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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

DriveSense: A Noise-Resilient Framework for Driving Mode Identification

Accurate drive mode classification is essential for enhancing the reliability and predictive maintenance of heavy-duty electric trucks. This study proposes a novel fuzzy logic-based framework, DriveSense, for real-time drive mode classification, addressing key challenges such as sensor noise, transitional behaviors, and computational efficiency. The proposed approach integrates a two-stage filtering pipeline, combining adaptive outlier removal and a dynamic Kalman filter to enhance data quality. A fuzzy inference system with smoothened trapezoidal membership functions is then applied to classify driving modes into standstill, constant speed, acceleration, and deceleration while mitigating the effects of noise and edge cases. Performance evaluation using real-world and simulated drive cycles demonstrates significant improvements in classification accuracy (up to 97.8%), F1-score (up to 0.97), and robustness against noise, while reducing false positives. Comparative analysis against baseline models, demonstrates DriveSense’s superior accuracy and generalizability across diverse driving patterns. The framework’s lightweight and interpretable fuzzy inference engine operates with low computational latency, ensuring compatibility with real-time embedded systems typical of heavy-duty electric trucks. Moreover, DriveSense models transitional behaviors through overlapping fuzzy sets and adaptive borderline classification logic, enabling smooth identification of subtle shifts such as rolling stops or gradual deceleration. These results highlight DriveSense’s potential to enhance predictive maintenance strategies, reduce downtime, and support scalable, fleet-wide diagnostics.

Kumar, Praveen [Oak Ridge National Laboratory (ORN↗
DOE OSTI2018

Plasma Surface Interactions: Bridging from the Surface to the Micron Frontier through Leadership Class Computing (Final Technical Progress Report)

The performance demands on plasma-facing components (PFCs) in future fusion power plants are beyond the capability of current materials. In defining the plasma-surface interaction (PSI), it is evident that three coupled spatial regions influence PFC materials evolution and performance. These regions consist of (1) the edge and scrape-off layer region of the plasma, (2) the near-surface material response to extreme thermal and particle fluxes under the influence of, and feedback to, the plasma sheath, and (3) the structural material’s response to an intense, 14 MeV peaked neutron spectrum, which produces very high concentrations of transmuted elements through (n,p) and (n,α) reactions and structural material property degradation. The objective of this project is to develop and deploy validated, high-performance simulation tools capable of predicting the performance of tungsten-based plasma-facing components (PFCs) in a burning fusion plasma environment, which includes modeling surface morphology evolution in either erosion or re-deposition regimes, and the recycling of hydrogen species. This requires the development of a leadership-scale computational code to predict PFC behavior that is well integrated to a suite of multiscale modeling techniques to bridge the scales needed to address complex physical and computational issues at the plasma surface interface and the transition region below the surface where neutron damage processes in the bulk dominate material behavior. Successful completion of this project will provide PFC simulation tools to evaluate steady-state performance of tungsten-based PFC and divertor components in burning plasma environments. This will enable the identification of critical experiments to confirm whether practical PFC solutions exist for magnetic fusion energy beyond ITER. More specifically, the research activities within this project focus on two broadly defined research thrusts: 1) Developing a new simulation code, Xolotl-PSI to predict PFC operating lifetime and performance, that is specifically designed to take advantage of leadership class computing facilities; and 2) Integrating and applying discrete particle-based, as well as continuum-based, multiscale modeling techniques to provide scientific discovery of the mechanisms controlling PFC and bulk materials evolution under fusion plasma and 14-MeV neutron exposure; in this thrust, the challenge of scale-bridging between atomistic/microstructural modeling and the continuum-scale of PFC device performance is achieved through multiscale modeling techniques. Throughout the grant period, research at UMass Amherst focused on the atomic-scale analysis of the dynamics and kinetics (transport and reactions) of small mobile helium clusters near surfaces and grain boundaries in plasma-exposed tungsten, as well as the proper parameterization and incorporation of these processes into our SciDAC team’s continuum-level cluster dynamics code (Xolotl). Our work also included characterization of large-scale molecular-dynamics (MD) simulation results of helium implantation into tungsten based on the findings of our analysis and comparisons of Xolotl simulation predictions with the large-scale MD simulation results for Xolotl validation purposes; emphasis was placed on the impact of helium cluster dynamics/kinetics on tungsten surface morphology, near-surface tungsten structure, and helium retention. Furthermore, our work included initiation of a research program on evaluating the impact of structural defects in tungsten due to plasma exposure (such as helium nanobubbles) on tungsten’s thermophysical properties, focusing on lattice thermal conductivity. Our studies contributed to a fundamental understanding of the effects of plasma-surface interactions on the dynamical response of plasma-facing materials in nuclear fusion devices and the development of predictive hierarchical multiscale computational tools for the quantitative description of the material’s dynamical response to plasma exposure.

