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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 991 records · Page 55
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Interpretation of Vector Electric Field Measurements of Bursty Langmuir Waves in the Cusp

An analysis of auroral Langmuir waves in the cusp observed by two high-frequency electric field instruments on the TRICE high-flyer sounding rocket shows many examples of Langmuir wave bursts modulated at approximately 10 kHz. Previous studies have explained these and similar observations as the result of beating between waves with very close frequencies near the Langmuir cutoff, resulting from either wave-wave interactions or independent linear excitations. The unique three-dimensional (3-D) data set provided by the NASA Goddard Space Flight Center TAEFWD instrument shows that up to 25% of waveforms selected from the most intense bursts exhibit anisotropic modulations, i.e., the beat nulls and peaks are not aligned in time across the three perpendicular electric field components. Modulations of this type arise when superposed wave normal modes possess differing polarizations, and simulations using wave modes calculated with the J-WHAMP numerical dispersion code show (1) that waves with differing polarizations can exist in conditions like those observed by TRICE-High and (2) that superpositions of such waves can produce anisotropic modulation. Fourier analysis of the 3-D waveform data suggests that both linear and elliptically polarized waves are present near the Langmuir cutoff at these times. These observations illustrate how 3-D measurements can give valuable insight into the nature of wave interactions in the auroral plasma environment.

Dombrowski, M. P.↗
NASA NTRS2019

A Simple Transient Thermal Test Assembly for Insulation Materials

A simple transient thermal test was developed for evaluating the thermal performance of high-temperature flexible insulation materials in atmospheric pressure air. The test setup was inspired by the test assembly used for evaluating the performance of fire shelters and fire protective clothing. The heating source used was a burner supplied with propane gas that can generate relatively uniform heating over a wide area. The ratio of the in-plane width to thickness of the insulation test sample in the test assembly was selected to be greater than ten so that the heat transfer in the center of the test assembly was nearly one-dimensional. A rigid, thin Inconel plate was used as the septum plate directly exposed to the flame to provide a relatively uniform, known-temperature boundary condition for the test assembly, and to prevent convective heat transfer through the test insulation from the burner. The test sample, flexible alumina paper insulation, was placed between the Inconel plate and a thin titanium witness plate. The overall assembly was further insulated using a combination of rigid and flexible ceramic insulations to minimize heat losses from the periphery of the test assembly. Thermocouples installed on the septum and witness plates and inside the insulation test sample provided temperature measurements at various locations. For the evaluation tests, the Inconel septum plate reached temperatures between 780°C and 850°C for tests with flame exposure durations of 120 sec to 300 sec. The measured temperatures for various tests with similar flame exposure times were repeatable. A one-dimensional numerical heat transfer model of the test assembly was developed. The close agreement between measured and predicted temperatures on the titanium witness plate and inside the insulation test sample indicated that this test may provide a one-dimensional thermal testing capability for evaluation of thermal performance of similar insulations.

Daryabeigi, Kamran↗
DOE OSTI2019

A Machine Learning Approach for the Discovery of Ligand-Specific Functional Mechanisms of GPCRs

G protein-coupled receptors (GPCRs) play a key role in many cellular signaling mechanisms, and must select among multiple coupling possibilities in a ligand-specific manner in order to carry out a myriad of functions in diverse cellular contexts. Much has been learned about the molecular mechanisms of ligand-GPCR complexes from Molecular Dynamics (MD) simulations. However, to explore ligand-specific differences in the response of a GPCR to diverse ligands, as is required to understand ligand bias and functional selectivity, necessitates creating very large amounts of data from the needed large-scale simulations. This becomes a Big Data problem for the high dimensionality analysis of the accumulated trajectories. Here we describe a new machine learning (ML) approach to the problem that is based on transforming the analysis of GPCR function-related, ligand-specific differences encoded in the MD simulation trajectories into a representation recognizable by state-of-the-art deep learning object recognition technology. We illustrate this method by applying it to recognize the pharmacological classification of ligands bound to the 5-HT 2A and D2 subtypes of class-A GPCRs from the serotonin and dopamine families. The ML-based approach is shown to perform the classification task with high accuracy, and we identify the molecular determinants of the classifications in the context of GPCR structure and function. This study builds a framework for the efficient computational analysis of MD Big Data collected for the purpose of understanding ligand-specific GPCR activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Toward memory-efficient melt pool monitoring: a classification framework using event-based imaging and sparse sensing technique

