Chemical Evaluation and Performance Characterization of Pentaerythritol Tetranitrate (PETN) under Melt Conditions
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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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Connections between nonlocal operators: From vector calculus identities to a fractional Helmholtz decomposition
Nonlocal vector calculus, which is based on the nonlocal forms of gradient, divergence, and Laplace operators in multiple dimensions, has shown promising applications in fields such as hydrology, mechanics, and image processing. In this work, we study the analytical underpinnings of these operators. We rigorously treat compositions of nonlocal operators, prove nonlocal vector calculus identities, and connect weighted and unweighted variational frameworks. We combine these results to obtain a weighted fractional Helmholtz decomposition which is valid for sufficiently smooth vector fields. Our approach identifies the function spaces in which the stated identities and decompositions hold, providing a rigorous foundation to the nonlocal vector calculus identities that can serve as tools for nonlocal modeling in higher dimensions.
97 MATHEMATICS AND COMPUTING↗
Using MALDI-FTICR-MS Imaging to Track Low-Molecular-Weight Aromatic Derivatives of Fungal Decayed Wood
Low-molecular-weight (LMW) aromatics are crucial in meditating fungal processes for plant biomass decomposition. Some LMW compounds are employed as electron donors for oxidative degradation in brown rot (BR), an efficient wood-degrading strategy in fungi that selectively degrades carbohydrates but leaves modified lignins. Previous understandings of LMW aromatics were primarily based on “bulk extraction”, an approach that cannot fully reflect their real-time functions during BR. Here, we applied an optimized molecular imaging method that combines matrix-assisted laser desorption ionization (MALDI) with Fourier-transform ion cyclotron resonance mass spectrometry (FTICR-MS) to directly measure the temporal profiles of BR aromatics as Rhodonia placenta decayed a wood wafer. We found that some phenolics were pre-existing in wood, while some (e.g., catechin-methyl ether and dihydroxy-dimethoxyflavan) were generated immediately after fungal activity. These pinpointed aromatics might be recruited to drive early BR oxidative mechanisms by generating Fenton reagents, Fe2+ and H2O2. As BR progressed, ligninolytic products were accumulated and then modified into various aromatic derivatives, confirming that R. placenta depolymerizes lignin. Together, this work confirms aromatic patterns that have been implicated in BR fungi, and it demonstrates the use of MALDI-FTICR-MS imaging as a new approach to monitor the temporal changes of LMW aromatics during wood degradation.
59 BASIC BIOLOGICAL SCIENCES↗
Power System Event Classification and Localization Using a Convolutional Neural Network
Detection and timely identification of power system disturbances are essential for situation awareness and reliable electricity grid operation. Because records of actual events in the system are limited, ensemble simulation-based events are needed to provide adequate data for building event-detection models through deep learning; e.g., a convolutional neural network (CNN). An ensemble numerical simulation-based training data set have been generated through dynamic simulations performed on the Polish system with various types of faults in different locations. Such data augmentation is proven to be able to provide adequate data for deep learning. The synchronous generators’ frequency signals are used and encoded into images for developing and evaluating CNN models for classification of fault types and locations. With a time-domain stacked image set as the benchmark, two different time-series encoding approaches, i.e., wavelet decomposition-based frequency-domain stacking and polar coordinate system-based Gramian Angular Field (GAF) stacking, are also adopted to evaluate and compare the CNN model performance and applicability. The various encoding approaches are suitable for different fault types and spatial zonation. With optimized settings of the developed CNN models, the classification and localization accuracies can go beyond 84 and 91%, respectively.
24 POWER TRANSMISSION AND DISTRIBUTION↗
High-resolution high-throughput spatiotemporal strain imaging reveals loss mechanisms in a surface acoustic wave device
Surface acoustic wave devices are key components for processing radio frequency signals in wireless communication because these devices offer simultaneously high performance, compact size and low cost. The optimization of the device structure requires a quantitative understanding of energy conversion and loss mechanisms. Here we use stroboscopic full-field diffraction x-ray microscopy to reveal an unanticipated acoustic loss in a prototypical one-port resonator device. A non-uniform acoustic excitation in the active area was responsible for the substantial end and side leakages observed at the design frequency. Quantitative analysis of the strain amplitude using a wave decomposition method allowed the determination of several key device parameters. This high-resolution high-throughput spatiotemporal strain imaging technique is more generally applicable to the study of dynamic strain modulation in nanoscale acoustic, electronic, optical and quantum devices. The high sensitivity allows precise measurement of the strain modulation with picometer-scale amplitude.
