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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 OSTI2022

Experimental X-ray Charge-Density Studies–A Suitable Probe for Superconductivity? A Case Study on MgB 2

Case studies of 1T-TiSe 2 and YBa 2 Cu 3 O 7-δ have demonstrated that X-ray diffraction (XRD) studies can be used to trace even subtle structural phase transitions which are inherently connected with the onset of superconductivity in these benchmark systems. However, the utility of XRD in the investigation of superconductors like MgB 2 lacking an additional symmetry-breaking structural phase transition is not immediately evident. Nevertheless, high-resolution powder XRD experiments on MgB 2 in combination with maximum entropy method analyses hinted at differences between the electron density distributions at room temperature and 15 K, that is, below the T c of approx. 39 K. The high-resolution single-crystal XRD experiments in combination with multipolar refinements presented here can reproduce these results but show that the observed temperature-dependent density changes are almost entirely due to a decrease of atomic displacement parameters as a natural consequence of a reduced thermal vibration amplitude with decreasing temperature. Our investigations also shed new light on the presence or absence of magnesium vacancies in MgB 2 samples–a defect type claimed to control the superconducting properties of the compound. Here, we propose that previous reports on the tendency of MgB 2 to form non-stoichiometric Mg 1–x B 2 phases (1 – x ~ 0.95) during high-temperature (HT) synthesis might result from the interpretation of XRD data of insufficient resolution and/or usage of inflexible refinement models. Indeed, advanced refinements based on an Extended Hansen–Coppens multipolar model and high-resolution X-ray data, which consider explicitly the contraction of core and valence shells of the magnesium cations, do not provide any significant evidence for the formation of non-stoichiometric Mg 1–x B 2 phases during HT synthesis.

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

A Computational Study of RNA Tetraloop Thermodynamics, Including Misfolded States

An important characteristic of RNA folding is the adoption of alternative configurations of similar stability, often referred to as misfolded configurations. These configurations are considered to compete with correctly folded configurations, although their rigorous thermodynamic and structural characterization remains elusive. Tetraloop motifs found in large ribozymes are ideal systems for an atomistically detailed computational quantification of folding free energy landscapes and the structural characterization of their constituent free energy basins, including nonnative states. In this work, we studied a group of closely related 10-mer tetraloops using a combined parallel tempering and metadynamics technique that allows a reliable sampling of the free energy landscapes, requiring only knowledge that the stem folds into a canonical A-RNA configuration. Here we isolated and analyzed unfolded, folded, and misfolded populations that correspond to different free energy basins. We identified a distinct misfolded state that has a stability very close to that of the correctly folded state. This misfolded state contains a predominant population that shares the same structural features across all tetraloops studied here and lacks the noncanonical A-G base pair in its loop portion. Further analysis performed with biased trajectories showed that although this competitive misfolded state is not an essential intermediate, it is visited in most of the transitions from unfolded to correctly folded states. Moreover, the tetraloops can transition from this misfolded state to the correctly folded state without requiring extensive unfolding.

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

Comparative Study on the Machine Learning-Based Prediction of Adsorption Energies for Ring and Chain Species on Metal Catalyst Surfaces

Computation of adsorption and transition state energies for a large number of surface intermediates for numerous active site models pose significant computational overhead in computational screening of catalysts. Machine learning (ML) techniques can be used to predict part of these energies. To predict the energies, ML models need to be fed appropriate metal and species descriptors. For complex surface chemistries, the structures of the intermediate species can vary greatly. In this paper, working with the hydrodeoxygenation of succinic acid on six different metal surfaces, we have studied the effect of linear and non-linear ML models used along with pen-and-paper based species descriptors and two categories of metal descriptors on two different categories of intermediate species: chain and ring. More specifically, our computations include the prediction of chain species when trained on only chain species and also when trained on both chain and ring species. Similar computations were performed for predictions of ring species. In each case, results of linear ML models were compared with kernel based non-linear models. Our results indicate that ring species data does not improve the prediction of chain species. Similarly, chain species data does not improve the prediction of ring species. The use of non-linear ML models, however, did help to minimize the prediction errors compared to the linear models. Furthermore, the study also shows that electronic or adsorption energy based metal descriptors along with bond count based species fingerprints can achieve a mean absolute error (MAE) of less than 0.2 eV for complex chain molecules when used with an appropriate machine learning model.