36 MATERIALS SCIENCE↗
DOE OSTI2025

xGFabric: Coupling Sensor Networks and HPC Facilities with Private 5G Wireless Networks for Real-Time Digital Agriculture

Advanced scientific applications require coupling distributed sensor networks with centralized high-performance computing facilities. Citrus Under Protective Screening (CUPS) exemplifies this need in digital agriculture, where citrus research facilities are instrumented with numerous sensors monitoring environmental conditions and detecting protective screening damage. CUPS demands access to computational fluid dynamics codes for modeling environmental conditions and guiding real-time interventions like water application or robotic repairs. These computing domains have contrasting properties: sensor networks provide low-performance, limited-capacity, unreliable data access, while high-performance facilities offer enormous computing power through high-latency batch processing. Private 5G networks present novel capabilities addressing this challenge by providing low latency, high throughput, and reliability necessary for near-real-time coupling of edge sensor networks with HPC simulations. This work presents xGFabric, an end-to-end system coupling sensor networks with HPC facilities through Private 5G networks. The prototype connects remote sensors via 5G network slicing to HPC systems, enabling real-time digital agriculture simulation.

Digital Agriculture↗
DOE OSTI2017

Thermally Effective and Efficient Cooling Technologies for Advanced Gas Turbine Systems

This report presents the research which has been undertaken under DOE grant DE-FE-0011875 as part of NETL’s UTSR (University Turbine System Research) Program. The experimental portion of this work was undertaken at the University of North Dakota (UND) under the direction of Dr. Forrest Ames. The computational work was undertaken at LSU (Louisiana State University), UM (University of Memphis), and at IIT (Illinois Institute of Technology) under the direction of Dr. Sumanta Acharya. The research program was organized into a four phase program which included: 1) development of a FDA/FEA (finite difference analysis/finite element analysis) cooling model, 2) experimental/computational investigation of appropriate internal cooling methods, 3) experimental/computational investigation of external heat transfer and film cooling, 4) development of an enhanced FDA/FEA model along with the development of a warm cascade test for comparison purposes. The experimental work at UND included research on both internal cooling methods and external heat transfer. The internal cooling work included the design and testing of multiple variable hole-size “incremental impingement” configurations along with testing of two converging rounded diamond pedestal arrays for the trailing edge region. The external heat transfer work included the building of a large scale cascade facility to test a vane design with a large leading edge and an aft loaded suction surface. UND has acquired both midspan and full suction surface heat transfer measurements as well as prepared for film cooling measurements under the grant. LSU/UM/IIT has developed and conducted wall resolved LES computations for the incremental impingement geometries and provided rational understanding of the effect of hole sizes and their distributions. Additionally they have provided design correlations for the incremental impingement heat transfer coefficients. They have also developed LES methods to predict flow through film cooling plenums, which have pedestals along with the development of slot film cooling downstream from the plenums. Additionally, they have developed wall resolved LES to predict vane surface heat transfer including bypass transition without the use of a transition model. Through these simulations they provide understanding of the aerodynamic and surface heat transfer behavior under a range of influences such as freestream turbulence. The present study has produced some very useful research outcomes.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Erosion of focus rings in capacitively coupled plasma etching reactors