Zhou, Tao [ESRF - The European Synchrotron, Grenob↗
In situ monitoring of microstructure evolution during thermal processing of uranium-zirconium alloys using laser-generated ultrasound
In this study, laser-generated ultrasound was used to monitor microstructure evolution during thermal processing of as-cast, polycrystalline binary uranium-zirconium metallic fuel alloys with compositions of U-20wt.%Zr (U-20Zr), U-50wt.%Zr (U-50Zr) and U-80wt.%Zr (U-80Zr). Ultrasonic waveforms were recorded during heating and cooling the samples from room temperature to >973 K and back. A phase transition temperature for all three compositions was estimated from the temperature at which an abrupt and rapid reduction in ultrasonic velocities was observed. Microstructural features on the length scale of tens of micrometers were inferred from the observation of scattering of ultrasonic waves by elastic heterogeneities above ~823 K in U-20Zr, while a hysteresis in the ultrasonic velocities of U-80Zr upon cooling was attributed to a partial retention of the high temperature phase following thermal annealing. The U-50Zr alloy exhibited a reversible viscoelastic response above 933 K, as evidenced by the observation of high frequency attenuation of the shear component of the waveforms at high temperature. Ultrasonic measurements were supplemented by in situ transmission electron microscopy (TEM). The TEM images revealed that the δ-U-Zr matrix in the three compositions underwent a spinodal decomposition above ~823 K into nanoscale regions. The ultrasonic measurements revealed larger, micron-scale structure evolution in the U-20Zr alloy at the same temperature. This large-scale structure is associated with heterogeneous regions having different Zr content. Our findings show the potential heating rate dependence of microstructural evolution in U-Zr alloys and highlight differences in the thermomechanical response and associated length scales during thermal annealing between single- and dual-phase compositions. These results demonstrate the utility of laser ultrasonics to rapidly and efficiently scan phase boundaries and monitor micrometer-scale structure evolution in metallic fuel alloys.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Physical optics simulations for synchrotron radiation sources
Here we describe approaches to high-accuracy physical optics calculations used for the development of x-ray beamlines at synchrotron radiation sources, as well as simulation of experiments and processing of experimental data at some of these beamlines. We pay special attention to the treatment of the partial coherence of x rays, a topic of high practical importance for modern low-emittance high-brightness synchrotron radiation facilities. The approaches are based, to a large extent, on the works of Emil Wolf and co-authors, including the basic scalar diffraction theory and the coherent mode decomposition method. The presented simulation examples are related to the case of the novel Coherent Diffractive Imaging beamline that is currently under development at the National Synchrotron Light Source II at the Brookhaven National Laboratory.
36 MATERIALS SCIENCE↗
WavPool: A New Block for Deep Neural Networks
Modern deep neural networks comprise many operational layers, such as dense or convolutional layers, which are often collected into blocks. In this work, we introduce a new, wavelet-transform-based network architecture that we call the multi-resolution perceptron: by adding a pooling layer, we create a new network block, the WavPool. The first step of the multi-resolution perceptron is transforming the data into its multi-resolution decomposition form by convolving the input data with filters of fixed coefficients but increasing size. Following image processing techniques, we are able to make scale and spatial information simultaneously accessible to the network without increasing the size of the data vector. WavPool outperforms a similar multilayer perceptron while using fewer parameters, and outperforms a comparable convolutional neural network by ~ 10% on relative accuracy on CIFAR-10.
McDermott, Samuel D.↗
The role of precursor decomposition in the formation of samarium doped ceria nanoparticles via solid-state microwave synthesis
The impact on the final morphology of ceria (CeO 2 ) nanoparticles made from different precursors (commercial: cerium acetate/nitrate) and in house: cerium tri(methylsilyl)amide (Ce-TMSA)) via a microwave solid state reaction has been determined. In all instances, powder X-ray diffraction indicated that the cubic fluorite CeO 2 phase (PDF# 04–004-9150, with the space group Fm-3 m) had formed. Scanning electron microscopy (SEM) images revealed spherical nanoparticles were produced from the Ce-TMSA precursor. The commercial acetate and nitrate precursors produced particles with irregular morphology. The roles of the precursor decomposition and binding energy in the synthesis of the nanocrystals with various morphologies, as well as a possible growth mechanism, were evaluated based on experimental and computational data. The formation of spherical shaped nanoparticles was determined to be due to the preferential single-step decomposition of the Ce-TMSA as well as the low activation energy to overcome decomposition. Due to the complicated decomposition of the commercial precursors and high activation energy the resulting particles adopted an irregular morphology. Highly uniform samarium doped ceria (Sm x Ce 1-x O 2-δ ) nanospheres were also synthesized from Ce-TMSA and samarium tri(methylsilyl)amide (Sm-TMSA). The effects of reaction time and temperature, on the final morphology were observed through SEM. The rapid single-step decomposition of TMSA-based precursors as observed through thermogravimetric analysis (TGA) and confirmed through the calculation of potential energy surfaces and binding energies from density functional theory (DFT) calculations, indicated that nanoparticle formation follows LaMer’s classical nucleation theory.