Adsorption↗
DOE OSTI2020

Electrochemical Oxidation of Metal–Catechol Complexes as a New Synthesis Route to the High-Quality Ternary Photoelectrodes: A Case Study of Fe 2 TiO 5 Photoanodes

A new electrochemical, solution-based synthesis method to prepare uniform multinary oxide photoelectrodes was developed. This method involves solubilizing multiple metal ions as metal-catechol complexes in a pH condition where they are otherwise insoluble. When some of the catechol ligands are electrochemically oxidized, the remaining metal complexes become insoluble and are deposited as metal-catechol films on the working electrode. The resulting films are then annealed to form crystalline multinary oxide electrodes. Since catechol can serve as a complexing agent for a variety of metal ions, the newly developed method can be used to prepare a variety of multinary oxide films. In the present study, we used this method to prepare n-type Fe 2 TiO 5 photoanodes and investigated their photoelectrochemical properties for use in a photoelectrochemical water splitting cell. We also performed a computational investigation with two goals. The first goal was to investigate small electron polaron formation in Fe 2 TiO 5 . Charge transport in most oxide photoelectrodes involves small polaron hopping, but small polaron formation in Fe 2 TiO 5 has not been examined prior to this work. The second goal was to investigate the effect of substitutional Sn doping at the Fe site on the electronic band structure and the carrier concentration of Fe 2 TiO 5 . Here, the combined experimental and theoretical results presented in this study greatly improve our understanding of Fe 2 TiO 5 for use as a photoanode.

14 SOLAR ENERGY↗
DOE OSTI2021

Bragg Coherent Diffraction Imaging for In Situ Studies in Electrocatalysis

Electrocatalysis is at the heart of a broad range of physicochemical applications that play an important role in the present and future of a sustainable economy. Among the myriad of different electrocatalysts used in this field, nanomaterials are of ubiquitous importance. An increased surface area/volume ratio compared to bulk makes nanoscale catalysts the preferred choice to perform electrocatalytic reactions. Bragg coherent diffraction imaging (BCDI) was introduced in 2006 and since has been applied to obtain 3D images of crystalline nanomaterials. BCDI provides information about the displacement field, which is directly related to strain. Lattice strain in the catalysts impacts their electronic configuration and, consequently, their binding energy with reaction intermediates. Even though there have been significant improvements since its birth, the fact that the experiments can only be performed at synchrotron facilities and its relatively low resolution to date (~10 nm spatial resolution) have prevented the popularization of this technique. Herein, we will briefly describe the fundamentals of the technique, including the electrocatalysis relevant information that we can extract from it. Subsequently, we review some of the computational experiments that complement the BCDI data for enhanced information extraction and improved understanding of the underlying nanoscale electrocatalytic processes. We next highlight success stories of BCDI applied to different electrochemical systems and in heterogeneous catalysis to show how the technique can contribute to future studies in electrocatalysis. Finally, we outline current challenges in spatiotemporal resolution limits of BCDI and provide our perspectives on recent developments in synchrotron facilities as well as the role of machine learning and artificial intelligence in addressing them.

bragg coherent diffraction imaging↗
DOE OSTI2021

High-Field One-Dimensional and Two-Dimensional 27Al Magic-Angle Spinning Nuclear Magnetic Resonance Study of ?-, d-, and ?-Al2O3 Dominated Aluminum Oxides: Toward Understanding the Al Sites in ?-Al2O3

Herein, a detailed analysis was carried out using high-field (19.9 T) 27Al magic-angle spinning (MAS) nuclear magnetic resonance (NMR) on three specially prepared aluminum oxide samples where the ?-, d-, and ?-Al2O3 phases are dominantly expressed through careful control of the synthesis conditions. Specifically, two-dimensional (2D) multiquantum (MQ) MAS 27Al was used to obtain high spectral resolution, which provided a guide for analyzing quantitative 1D 27Al NMR spectra. Six aluminum sites were resolved in the 2D MQ MAS NMR spectra, and seven aluminum sites were required to fit the 1D spectra. A set of octahedral and tetrahedral peaks with well-defined quadrupolar line shapes was observed in the ?-phase dominant sample and was unambiguously assigned to the ?-Al2O3 phase. The distinct line shapes related to the ?-Al2O3 phase provided an opportunity for effectively deconvoluting the more complex spectrum obtained from the d-Al2O3 dominant sample, allowing the peaks/quadrupolar parameters related to the d-Al2O3 phase to be extracted. The results show that the d-Al2O3 phase contains three distinct AlO sites and three distinct AlT sites. This detailed Al site structural information offers a powerful way of analyzing the most complex ?-Al2O3 spectrum. It is found that the ?-Al2O3 phase consists of Al sites with local structures similar to those found in the d-Al2O3 and ?-Al2O3 phases albeit with less ordering. Spin–lattice relaxation time measurement further confirms the disordering of the lattice. Collectively, this study uniquely assigns 27Al features in transition aluminas, offering a simplified method to quantify complex mixtures of aluminum sites in transition alumina samples.