Vision sensors like CMOS and CCD cameras are often used for in-process monitoring of melt pools in laser-based additive and welding processes, but they require transferring large amounts of data and computational processing resources. Event-based neuromorphic imagery, on the other hand, detects only the change in pixel intensity, thus potentially reducing the data amount and latency. With an event imager, this study develops a framework for melt pool condition classification, including image construction, time scale selection, optimal pixel selection, and sparse classification, to achieve a highly memory-efficient scheme. These are based on sparse sensing techniques with singular value decomposition (SVD) and QR pivoting, the two fundamental matrix transformations for linear dimensionality reduction. The framework is then validated by classifying a controlled experiment by exciting various mode shapes of liquid gallium pools of varying depths (3, 6, and 8 mm). At 200 pixels, the classifier can reach overall accuracy of 75%, while at 2000 pixels (0.013% of the total possible pixels), the accuracy is nearly 90% (89.86%). At the same number of pixels, random selection can only achieve 46% and 67%, respectively. The memory savings of the sparsely sampled event data compared to a conventional imager is about 500 times. In addition to performance, implementation and limitations of the framework are also discussed.

42 ENGINEERING↗
DOE OSTI2021

Structural descriptor for enhanced spin-splitting in 2D hybrid perovskites

Abstract Two-dimensional (2D) hybrid metal halide perovskites have emerged as outstanding optoelectronic materials and are potential hosts of Rashba/Dresselhaus spin-splitting for spin-selective transport and spin-orbitronics. However, a quantitative microscopic understanding of what controls the spin-splitting magnitude is generally lacking. Through crystallographic and first-principles studies on a broad array of chiral and achiral 2D perovskites, we demonstrate that a specific bond angle disparity connected with asymmetric tilting distortions of the metal halide octahedra breaks local inversion symmetry and strongly correlates with computed spin-splitting. This distortion metric can serve as a crystallographic descriptor for rapid discovery of potential candidate materials with strong spin-splitting. Our work establishes that, rather than the global space group, local inorganic layer distortions induced via appropriate organic cations provide a key design objective to achieve strong spin-splitting in perovskites. New chiral perovskites reported here couple a sizeable spin-splitting with chiral degrees of freedom and offer a unique paradigm of potential interest for spintronics.

42 ENGINEERING↗
DOE OSTI2020

Phonon-exciton Interactions in WSe 2 under a quantizing magnetic field

Strong many-body interaction in two-dimensional transitional metal dichalcogenides provides a unique platform to study the interplay between different quasiparticles, such as prominent phonon replica emission and modified valley-selection rules. A large out-of-plane magnetic field is expected to modify the exciton-phonon interactions by quantizing excitons into discrete Landau levels, which is largely unexplored. Here, we observe the Landau levels originating from phonon-exciton complexes and directly probe exciton-phonon interaction under a quantizing magnetic field. Phonon-exciton interaction lifts the inter-Landau-level transition selection rules for dark trions, manifested by a distinctively different Landau fan pattern compared to bright trions. This allows us to experimentally extract the effective mass of both holes and electrons. The onset of Landau quantization coincides with a significant increase of the valley-Zeeman shift, suggesting strong many-body effects on the phonon-exciton interaction. Our work demonstrates monolayer WSe 2 as an intriguing playground to study phonon-exciton interactions and their interplay with charge, spin, and valley.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Selective Uptake of Ethane/Ethylene Mixtures by UTSA-280 is Driven by Reversibly Coordinated Water Defects

