ gas-cell transmission electron microscopy (TEM). The reduction reaction front was maintained at the external surface of the oxide. During reduction, the oxide layer expanded and transformed into porous structures where oxidized Cu was fully reduced to Cu NPs while Fe, Co, and Ni remained in the oxidized form. In situ chemical analysis showed that the expansion of the oxide layer resulted from the outward diffusion flux of all transition metals (Fe, Co, Ni, Cu). Revealing the H-2 reduction behavior of HEA NPs facilitates the development of advanced multicomponent alloys for applications targeting H 2 formation and storage, catalytic hydrogenation, and corrosion removal.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

A Dynamic Reduction Network for Point Clouds

Classifying whole images is a classic problem in machine learning, and graph neural networks are a powerful methodology to learn highly irregular geometries. It is often the case that certain parts of a point cloud are more important than others when determining overall classification. On graph structures this started by pooling information at the end of convolutional filters, and has evolved to a variety of staged pooling techniques on static graphs. In this paper, a dynamic graph formulation of pooling is introduced that removes the need for predetermined graph structure. It achieves this by dynamically learning the most important relationships between data via an intermediate clustering. The network architecture yields interesting results considering representation size and efficiency. It also adapts easily to a large number of tasks from image classification to energy regression in high energy particle physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Discrete Empirical Interpolation Method Based Dynamic Load Model Reduction

Dynamic load models add significant complexity to bulk power system time-domain simulations. The complexity is due to the large number of ordinary differential equations (ODEs) introduced by the dynamic load components such as induction motors. It is challenging to derive reduced-order models (ROMs) for dynamic loads due to the nonlinear functions in their governing equations. This paper applies the discrete empirical interpolation method enhanced proper orthogonal decomposition (DEIM-POD) to approximate the full dynamic load model with the ROM that minimizes the projection error of the nonlinear functions in dynamic load ODEs onto their dominant modes. This approach only requires evaluation of nonlinear functions at selected observation points. The observation points selected by DEIM also provide information for screening critical load buses where dynamic load model parameters contribute the most to the accuracy of ROM across multiple contingencies. The proposed approach is validated on IEEE 9-bus, WECC 179-bus and 2384-bus Polish systems.

bulk power system↗
DOE OSTI2021

The role of sub-surface hydrogen on CO 2 reduction and dynamics on Ni(110): An ab initio molecular dynamics study

The catalytic reduction of carbon dioxide is a crucial step in many chemical industrial reactions, such as methanol synthesis, the reverse water-gas shift reaction (rWGS) and formic acid synthesis. Here, we investigate the role of bulk hydrogen, where hydrogen atoms are found deep inside a metal surface as opposed to subsurface ones, upon CO 2 reduction over a Ni(110) surface using density functional theory (DFT) and ab initio molecular dynamics (AIMD) simulations. While it has previously been shown that subsurface hydrogen stabilizes CO 2 and can aid in overcoming reaction barriers, the role of bulk hydrogen is less studied and thus unknown with regard to CO 2 reduction. Furthermore, we find that the presence of bulk hydrogen can significantly alter the electronic structure of the Ni(110) surface, particularly the work function and d-band center, such that CO 2 adsorbs more strongly to the surface and is more easily reduced. Our results show an enhanced CO 2 dissociation in the presence of bulk hydrogen, shedding light on a hitherto underappreciated mechanistic pathway for CO 2 reduction on metal surfaces.

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

Dynamic Model Reduction for Large-Scale Power Systems Using Wide-Area Measurements

To perform faster than the real-time dynamic simulation of large-scale power systems, it is necessary to reduce the simulated system size by using equivalents for surrounding areas of the study area, and existing dynamic model reduction approach could provide the needed structure of the reduced area. However, further parameter optimization is required to achieve the desired accuracy. In this paper, a particle swarm optimization (PSO) based approach is used to solve the above problem. Parameters for the individual dynamic elements in the reduced system are calibrated repeatedly until the wide-area measurements of the reduced model and the original model are very similar to each other with satisfactory accuracy. Results indicate that after optimization, the dynamic response of the reduced model matches better with that of the original one than using existing methods. Under both the generator-trip event and the bus-fault event, the reduced model has a higher frequency match and less power mismatch.

42 ENGINEERING↗
DOE OSTI2020

Windowed least-squares model reduction for dynamical systems

Here we propose a windowed least-squares (WLS) approach for model reduction of dynamical systems. The proposed approach sequentially minimizes the time-continuous full-order-model residual within a low-dimensional space–time trial subspace over time windows. The approach comprises a generalization of existing model reduction approaches, as particular instances of the methodology recover Galerkin, least-squares Petrov–Galerkin (LSPG), and space–time LSPG projection. In addition, the approach addresses key deficiencies in existing model reduction techniques, e.g., the dependence of LSPG and space–time LSPG projection on the time discretization and the exponential growth in time exhibited by a posteriori error bounds for both Galerkin and LSPG projection. We consider two types of space–time trial subspaces within the proposed approach: one that reduces only the spatial dimension of the full-order model, and one that reduces both the spatial and temporal dimensions of the full-order model. For each type of trial subspace, we consider two different solution techniques: direct (i.e., discretize then optimize) and indirect (i.e., optimize then discretize). Numerical experiments conducted using trial subspaces characterized by spatial dimension reduction demonstrate that the WLS approach can yield more accurate solutions with lower space–time residuals than Galerkin and LSPG projection.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Dynamic Species Reduction for Multi-Cycle CFD Simulations (Final Technical Report)