Xu, Suochang↗
DOE OSTI2025

A Comparative Study of Physics‐Informed and Data‐Driven Neural Networks for Compound Flood Simulation at River‐Ocean Interfaces: A Case Study of Hurricane Irene

Simulating compound flooding (CF) at the river-ocean interface within large-scale Earth System Models (ESMs) presents significant challenges due to complex interactions between river discharge, storm surge, and tides. This study assesses the comparative advantages of physics-informed and data-driven machine learning (ML) approaches for enhancing local ESM performance. We systematically compare data-driven neural network models (i.e., CNNs, U-Net, Long Short-Term Memory (LSTM), Gated Recurrent Unit), and physics-informed neural network (PINN) models, including vanilla PINN and a finite-difference-based PINN (FD-PINN). Specifically, FD-PINN is introduced to enhance computational efficiency, accelerating vanilla PINNs by ∼6.5 times while improving accuracy. To enhance data-driven model training, a new data-generation approach is developed to sample historical fluvial and coastal flood events, which ensures a robust data set for extreme event prediction. The models are evaluated using a realistic one-dimensional river domain extracted from an ESM's river mesh and the Hurricane Irene event as an independent test case. Results show that FD-PINN achieves accurate predictions with significantly reduced computational costs relative to vanilla PINNs. Among data-driven models, the best overall performance is achieved by a CNN-LSTM hybrid, which balances accuracy and efficiency. While a fully connected CNN (CNN-FC) provides the best accuracy, it incurs high computational cost. Architectures lacking strong temporal modeling tend to underperform on unseen events. These findings highlight the importance of sequence-aware designs for robust generalization. This study reveals the trade-offs between physics-informed and data-driven models and proposes an adaptive hybrid framework for integrating ML into ESMs to enhance local flood simulations.

Earth Systems Modeling↗
DOE OSTI2021

Nickel(ii) complexes based on dithiolate–polyamine binary ligand systems: crystal structures, hirshfeld surface analysis, theoretical study, and catalytic activity study on photocatalytic hydrogen generation

To ascertain the influence of binary ligand systems [1,1-dicyanoethylene-2,2-dithiolate (i-mnt -2 ) and polyamine {tetraen = tris(2-aminoethyl)amine, tren = diethylene triamine and opda = o-phenylenediamine}] on the coordination modes of the Ni(II) metal center and resulting supramolecular architectures, a series of nickel(II) thiolate complexes [Ni(tetraen)(i-mnt)](DMSO) (1), [Ni 2 (tren) 2 (i-mnt) 2 ] (2), and [Ni 2 (i-mnt) 2 (opda) 2 ] n (3) have been synthesized in high yield in one step in water and structurally characterized by single crystal X-ray crystallography and spectroscopic techniques. X-ray diffraction studies disclose the diverse i-mnt -2 coordination to the Ni +2 center in the presence of active polyamine ligands, forming a slightly distorted octahedral geometry (NiN 4 S 2 ) in 1, square planar (NiS 4 ) and distorted octahedral geometries (NiN 6 ) in the bimetallic co-crystallized aggregate of cationic [Ni(tren) 2 ] +2 and anionic [Ni(i-mnt) 2 ] -2 in 2, and a one dimensional (1D) polymeric chain along the [100] axis in 3, having consecutive square planar (NiS 4 ) and octahedral (NiN 6 ) coordination kernels. The N–H···O, N–H···S, N–H···N, N–H···S, N–H···N, and N–H···O type hydrogen bonds stabilize the supramolecular assemblies in 1, 2, and 3 respectively imparting interesting graph-set-motifs. The molecular Hirshfeld surface analyses (HS) and 2D fingerprint plots were utilized for decoding all types of non-covalent contacts in the crystal networks. Atomic HS analysis of the Ni +2 centers reveals significant Ni–N metal–ligand interactions compared to Ni–S interactions. We have also studied the unorthodox interactions observed in the solid state structures of 1–3 by QTAIM and NBO analyses. Moreover, all the complexes proved to be highly active water reduction co-catalysts (WRC) in a photo-catalytic hydrogen evolution process involving iridium photosensitizers, wherein 2 and 3 having a square planar arrangement around the nickel center(s) – were found to be the most active ones, achieving 1000 and 1119 turnover numbers (TON), respectively.