The metal–organic framework (MOF) UTSA-280 has been shown previously to separate ethane/ethylene mixtures. We show that this separation is kinetic; if full equilibrium is reached, the MOF is not selective. The time scales associated with this kinetic separation are sufficient to make the material potentially interesting for practical separations. The observation of kinetic separation is surprising because the MOF’s one-dimensional (1D) channels are small enough that molecular motion along the channels would be expected to take place by single-file diffusion, which is not observed. We show using a combination of experimental and modeling techniques that diffusion of molecules between adjacent 1D channels through local defects associated with coordinated water molecules allows net mixing of molecules and, moreover, that this process is potentially responsible for the selective nature of the observed kinetic separation. We show how activation and regeneration conditions play an essential role in controlling the density of these local defects.

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

Lithium-Ion Battery Diagnostics Using Electrochemical Impedance via Machine-Learning

Diagnosing battery states such as health, state-of-charge, or temperature is crucial for ensuring the safety and reliability of electrochemical energy storage systems. While some states, such as temperature, may be measured using cheap sensors, accurate diagnosis of battery health metrics usually requires time-consuming performance measurements, making them infeasible for use in real-world operation. These health metrics can be measured during lab-testing and then estimated on-line using predictive life models or via state observer algorithms such as Kalman filters, but these predictive methods should be supplemented by actual measurement of battery health whenever possible to ensure reliability. Rapid measurement of battery health may be done by various types of fast diagnostic techniques such as electrochemical impedance spectroscopy (EIS), which can be performed in only a few minutes and require only a fraction of the energy and power needed for a full charge and discharge measurement. But there is a substantial challenge for estimating battery health using EIS data, as EIS is sensitive to cell temperature, state-of-charge, current, and resting time in addition to health. Thus, utilizing EIS data to predict battery capacity requires correcting for all these additional variables, a task that is extremely difficult to handle analytically. This talk utilizes machine-learning methods to estimate the effectiveness of battery capacity prediction from EIS data, leveraging a data set of hundreds of EIS measurements recorded at varying temperature and state-of-charge throughout a 500-day aging study of 32 commercial, large-format NMC-Graphite lithium-ion batteries. Using EIS as input to machine-learning models is complicated by the nonlinear response of impedance to battery health, temperature, and state-of-charge, as well as the collinearity between the impedance response at neighboring frequencies, which can easily lead to overfit models. To train robust models, features from EIS data need to be extracted from the data or some subset of critical frequencies selected. Many approaches for extracting and selecting features from EIS data from electrochemical analysis and machine-learning fields were identified for analysis: using the entire raw spectra; selection of one, two, or many frequencies from the entire spectra; selecting interesting points from the EIS measurement using domain knowledge; fitting EIS with an equivalent-circuit model; calculating statistics on the raw impedance values; and reducing the dimensionality of the data using unsupervised linear (principal component analysis) and non-linear (uniform manifold approximation and projection) methods. These approaches were rigorously compared using a machine-learning pipeline approach, training linear, Gaussian process, and random forest regression models and quantifying performance using cross-validation as well as a held-out test set. An artificial neural network model trained on the raw spectra was also tested. Promising pipelines were fine-tuned via Bayesian hyperparameter optimization using cross-validation loss and training with class-specific weights to counter data set imbalance. The most reliable method for utilizing impedance in this work was the selection of two optimal frequencies through an exhaustive search, resulting in about 2% mean absolute error on test data for both Gaussian process and random forest model architectures. Interrogation of a variety of models reveals critical frequencies of 100 Hz and 103 Hz for this data set, though the optimal set of frequencies is not necessarily intuitive, i.e., the best performing models are not simply those that use impedance at frequencies that have the highest correlation to the relative discharge capacity. The best performing model is an ensemble model, which is able to predict battery capacity with 1.9% mean absolute error for unseen cells using impedance recorded at a variety of temperatures and states-of-charge.