In plasma etching reactors, the structure surrounding the wafer, often called a focus ring (FR), plays an important role in maintaining uniform fluxes of reactants across the wafer. The FR is typically made of dielectric materials. During the plasma etching process, the sheath that forms over the wafer to accelerate ions anisotropically into the surface extends over the FR. Electrical charging of the FR modifies the sheath relative to that over the wafer. On the one hand, one wants the sheath to be uniform across the wafer-FR boundary to enable uniform fluxes to the edge of the wafer. On the other hand, maintaining a high voltage sheath over the FR will erode the FR, which is undesirable as the FR is a consumable component that must be periodically replaced in high volume manufacturing. In this work, we computationally investigated the consequences of dielectric constant ε of the FR materials on erosion of the FR. The series capacitance of the FR and its underlying structure is typically smaller than that of the wafer and its underlying structure. As a result, the FR charges quickly relative to the wafer, which then reduces the voltage across the sheath on top of the FR. The ion energy and angular distributions (IEADs) striking the FR are, therefore, generally lower in energy with a broader angular distribution. With ε = 2, the ion energies striking the middle to the outer edge of the FR are 30–180 eV, whereas for ε = 100, the ion energies are 120–380 eV. At the transition between the wafer and the FR, there is a skew in the IEAD as large as 15° that results from the difference in sheath thickness above the wafer and the FR. This skew and the erosion rate across the FR are functions of the dielectric constant of the FR material. With low ε, the FR charges quickly, less plasma is produced above the FR, and there is less voltage across the sheath that results in less FR erosion. Increasing ε of the FR produces a higher sheath voltage as well as higher ion fluxes over the FR, which increases erosion, while the skew at the edge of the wafer is less severe. Further, the material of the subsurface portion of the FR, which dominates its capacitance, is an important consideration in the design of the substrate assembly.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Bragg edge imaging (BEI) of B12W and M8N socket sections of the Arecibo telescope

From neutron user principal investigator: We kindly request the public release of three neutron imaging datasets through ONCat. All datasets were collected from two forensic specimens, B12W and M8N, sectioned from zinc-filled steel-wire sockets recovered from the collapsed Arecibo Telescope. The dataset titled “Neutron radiographs of B12W and M8N socket sections of the Arecibo telescope” contains normalized two-dimensional (2D) neutron radiographs of the specimens, showing the geometry and spatial distribution of the steel wires embedded within the zinc matrix, as well as internal features such as voids and cracks. The dataset titled “Neutron computed tomography of B12W and M8N socket sections of the Arecibo telescope” contains normalized 2D neutron projection images acquired over a range of specimen rotation angles for one selected region of each specimen. These projection images were used to reconstruct three-dimensional (3D) tomographic volumes that reveal the embedded-wire geometry and internal defects. The dataset titled “Bragg edge imaging (BEI) of B12W and M8N socket sections of the Arecibo telescope” contains six time-of-flight (TOF) neutron imaging datasets, three from each specimen, acquired at regions of interest selected based on the radiographs. The spatially resolved 2D TOF images show the zinc matrix and embedded steel wires, and the wavelength-dependent neutron transmission data were used to characterize crystallographic texture within the zinc. All components and their condition are in the public domain as they are the property of the National Science Foundation (NSF). The neutron imaging data, part geometries, and detailed forensic information have been widely published in the Arecibo Telescope Collapse Forensic Report by Thornton Tomasetti Engineers and others (NASA report and NASEM report).

Bilheux, Hassina↗
DOE OSTI2023

Site-Selective Atomic Layer Deposition on Rutile TiO 2 : Selective Hydration as a Route to Target Point Defects

Routes to area-and especially site-selective atomic layer deposition (ALD) remain an enticing challenge in precision surface science, despite the potentially game-changing capability for many energy applications. An unparalleled level of surface reaction control is required to direct ALD to select sites on the same nominal material, for example, targeted growth on distinct phases, facets, step-edges, and/or defects. However, as a sequential surface synthesis method, ALD is uniquely suited to these challenges, including the possibility of selective deposition at defective surface atom arrangements. Here, we computationally identify conditions for site-selective ALD through hydration of surface defects, including oxygen vacancies and titanium interstitials on low-index rutile TiO 2 facets. First-principles computation is used to predict, as a function of temperature, the hydroxylation of defects that are targeted by proton-exchange-mediated ALD processes. In situ ellipsometric measurements of ALD Al 2 O 3 nucleation on TiO 2 (110) single crystals prepared with and without abundant oxygen vacancies demonstrate striking contrast, corroborating computational predictions and revealing a mechanistically clear path to site-selective ALD.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Single and dual metal atom catalysts for enhanced singlet oxygen generation and oxygen reduction reaction