This project primarily sought to address some of the computational cost concerns of detailed simulations by developing improved methods of handling species transport, and chemical kinetics evaluation in a commercial 3D Computational Fluid Dynamics (CFD) environment. Secondary goals were to apply these techniques to fuels and conditions of interest to better understand the key species and reactions required to adequately capture cycle to cycle coupling. Improved modeling will lead to better understanding of these combustion modes and their dependence on fuel composition, which can then enable more clean and efficient engines, ultimately benefiting the consumer as well as the general public. Two approaches were used to address the computational cost. A “Dynamic Species Reduction” (DSR) procedure was developed to remove chemical species from the simulation during periods when chemical reactions were not expected to be important, particularly during gas exchange when temperatures are low and little fuel remains. This modelling procedure automatically detects relevant species to retain in the simulation domain based on their local concentration, removes species below a specified concentration threshold, and then adjusts the remaining species mass to conserve not only the number of H, C, and O atoms in each cell, but also the relative proportions between species and the heating value of the mixture in every cell. The second procedure was a “Product Directed Remapping” (PDR) updates the algorithm used to group individual computational cells for chemical kinetics evaluation to account for non-uniform temperature distributions and the CO to CO 2 ratio in the cells. The model techniques developed in this work successfully demonstrated computational performance improvements for a range of conditions relevant for Highly Dilute SI and HCCI engine operation. Runtime reductions of 10% were observed for small mechanisms, with further reductions of up to 36% observed for larger mechanisms. Improvements were primarily related to reducing the number of chemical species tracked during the gas exchange process using the Dynamic Species Reduction method. With smaller benefits observed from changes to the kinetics binning and evaluation strategy in the post combustion region using the PDR method. The results of this work show the potential for improved computational runtime for complicated simulations. They can and should be extended to additional conditions and new bio-derived and renewable fuels as they are developed and new kinetic mechanisms become available.

10 SYNTHETIC FUELS↗
DOE OSTI2020

Reentrant pinning, dynamic row reduction, and skyrmion accumulation for driven skyrmions in inhomogeneous pinning arrays

In this work, we examine the dynamics of skyrmions in systems with a region of strong pinning coexisting with a pin-free region, where the equilibrium state has a uniform skyrmion density. Under an applied drive, skyrmions accumulate in the pin-free region along the edge of the pinned region due to the skyrmion Hall effect. As the drive increases, a series of dynamical structural transitions occur in the flowing skyrmion lattice similar to those observed in the compression dynamics of crystals. These transitions correspond to reductions in the number of flowing rows of skyrmions due to the collective motion of the skyrmions into the pinned region, and they are accompanied by a series of steps in the velocity force curves leading to a negative differential conductivity. When the number of pinning sites is sufficiently large, a drive-induced pinning effect can occur in which the skyrmion Hall effect forces all of the skyrmions to enter the strongly pinned region. This reentrant pinning effect becomes more pronounced for increasing intrinsic skyrmion Hall angle and larger pinning sites.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Extended dynamic mode decomposition for model reduction in fluid dynamics simulations

High computational cost and storage/memory requirements of fluid dynamics simulations constrain their usefulness as a predictive tool. Reduced-order models (ROMs) provide a viable solution to this challenge by extracting the key underlying dynamics of a complex system directly from data. We investigate the efficacy and robustness of an extended dynamic mode decomposition (xDMD) algorithm in constructing ROMs of three-dimensional cardiovascular computations. Focusing on the ROMs' accuracy in representation and interpolation, we relate these metrics to the truncation rank of singular value decomposition, which underpins xDMD and other approaches to ROM construction. Our key innovation is to relate the truncation rank to the singular values of the original flow problem. This result establishes a priori guidelines for the xDMD deployment and its likely success as a means of data compression and reconstruction of the system's dynamics from dominant spatiotemporal structures present in the data.

Mechanics↗
DOE OSTI2026

Cross-Linker Selection Controls Glass Transition Elevation or Reduction in Dynamic Covalently Bonded Polymer Networks

Introducing cross-links is a powerful approach to improve polymeric material performance relevant to controlling viscoelasticity, thermal and creep resistance, degradability, and efficient membrane separations. The chemically specific glass transition temperature T g is of fundamental importance in determining the time scales of key dynamical processes and physical state of the material in such applications. Here, we study experimentally how the introduction of relatively large cross-linking molecules in slowly exchanging dynamic bond-forming polymers (vitrimers) impacts vitrification for diverse polymer chemistries and a wide range of cross-link fractions. We find T g can increase, decrease, or even remain essentially unchanged, in qualitative contrast to the generic elevation of T g in traditional permanent polymer networks. We formulate an effective terpolymer network model to understand this rich behavior, which emerges as a consequence of a competition between pure cross-linking and generalized plasticization effects. The latter is associated with the tunable cross-linker size and intrinsic dynamic mobility that can offset slowing down due to traditional permanent cross-linking constraints. Here, a new strategy for functional polymer network design is suggested based on adjusting the relative importance of the two competing physical effects, which potentially can significantly enhance energy savings in applications while retaining other intrinsic properties germane to advanced materials performance.