battery↗
DOE OSTI2023

Methane Partial Oxidation over Multifunctional 2-D Materials

The objective of this research is to design, synthesize, and evaluate highly selective, active, and stable multifunctional catalysts for the low temperature (< 500 Kelvin (K)) partial oxidation of methane to methanol (MTM) with molecular oxygen: CH 4 (g) + $\frac{1}{2}$O 2 (g) → CH 3 OH(g). Methane, the primary component of natural gas, is a source of energy and economic growth as well as an environmental concern. Recent developments in horizontal drilling and enhanced extraction methods have resulted in production of an estimated 62.4 trillion m 3 of ‘stranded’, or uneconomic, natural gas. Uneconomical natural gas is often flared or vented at remote oil production sites. Leaked, flared, and/or vented gas represents a "lost opportunity”, and this research project aims to maximize the value of the resource. Conventional catalysts for MTM suffer from low methanol selectivity since they exhibit ~0.55 eV higher barrier for C-H bond activation of methane compared to methanol. Without breaking these scaling relations, methanol oxidation is orders of magnitude faster than methane oxidation and it is very challenging to envision a process with economically viable single-pass yield. Here, we chose to investigate single-atom catalysts embedded and stabilized in two-dimensional materials such as graphene (GR) and "supported" on Group VIII and IB transition metals such as nickel. The electronic atomic monolayer-metal support interaction (EAMSI) present in these systems could promote methanol selectivity by breaking the scaling relations of the C-H bond activation of methane and methanol. A density functional theory (DFT) based computational study focused on predicting families of GR-based catalysts that could be active and selective for MTM. The catalyst systems predicted by the computational study were synthesized and evaluated for the gas phase MTM under relevant conditions. Unfortunately, the experimental activity and selectivity was lower than computationally predicted. The origin for the discrepancy is likely related to difficulties in synthesizing single atom catalysts in a threecomponent catalyst system at high density and with high selectivity. Future work in our groups is thus focused on reducing the system complexity to a two-component catalyst system. Finally, a techno-economic analysis (TEA) was also conducted to identify critical bottlenecks that inhibit future commercialization.

03 NATURAL GAS↗
DOE OSTI2020

A 3D nm-thin biomimetic membrane for ultimate molecular separation

Multi-functional membranes with high permeance and selectivity that can mimic nature's designs have tremendous industrial and bio-medical applications. In this paper, we report a novel concept of a 3D nanometer (nm)-thin membrane that can overcome the shortcomings of conventional membrane structures. Our 3D membrane is composed of two three-dimensionally interwoven channels that are separated by a continuous nm-thin amorphous TiO 2 layer. This 3D architecture dramatically increases the surface area by 6000 times, coupled with an ultra-short diffusion distance through the 2–4 nm-thin selective layer that allows for ultrafast gas and water transport, ~900 l m –2 h –1 bar –1 . The 3D membrane also exhibits a very high ion rejection (R ~ 100% for potassium ferricyanide) due to the combined size- and charge-based exclusion mechanisms. The combination of high ion rejection and ultrafast permeation makes our 3DM superior to the state-of-the-art high-flux membranes whose performances are limited by the flux-rejection tradeoff. Furthermore, its ultimate Li+ selectivity over polysulfide or gas can potentially solve major technical challenges in energy storage applications, such as lithium–sulfur or lithium–O 2 batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Three-dimensional broadband nonlinear phased array imaging

System include an ultrasonic transducer configured to couple to a nondestructive testing (NDT) sample and configured to produce and direct an ultrasonic probe wave at a selected frequency into a subsurface region of the NDT sample, a 3D laser scanning vibrometer configured to direct a detection beam in a scan area on a surface of the NDT sample and to receive a return beam from the scan area, and to detect, based on the return beam, a 3D motion of the surface across a wideband frequency range, and a processor, and a memory configured with instructions that, when executed by the processor, cause the processor to produce sub-surface image data of the NDT sample at multiple harmonics of the selected frequency in the wideband frequency range based on the detected 3D surface motion, wherein the sub-surface image data describes a nonlinear defect response produced in the NDT sample by interaction of the ultrasonic probe wave with the subsurface region.