We demonstrate rational design of graphene-supported single and dual metal atom catalysts (SACs and DACs) for photocatalytic applications, such as singlet oxygen ( 1 O 2 ) sensitization and H 2 O 2 production. Here we combine density functional theory (DFT) and time-dependent DFT (TD-DFT) calculations with experimental verifications. We found a synergistic effect between triplet sensitization and triplet–triplet (Dexter) energy transfer; both play a role in the photocatalytic activity through the volcano plot of 3d transition metal SACs. More specifically, FeN 4 -SAC exhibits a low ISC energy gap (ΔE ISC ) of 0.039 eV, compared with 0.108 eV for FeNiN8-DACs, both possessing a high Bader charge transfer of 0.366 e- and 0.405 e-, respectively. Guided by these computational results, we synthesized a series of SACs and a DAC and confirmed their structures with scanning transmission electron microscopy (STEM) along with the X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine structure (EXAFS). We then confirm their band structures with low-energy inverse photoemission spectroscopy (LEIPS) and UV-vis-NIR. Subsequently, we synthesized the catalysts for the photooxygenation of anthracene and two-electron oxygen reduction reaction (ORR) to measure their photocatalytic activity. We found that H 2 O 2 production through the two-electron ORR competes with the 1 O 2 generation through Dexter energy transfer. FeN 4 -SAC demonstrates a high photooxygenation conversion of 86% and a high 1 O 2 quantum yield of 1.04, obtained from electron spin resonance (ESR) spectroscopy, with low H 2 O 2 production. In contrast, NiN 4 -SAC exhibits a low 1 O 2 generation and a high H 2 O 2 production mainly because of the high Gibbs free energy of the OOH* intermediate. In conclusion, this work proposes an effective DFT-guided strategy for designing SACs and DACs for various photocatalytic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Unlocking the potential: machine learning applications in electrocatalyst design for electrochemical hydrogen energy transformation

Machine learning (ML) is rapidly emerging as a pivotal tool in the hydrogen energy industry for the creation and optimization of electrocatalysts, which enhance key electrochemical reactions like the hydrogen evolution reaction (HER), the oxygen evolution reaction (OER), the hydrogen oxidation reaction (HOR), and the oxygen reduction reaction (ORR). This comprehensive review demonstrates how cutting-edge ML techniques are being leveraged in electrocatalyst design to overcome the time-consuming limitations of traditional approaches. ML methods, using experimental data from high-throughput experiments and computational data from simulations such as density functional theory (DFT), readily identify complex correlations between electrocatalyst performance and key material descriptors. Leveraging its unparalleled speed and accuracy, ML has facilitated the discovery of novel candidates and the improvement of known products through its pattern recognition capabilities. This review aims to provide a tailored breakdown of ML applications in a format that is readily accessible to materials scientists. Hence, we comprehensively organize ML-driven research by commonly studied material types for different electrochemical reactions to illustrate how ML adeptly navigates the complex landscape of descriptors for these scenarios. We further highlight ML's critical role in the future discovery and development of electrocatalysts for hydrogen energy transformation. Potential challenges and gaps to fill within this focused domain are also discussed. As a practical guide, we hope this work will bridge the gap between communities and encourage novel paradigms in electrocatalysis research, aiming for more effective and sustainable energy solutions.

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

Ab Initio modeling of Near-Edge EELS spectra for chemisorbed molecules

Abstract Electron energy loss spectroscopy (EELS) has recently been applied to probe chemisorbed molecules on metal nanostructures, but a fundamental understanding of the correlation between these spectra and the electronic structures of the adsorbates has been limited. We report here on the insights afforded by time-dependent density functional theory to decipher the energy loss near edge structure (ELNES) of EELS spectra associated with chemisorption. These first-principles calculations simulate the ELNES-EELS spectra for chemisorbed CO on various facets of Au and Pt. Computational predictions of key signatures such as the ‘red shift’ and reductions in the peak intensity of the 2 π * and 6 σ * peaks, as compared to free CO in the gas phase, are validated in comparison to experimentally collected EELS spectra. These signatures are revealed to arise from changes in the electronic structure in terms of unoccupied density of states associated with the chemisorption process. They are consistent with a Blyholder model that incorporates donation and back-donation of electrons. They are also characteristic of the chemisorption process, such as the choice of metal, site of adsorption and the coverage and distribution of adsorbates. Our simulations thus provide guidelines for the use of ELNES-EELS to characterize the atomic structure and adsorption property of nanostructured surfaces and facilitate the development of advanced nanomaterials for catalytic applications.