Copolymers↗
DOE OSTI2025

Atomic dynamics of gas-dependent oxide reducibility

Understanding oxide reduction is critical for advancing metal production, catalysis and energy technologies. Although carbon monoxide (CO) and hydrogen (H 2 ) are widely used reductants, the mechanisms by which they work are often presumed to be similar, both involving lattice oxygen removal. However, because of growing interest in replacing CO with H 2 to lower CO 2 emissions, distinguishing gas-specific reduction pathways is critical. Yet, capturing these atomic-scale processes under reactive gas and high-temperature conditions remains challenging. Here we use environmental transmission electron microscopy, which is capable of real-time, atomic-resolution imaging of gas–solid redox reactions to directly visualize the gas-dependent oxide reduction dynamics in NiO. We show that CO drives surface nucleation and the growth of metallic Ni islands, leading to self-limiting surface metallization. Conversely, H 2 activates a coupled surface-to-bulk transformation, where protons from dissociated H 2 infiltrate the oxide lattice to promote the inward migration of surface-generated oxygen vacancies and enabling bulk metallization. By contrast, oxygen vacancies formed by CO remain confined near the surface, where they rapidly form a metallic Ni layer that inhibits further reduction. Furthermore, these results reveal distinct atomistic pathways for CO and H 2 and provide insights that may guide metallurgical processes and catalyst design.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Rapid data-driven model reduction of nonlinear dynamical systems including chemical reaction networks using ℓ 1 -regularization

We develop a new data-driven paradigm for efficient model reduction of a broad class of nonlinear dynamical systems. Our model reduction method directly enables the interpretation of key components of the dynamical system, unlike traditional projection-based model reduction methods that focus on reducing computational complexity more than interpretability. Our method is not application specific and is simple to implement on nonlinear dynamical systems arising from a variety of different fields. It requires minimal parameterization using a single parameter to trade-off between model complexity and estimation error. We use a data-driven paradigm to formulate model reduction as an efficient convex optimization problem that scales polynomially in the original size of the complex system, enabling systems with as many as thousands of components to be reduced in a matter of minutes.

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

Ultrafast Electron Correlations and Memory Effects at Work: Femtosecond Demagnetization in Ni

Experimental observations of the ultrafast (less than 50 fs) demagnetization of Ni have so far defied theoretical explanations particularly since its spin-flipping time is much less than that resulting from spin-orbit and electron-lattice interactions. Through the application of an approach that benefits from spin-flip time-dependent density-functional theory and dynamical mean-field theory, we show that proper inclusion of electron correlations and memory (time dependence of electron-electron interaction) effects leads to demagnetization at the femtosecond scale, in good agreement with experimental observations. Furthermore, our calculations reveal that this ultrafast demagnetization results mainly from spin-flip transitions from occupied to unoccupied orbitals implying a dynamical reduction of exchange splitting. In this work, these conclusions are found to be valid for a wide range of laser pulse amplitudes. They also pave the way for ab initio investigations of ultrafast charge and spin dynamics in a variety of quantum materials in which electron correlations may play a definitive role.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Is a Malleable Active Site Loop the Key to High Substrate Promiscuity? Hybrid, Biocatalytic Route to Structurally Diverse Taxoid Side Chains with Remarkable Dual Stereocontrol

Abstract These studies reveal the first structure ofClostridium acetobutylicumalcohol dehydrogenase (CaADH), a protein exhibiting remarkable substrate promiscuity and stereochemical fidelity. The CaADH enzyme is utilized here for synthesizing 20 potential aryl isoserine side chains for the Taxotere family of tubulin‐binding chemotherapeutics. The approach involves dynamic reductive kinetic resolution (DYRKR) upon the corresponding α‐chloro‐β‐keto esters, showing high D‐synstereoselectivity, including those leading to the clinically relevant milataxel (Ar = 2‐furyl) and simotaxel (Ar = 2‐thienyl) side chains. Furthermore, various cross‐coupling chemistries performed on thep‐bromophenyl isoserine side chain significantly enhance the structural diversity of the taxoid side chain library obtained (16 additional taxoid side chains). The CaADH structure is notable: (i) the nicotinamide cofactor is bound in ananti‐conformation, with the amide carbonyl occupying the ketone binding pocket, and (ii) a flexible loop near the active site likely contributes to the remarkable substrate promiscuity observed in CaADH. We present our perspective on the dynamic nature of the CaADH active site through molecular dynamics simulation, proposing a halogen bonding model as a potential mechanism for the remarkable selectivity for an (S)‐configured C─Cl bond, in addition to the D‐facial selectivity, demonstrated across 20 diverse substrates by this remarkable short‐chain dehydrogenase enzyme.

Chemistry↗