Remillieux, Marcel↗
DOE OSTI2025

Engineered chirality of one-dimensional nanowires

The origin and function of chirality in DNA, proteins, and other building blocks of life represent a central question in biology. Observations of spin polarization and magnetization associated with electron transport through chiral molecules, known collectively as the chiral induced spin selectivity effect, suggest that chirality improves electron transfer. Using reconfigurable nanoscale control over conductivity at the LaAlO 3 /SrTiO 3 interface, we create chiral electron potentials that explicitly lack mirror symmetry. Quantum transport measurements on these chiral nanowires reveal enhanced electron pairing persisting to high magnetic fields (up to 18 tesla) and oscillatory transmission resonances as functions of both magnetic field and chemical potential. We interpret these resonances as arising from an engineered axial spin-orbit interaction within the chiral region. The ability to create one-dimensional electron waveguides with this specificity creates opportunities to test, via analog quantum simulation, theories about chirality and spin-polarized electron transport in one-dimensional geometries.

Science & Technology - Other Topics↗
DOE OSTI2020

Intermolecular vibrational energy transfer enabled by microcavity strong light–matter coupling

Selective vibrational energy transfer between molecules in the liquid phase, a difficult process hampered by weak intermolecular forces, is achieved through polaritons formed by strong coupling between cavity photon modes and donor and acceptor molecules. Using pump-probe and two-dimensional infrared spectroscopy, we found that the excitation of the upper polariton, which is composed mostly of donors, can efficiently relax to the acceptors within ~5 picoseconds. The energy-transfer efficiency can be further enhanced by increasing the cavity lifetime, suggesting that the energy transfer is a polaritonic process. This vibrational energy-transfer pathway opens doors for applications in remote chemistry, sensing mechanisms, and vibrational polariton condensation.