Materials Science↗
DOE OSTI2018

Dynamical signature of fractionalization at a deconfined quantum critical point

Deconfined quantum critical points govern continuous quantum phase transitions at which fractionalized (deconfined) degrees of freedom emerge. Here we study dynamical signatures of the fractionalized excitations in a quantum magnet (the easy-plane J-Q model) that realize a deconfined quantum critical point with emergent O(4) symmetry. By means of large-scale quantum Monte Carlo simulations and stochastic analytic continuation of imaginary-time correlation functions, we obtain the dynamic spin-structure factors in the $S^x$ and $S^z$ channels. In both channels, we observe broad continua that originate from the deconfined excitations. We further identify several distinct spectral features of the deconfined quantum critical point, including the lower edge of the continuum and its form factor on moving through the Brillouin zone. We provide field-theoretical and lattice model calculations that explain the overall shapes of the computed spectra, which highlight the importance of interactions and gauge fluctuations to explain the spectral-weight distribution. We make further comparisons with the conventional Landau O(2) transition in a different quantum magnet, at which no signatures of fractionalization are observed. The distinctive spectral signatures of the deconfined quantum critical point suggest the feasibility of its experimental detection in neutron scattering and nuclear magnetic resonance experiments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Generating Massive Scale-free Networks: Novel Parallel Algorithms using the Preferential Attachment Model

Recently, there has been substantial interest in the study of various random networks as mathematical models of complex systems. As real-life complex systems grow larger, the ability to generate progressively large random networks becomes all the more important. This motivates the need for efficient parallel algorithms for generating such networks. Naïve parallelization of sequential algorithms for generating random networks is inefficient due to inherent dependencies among the edges and the possibility of creating duplicate (parallel) edges. In this article, we present message passing interface-based distributed memory parallel algorithms for generating random scale-free networks using the preferential-attachment model. Our algorithms are experimentally verified to scale very well to a large number of processing elements (PEs), providing near-linear speedups. The algorithms have been exercised with regard to scale and speed to generate scale-free networks with one trillion edges in 6 minutes using 1,000 PEs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Self-Sustained CPS Design for Reliable Wildfire Monitoring

Continuous monitoring of areas nearby the electric grid is critical for preventing and early detection of devastating wildfires. Existing wildfire monitoring systems are intermittent and oblivious to local ambient risk factors, resulting in poor wildfire awareness. Ambient sensor suites deployed near the gridlines can increase the monitoring granularity and detection accuracy. However, these sensors must address two challenging and competing objectives at the same time. First, they must remain powered for years without manual maintenance due to their remote locations. Second, they must provide and transmit reliable information if and when a wildfire starts. The first objective requires aggressive energy savings and ambient energy harvesting, while the second requires continuous operation of a range of sensors. To the best of our knowledge, this paper presents the first self-sustained cyber-physical system that dynamically co-optimizes the wildfire detection accuracy and active time of sensors. The proposed approach employs reinforcement learning to train a policy that controls the sensor operations as a function of the environment (i.e., current sensor readings), harvested energy, and battery level. Here, the proposed cyber-physical system is evaluated extensively using real-life temperature, wind, and solar energy harvesting datasets and an open-source wildfire simulator. In long-term (5 years) evaluations, the proposed framework achieves 89% uptime, which is 46% higher than a carefully tuned heuristic approach. At the same time, it averages a 2-minute initial response time, which is at least 2.5× faster than the same heuristic approach. Furthermore, the policy network consumes 0.6 mJ per day on the TI CC2652R microcontroller using TensorFlow Lite for Micro, which is negligible compared to the daily sensor suite energy consumption.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Topological Analysis of The SPOKE Graph

The SPOKE graph [2, 6] is a sparse decorated semantic graph representing a collection of knowledge collected in many scientific databases from the fields of healthcare, biochemistry, chemistry, biology, et cetera. This knowledge graph is stored as a relational dataset decorated with metadata on each constituent vertex and edge. Formally, the graph is G(V, E, D), where V is a set of n vertices V := {1, ..., n} and edges of the form (i, j) ϵ E for i, j ϵ V, and table D that for any item in V υ E stores unstructured data such as vertex/edge type, nature of a relationship, et cetera. D(i) = {data involving vertex i ϵ V}, and D(i, j) = {data involving edge (i, j) ϵ E}. Here, we treat the graph as undirected in the sense that a direct relationship for (i, j) causes a (possibly opposite) reverse direct relationship for (j, i). The SPOKE graph G(V, E, D) is formed by processing a collection of relational datasets from medicine, chemistry, and biology, connecting many entities. Here, we analyze an instance from 2019, Spoke-20190707, where a graph file contains 6.16M edges and associated metadata and a vertex file contains 2.15M vertices and the associated metadata. There are 12 different types of vertex entities; all edge types used are implicit (see §2). There is other metadata in D on edges and vertices, but we just use the topology and the vertex labels in this report. SPOKE is growing as more knowledge is gained and more datasets are added. SPOKE is likely to grow 10x during the next phase of this project, and we therefore would like to consider topoligical analysis techniques that are scalable to several orders of magnitude larger than the current dataset (say >1B edges).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Performance and usability enhancements for continuous subgraph matching queries on graph-structured data