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

Rethinking the magnetic properties of lepidocrocite: A density functional theory and cluster expansion study

The iron oxyhydroxide lepidocrocite (g-FeOOH) is an abundant mineral critical to a number of chemical and technological applications. Of particular interest is the ground state and nite temperature magnetic order, and the subsequent impact this has upon crystal properties. The magnetic properties, investigated in this work are governed primarily through superexchange interactions, and have been calculated using density functional theory and cluster expansion methods. Quantication of these exchange terms has facilitated the determination of the ground state magneto-crystalline structure and subsequent calculation of its lattice constants, elastic moduli, cohesive enthalpy, and electronic density of states. Further, using a collinear magnetic conguration model, the magnetic heat capacity versus temperature has been studied and the Neel temperature obtained.

Pope, Daniel J.↗
DOE OSTI2020

Experimental study of high-k turbulence during an energy confinement degradation phase in EAST ohmic plasmas

In this paper, we present experimental studies of both high-k(r) ( cm(-1), and , respectively; ) and high- ( cm(-1), and , respectively; ) turbulence behavior during an ohmic energy confinement degradation phase in experimental advanced superconducting tokamak (EAST). High-k(r) turbulence from density fluctuation at and high- turbulence from density fluctuation at -0.97 were measured by tangential and poloidal CO2 laser collective scattering diagnostics, respectively. Note that k(r), , and are radial wavenumber, poloidal wavenumber, perpendicular wavenumber and ion gyroradius at electron temperature, respectively. Both high-k(r)/ turbulence power and energy confinement time are found to be temporally correlated to line-averaged electron density n(e) in the plasma current flat-top phase (I-p = 0.4 MA): when the n(e) shows continuous increase/decrease, the high-k(r)/ turbulence power increases/decreases correspondingly and the shows corresponding decrease/increase; the stable n(e) is related to stable both and high-k(r)/ turbulence power. Statistical results of high-k(r)/ turbulence power versus further imply that high-k(r)/ turbulence shows a strong correlation with plasma energy confinement degradation in high line-averaged n(e) condition, but high-k(r) and high- turbulence have relatively weak and no dependence on the transition between linear range in low-n(e) condition and energy confinement degradation in the high-n(e) condition, respectively. Moreover, profiles of electron temperature T-e and n(e) as well as their normalized gradients in a part of high- density fluctuation measurement region also have been given to qualitatively explain the enhancement of turbulence power with the increase of line-averaged n(e).

EAST tokamak↗
DOE OSTI2023

The use of synchrotron X-ray fluorescent imaging to study distribution and content of elements in chemically fixed single cells: a case study using mouse pancreatic beta-cells

Synchrotron X-ray fluorescence microscopy (SXRF) presents a valuable opportunity to study the metallome of single cells because it simultaneously provides high-resolution subcellular distribution and quantitative cellular content of multiple elements. Different sample preparation techniques have been used to preserve cells for observations with SXRF, with a goal to maintain fidelity of the cellular metallome. In this case study, mouse pancreatic beta-cells have been preserved with optimized chemical fixation. We show that cell-to-cell variability is normal in the metallome of beta-cells due to heterogeneity and should be considered when interpreting SXRF data. Additionally, we determined the impact of several immunofluorescence (IF) protocols on metal distribution and quantification in chemically fixed beta-cells and found that the metallome of beta-cells was not well preserved for quantitative analysis. However, zinc and iron qualitative analysis could be performed after IF with certain limitations. To help minimize metal loss using samples that require IF, we describe a novel IF protocol that can be used with chemically fixed cells after the completion of SXRF.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Separability of the Dirac equation in the STU black hole spacetime: Pairwise-equal charge case study