Science & Technology - Other Topics↗
DOE OSTI2014

Future of Electron Scattering and Diffraction

The ability to correlate the atomic- and nanoscale-structure of condensed matter with physical properties (e.g., mechanical, electrical, catalytic, and optical) and functionality forms the core of many disciplines. Directing and controlling materials at the quantum-, atomic-, and molecular-levels creates enormous challenges and opportunities across a wide spectrum of critical technologies, including those involving the generation and use of energy. The workshop identified next generation electron scattering and diffraction instruments that are uniquely positioned to address these grand challenges. The workshop participants identified four key areas where the next generation of such instrumentation would have major impact: A – Multidimensional Visualization of Real Materials B – Atomic-scale Molecular Processes C – Photonic Control of Emergence in Quantum Materials D – Evolving Interfaces, Nucleation, and Mass Transport Real materials are comprised of complex three-dimensional arrangements of atoms and defects that directly determine their potential for energy applications. Understanding real materials requires new capabilities for three-dimensional atomic scale tomography and spectroscopy of atomic and electronic structures with unprecedented sensitivity, and with simultaneous spatial and energy resolution. Many molecules are able to selectively and efficiently convert sunlight into other forms of energy, like heat and electric current, or store it in altered chemical bonds. Understanding and controlling such process at the atomic scale require unprecedented time resolution. One of the grand challenges in condensed matter physics is to understand, and ultimately control, emergent phenomena in novel quantum materials that necessitate developing a new generation of instruments that probe the interplay among spin, charge, orbital, and lattice degrees of freedom with intrinsic time- and length-scale resolutions. Molecules and soft matter require imaging and spectroscopy with high spatial resolution without damaging their structure. The strong interaction of electrons with matter allows high-energy electron pulses to gather structural information before a sample is damaged. Electron ScatteringImaging, diffraction, and spectroscopy are the fundamental capabilities of electron-scattering instruments. The DOE BES-funded TEAM (Transmission Electron Aberration-corrected Microscope) project achieved unprecedented sub-atomic spatial resolution in imaging through aberration-corrected transmission electron microscopy. To further advance electron scattering techniques that directly enable groundbreaking science, instrumentation must advance beyond traditional two-dimensional imaging. Advances in temporal resolution, recording the full phase and energy spaces, and improved spatial resolution constitute a new frontier in electron microscopy, and will directly address the BES Grand Challenges, such as to “control the emergent properties that arise from the complex correlations of atomic and electronic constituents” and the “hidden states” “very far away from equilibrium”. Ultrafast methods, such as the pump-probe approach, enable pathways toward understanding, and ultimately controlling, the chemical dynamics of molecular systems and the evolution of complexity in mesoscale and nanoscale systems. Central to understanding how to synthesize and exploit functional materials is having the ability to apply external stimuli (such as heat, light, a reactive flux, and an electrical bias) and to observe the resulting dynamic process in situ and in operando, and under the appropriate environment (e.g., not limited to UHV conditions). To enable revolutionary advances in electron scattering and science, the participants of the workshop recommended three major new instrumental developments: A. Atomic-Resolution Multi-Dimensional Transmission Electron Microscope: This instrument would provide quantitative information over the entire real space, momentum space, and energy space for visualizing dopants, interstitials, and light elements; for imaging localized vibrational modes and the motion of charged particles and vacancies; for correlating lattice, spin, orbital, and charge; and for determining the structure and molecular chemistry of organic and soft matter. The instrument will be uniquely suited to answer fundamental questions in condensed matter physics that require understanding the physical and electronic structure at the atomic scale. Key developments include stable cryogenic capabilities that will allow access to emergent electronic phases, as well as hard/soft interfaces and radiation- sensitive materials. B. Ultrafast Electron Diffraction and Microscopy Instrument: This instrument would be capable of nano-diffraction with 10 fs temporal resolution in stroboscopic mode, and better than 100 fs temporal resolution in single shot mode. The instrument would also achieve single- shot real-space imaging with a spatial/temporal resolution of 10 nm/10 ps, representing a thousand fold improvement over current microscopes. Such a capability would be complementary to x-ray free electron lasers due to the difference in the nature of electron and x-ray scattering, enabling space-time mapping of lattice vibrations and energy transport, facilitating the understanding of molecular dynamics of chemical reactions, the photonic control of emergence in quantum materials, and the dynamics of mesoscopic materials. C. Lab-In-Gap Dynamic Microscope: This instrument would enable quantitative measurements of materials structure, composition, and bonding evolution in technologically relevant environments, including liquids, gases and plasmas, thereby assuring the understanding of structure function relationship at the atomic scale with up to nanosecond temporal resolution. This instrument would employ a versatile, modular sample stage and holder geometry to allow the multi-modal (e.g., optical, thermal, mechanical, electrical, and electrochemical) probing of materials’ functionality in situ and in operando. The electron optics encompasses a pole piece that can accommodate the new stage, differential pumping, detectors, aberration correctors, and other electron optical elements for measurement of materials dynamics. To realize the proposed instruments in a timely fashion, BES should aggressively support research and development of complementary and enabling instruments, including new electron sources, advanced electron optics, new tunable specimen pumps and sample stages, and new detectors. The proposed instruments would have transformative impact on physics, chemistry, materials science, engineering

43 PARTICLE ACCELERATORS↗
DOE OSTI2015

Parametric behaviors of CLUBB in simulations of low clouds in the C ommunity A tmosphere M odel ( CAM )