A query graph, which includes vertices and edges, represents a query on graph-structured data. The query graph is decomposed into query subgraphs. A network analysis tool performs continuous subgraph matching queries to facilitate analysis of computer network traffic, social media events, or other streams of data represented as a dynamic data graph (graph-structured data). This can help identify emerging trends in the data. Some features of the network analysis tool enhance performance by effectively utilizing distributed computing resources (including processing cores and memory at different nodes of a cluster) to speed up the process of updating the dynamic data graph and detecting matches of query subgraphs. Features of a query graph building tool enhance usability by providing intuitive ways to specify query graphs and their subgraphs. Features of a results visualization tool enhance usability by providing an intuitive way to present the results of continuous subgraph matching queries.

Choudhury, Sutanay↗
DOE OSTI2023

Nanoscale Surface Structure of Nanometer-Thick Ferroelectric BaTiO 3 Films Revealed by Synchrotron X-ray Scanning Tunneling Microscopy: Implications for Catalytic Adsorption Reactions

Ferroelectric nanomaterials are of interest in catalysis, non-volatile memory, and neuromorphic computing among other applications because of their switchable structure that can alter electronic and interface properties of a single material. The investigation of the role of polarization on the surface structure and chemistry of ferroelectric nanomaterials is a longstanding challenge, as it ideally requires a combination of both nanoscale imaging and chemical spectroscopy. In this work we study a model ferroelectric BaTiO 3 thin film by synchrotron X-ray scanning tunneling microscopy (SX-STM), a unique method which integrates nanoscale surface imaging and chemically sensitive spectroscopy. We find that polarization switching from downward to upward in (001)single crystalline BaTiO 3 thin films increases the intensity of X-ray absorption across Ba M, Ti L and O K edges. Chemical mapping of nanometer sized domains further demonstrates the modulation of surface structures upon polarization switching, as well as confirming the trends observed in single point experiments across the surface. Here, we complement these measurements with ab-initio computational absorption spectroscopy to elucidate the effect of polarization switching on the core-hole excitations using the Bethe-Salpeter equation approach. Our experimental and theoretical results thus confirm a stronger binding strength for the upward polarized surface with molecular O 2 as a model reactant, offering mechanistic evidence that supports previous reports. This work advances the understanding of the surface chemistry and electronic structure of ferroelectrics, which can ultimately aid strategies to design interfaces with tailored properties.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Computational analysis of ion orbital loss in diverted positive- and negative-triangularity tokamaks

The mechanism of ion orbital loss in diverted negative-triangularity (NT) tokamaks is significantly different from that of positive-triangularity (PT) tokamaks. The orbital loss from the edge of NT tokamaks can be quite large, because the separatrix X-point is located on the outboard side, meaning that trapped ions crossing the separatrix are lost to the inboard divertor plate after a mirror reflection on the side of the strong magnetic field. NT tokamaks have difficulties in building up steep pressure gradients in the edge regions. However, the majority of trapped ions crossing the separatrix in PT tokamaks survive (through complete banana motions) without reaching the X-point region. For NT tokamaks, topological changes in the trapped ion trajectories can be achieved in the presence of the radial electric field, thus reducing the orbital loss, whereas for PT tokamaks, the orbital loss is relatively insensitive to the radial electric field.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

First-Principles Approach to Extracting Chemical Information from X-ray Absorption Near-Edge Spectra of Ga-Containing Materials

The X-ray absorption near-edge structure (XANES) can provide uniquely detailed information on the coordination environments of important Ga-containing materials with unknown structures, including catalytically active materials. In this work, the Ga K-edge XANES was simulated using first-principles-based methods for seven molecular Ga complexes as well β-Ga 2 O 3 , in order to explore the chemical origins of the experimentally observed features. The theoretical spectra were computed using FEFF, CASTEP, and StoBe, to assess the sensitivity of the results to the computational approach. While the XANES features depend on the Ga coordination environment, they are also sensitive to the electronegativity of the ligands and the symmetry at Ga. The white line position responds to changes in both the core state (due to differential screening) and the valence “p” states (arising from differences in ligand coordination).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