We study the separability of the Dirac equation in the background of four dimensional charged rotating asymptotically flat black hole solution of supergravity and the low energy string theory known as STU black hole. In particular, we analyze in detail the separability conditions in the pairwise equal charge STU black hole space-time. While in the latter case the minimally coupled Dirac equation is not separable, the introduction of a specific torsion term ensures the separability. The source of the torsion is the Kalb-Ramond field, which is an integral part of string theory, but further aspects of its properties and coupling to fermionic fields remain to be studied. To derive the torsion, two different approaches are used in conformally related frames, showing that the torsion is not unique. The correspondingly modified Dirac equations in the Einstein and string frames are shown to be separable. Furthermore, the massless radial and angular wave equations are examined; they show close similarity with corresponding equations for the standard Kerr background. A generalization of the Teukolsky equation for the pairwise equal case is conjectured. We also briefly analyze a technically sophisticated radial equation in the massive case.

Dirac equation↗
DOE OSTI2022

A Fast and Accurate Transient Stability Assessment Method Based on Deep Learning: WECC Case Study

Transient stability is one of the critical aspects of power system stability assessment. The increasing integration of inverter-based resources and the retirement of conventional synchronous generators result in the decreasing system inertia and growing complexity of system operating conditions. Using a few selected typical operating conditions cannot guarantee system transient stability in all operating conditions, and the time-domain simulation of all operating conditions requires tremendous time and is often infeasible. This paper proposes a more efficient transient stability assessment method based on deep learning. The binary search method is used to determine the critical clearing time (CCT) in creating training databased by time-domain simulation. This method is fast and accurate with 1 ms resolution. The buses whose CCTs are lower than 200 ms are considered critical buses. Buses close to each other are grouped based on their mutual admittance matrix to reduce the search space of the critical buses. This paper also proposes the generator feature normalization based on the physical model. Case study on the reduced 240-bus WECC system model demonstrates that the proposed method can predict CCT accurately and efficiently.

critical clearing time↗
DOE OSTI2020

Data-Driven Modeling and Optimization of Building Energy Consumption: a Case Study

Installing sensors and Building Automation Systems (BAS) allows controlling the facility operations while generating data that can be analyzed for model development. This work focuses on data-driven modeling of the building to optimize energy consumption. The City of Orlando aims to reduce its energy consumption so, they provided us access to their BAS for data and studying the operation of its facilities. We selected a mid-size pilot building to conduct data analysis and modeling. We develop an Application Programming Interface (API) to login to the servers and scrape data. The scraped data contains features ranging from environmental conditions to equipment activity. This dataset is a time series so, it's handled in accordance and analyzed to investigate patterns and relations between data points that help choose parameters for predictive models for building and equipment. Finally, the models are optimized to reduce the energy consumption of the facility.

Grover, Divas↗
DOE OSTI2023

Stochastic Look-Ahead Commitment: A Case Study in MISO

This paper introduces the Stochastic Look Ahead Commitment (SLAC) software prototyped and tested for the Midcontinent Independent System Operator (MISO) look ahead commitment process. SLAC can incorporate hundreds of wind, load, and net scheduled interchange (NSI) uncertainty scenarios. It uses a progressive hedging method to solve a novel two-stage stochastic unit commitment. The first stage commitment decisions, made only for those generators whose decision to commit or not in each time period cannot be deferred, can cover the uncertainties within the next three hours. The second stage includes both the dispatch for each of the scenarios and the commitment decisions that can be deferred. Study results on 15 MISO production days show that SLAC may bring economic and reliability benefits under uncertainty.

MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Comparison of Real-Time Pressure Rail Selection Algorithms for the Hybrid Hydraulic Electric Architecture: Case Study on a Track Loader

Abstract The hybrid hydraulic electric architecture (HHEA) seeks to combine the high power/torque/force density of hydraulics with the efficiency of electric machines. A set of common pressure rails is used to provide a majority of the power and this power is modulated by small electric machines to provide precise control for the operator. The HHEA has been studied in previous work using off-line dynamic programming optimization to determine energy efficient pressure rail selections, but this approach requires drive cycle information apriori. A Lagrange multiplier method has also been investigated where a set of gains (Lagrange multipliers) are optimized off-line with the idea the these gains, once determined, could be used for real-time operation. In this work, three new real-time pressure rail selection algorithms that do not require future drive cycle information are investigated; greedy, torque minimizing, and thresholding. The greedy control is found to only use 1% more energy than the globally optimal dynamic programming solution; but a model of energy loss is required.

24 POWER TRANSMISSION AND DISTRIBUTION↗