Abstract In this study, we investigate the sensitivity of simulated low clouds to 14 selected tunable parameters of Cloud Layers Unified By Binormals (CLUBB), a higher‐order closure (HOC) scheme, and four parameters of the Zhang‐McFarlane (ZM) deep convection scheme in the Community Atmosphere Model version 5 (CAM5). A Quasi‐Monte Carlo (QMC) sampling approach is adopted to effectively explore the high‐dimensional parameter space and a generalized linear model is applied to study the responses of simulated cloud fields to tunable parameters. Our results show that the variance in simulated low‐cloud properties (cloud fraction and liquid water path) can be explained by the selected tunable parameters in two different ways: macrophysics itself and its interaction with microphysics. First, the parameters related to dynamic and thermodynamic turbulent structure and double Gaussian closure are found to be the most influential parameters for simulating low clouds. The spatial distributions of the parameter contributions show clear cloud‐regime dependence. Second, because of the coupling between cloud macrophysics and cloud microphysics, the coefficient of the dissipation term in the total water variance equation is influential. This parameter affects the variance of in‐cloud cloud water, which further influences microphysical process rates, such as autoconversion, and eventually low‐cloud fraction. This study improves understanding of HOC behavior associated with parameter uncertainties and provides valuable insights for the interaction of macrophysics and microphysics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Selectively catalytic activity of metal–organic frameworks depending on the N-position within the pyridine ring of their building blocks

Iron metal–organic frameworks (MOFs) [Fe(L){sub 2}(SCN){sub 2}]{sub ∝} (L1: 4-bpdh=2,5-bis(4-pyridyl)-3,4-diaza-2,4-hexadiene for 1Fe; and L2: 3-bpdh=2,5-bis(3-pyridyl)-3,4-diaza-2,4-hexadiene for 2Fe) were assembled in a MeOH–H{sub 2}O solvent system. 1Fe exhibits a two-dimensional extended-grid network, whereas 2Fe exhibits a stair-like double-chain; the N-position within the pyridine ring of the complexes was observed to regulate the MOF structure as layers or chains. Furthermore, selectively catalytic activity was observed for the layered MOF but not the chain-structured MOF; micro/nanoparticles of the layered MOF were therefore investigated for new potential applications of micro/nano MOFs. - Graphical abstract: Iron metal–organic frameworks (MOFs) [Fe(L){sub 2}(SCN){sub 2}]{sub ∝} (L1: 4-bpdh=2,5-bis(4-pyridyl)-3,4-diaza-2,4-hexadiene for 1Fe; and L2: 3-bpdh=2,5-bis(3-pyridyl)-3,4-diaza-2,4-hexadiene for 2Fe) were assembled in a MeOH–H{sub 2}O solvent system. The N-position within the pyridine ring of the complexes was observed to regulate the MOF structure as layers or chains. Selectively catalytic activity was observed for the layered MOF but not the chain-structured MOF. - Highlights: • Synthesis and structure of metal–organic framework [Fe(L){sub 2}(SCN){sub 2}]{sub ∝}. • Selectively catalytic activity depending on the N-position within the pyridine ring. • The degradation and conversion of methyl orange.

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

DeepMerge: Classifying high-redshift merging galaxies with deep neural networks

In this work, we investigate and demonstrate the use of convolutional neural networks (CNNs) for the task of distinguishing between merging and non-merging galaxies in simulated images, and for the first time at high redshifts (i.e. $z=2$). We extract images of merging and non-merging galaxies from the Illustris-1 cosmological simulation and apply observational and experimental noise that mimics that from the Hubble Space Telescope; the data without noise form a "pristine" data set and that with noise form a "noisy" data set. The test set classification accuracy of the CNN is $79\%$ for pristine and $76\%$ for noisy. The CNN outperforms a Random Forest classifier, which was shown to be superior to conventional one- or two-dimensional statistical methods (Concentration, Asymmetry, the Gini, $M_{20}$ statistics etc.), which are commonly used when classifying merging galaxies. We also investigate the selection effects of the classifier with respect to merger state and star formation rate, finding no bias. Finally, we extract Grad-CAMs (Gradient-weighted Class Activation Mapping) from the results to further assess and interrogate the fidelity of the classification model.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

Comparison of two-dimensional integration methods for shape reconstruction from gradient data

As a requisite and key step in some gradient-based measurement techniques, the reconstruction of the shape, more generally the scalar potential, from the measured gradient data has been studied for many years. Here, in this work, three types of two-dimensional integration methods are compared under various conditions. The merits and drawbacks of each integration method are consequently revealed to provide suggestions in selection of a proper integration method for a particular application.

36 MATERIALS SCIENCE↗