36 MATERIALS SCIENCE↗
Voltage and temperature effects on low cobalt lithium-ion battery cathode degradation
Degradation of low cobalt lithium-ion cathodes was tested using a full factorial combination of upper cut-off voltage (4.0 V and 4.3 V vs. Li/Li + ) and operating temperature (25 °C and 60 °C). Half-cell batteries were analyzed with electrochemical and microstructural characterization methods. Electrochemical performance was assessed with galvanostatic cycling, cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS) supported by distribution of relaxation times (DRT) analysis. Electrode microstructure was characterized with scanning electron microscopy (SEM), X-ray diffraction (XRD), and X-ray absorption near edge structure (XANES) imaging. Higher cut-off voltage cycling shows presence of NiO x formation, a low diffusivity rock-salt phase, in both CV and XRD data. XRD patterns confirmed that the rock-salt phase was beginning to form at the low cut-off voltage at high temperature, but in much lower intensity than at the high cut-off voltage. Higher temperature accelerates degradation processes at both voltages. Degradation factors at high temperature include NiO x formation, cathode material dissolution, and electrolyte decomposition. SEM analysis suggests that supporting phases may isolate and disconnect active material particles reducing capacity retention and battery life cycle. DRT analysis and XANES imaging show that both high temperature samples revealed a NiO x phase based on an increased diffusive impedance and a visible shift in the XANES spectra. The low cut-off voltage, high temperature sample showed a split peak and shift to lower energies indicating early formation of the NiO x phase. The diffusive impedance, which hinders intercalation and deintercalation, is driven by the formation of the NiO x phase. While primarily driven by cut-off voltage, elevated temperature also contributes to this degradation mechanism.
electrochemical impedance spectroscopy↗
Development and implementation of an in situ high-resolution isotopic microscope for measuring metabolic interactions in soil mesocosms (Final Report)
Many of our planet’s ecosystems rely on the activities of soil microbial communities. These microbes have significant and wide-ranging effects: they metabolize carbon and other nutrients, interact with plants and fungi, and perform other processes important to soil health. However, our ability to directly observe the enzymatic and metabolic activities of microbes within soil is currently limited, not only by the complexity of soil microbial communities themselves, but also by the lack of experimental tools to study them and their molecular interactions in situ. These challenges hinder our understanding of the life-sustaining processes of biomass decomposition and the manner in which it contributes to the movement of freed carbon within soil ecosystems. Here we aimed to develop a novel ultrahigh-resolution isotopic microscope that combines complementary imaging modalities to gain insights into metabolic cycling in soil. This final report covers both the portion of this work that was initially completed at the University of North Carolina at Chapel Hill from 2018 to 2019 (as award DE-SC0019012) and then from 2020 to 2022 (2023 in NCE) at the University of Massachusetts Chan Medical School. This goal was to build an integrated platform consisting of fluorescence microscopy, Raman microspectroscopy, and nanospray desorption electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (nanoDESI-FTICR-MS) to directly investigate microbial activities and molecular transformations occurring in soil by exploiting the use of both fluorescent labels and stable isotope probing. Specifically, our instrument was conceived as integrating: (A) fluorescence detection to localize soil microbes, identify bacteria taking up polysaccharides, and monitor gene expression of enzymes involved in decomposition; (B) Raman microspectroscopy to determine which microbes incorporate decomposition products into their biomass, as well as which decomposers are metabolically active; and (C) nanoDESI-FTICR-MS imaging to spatially probe, in real-time, the metabolites in the surrounding area, which we expect will reveal the distributions of the products of the enzymatic breakdown of polysaccharides, as well as specialized metabolites acting as cell-cell signals between decomposers. To enable these measurements, we exploited fluorescence-based probes to map the microbes that are enzymatically active (per A) and utilized stable isotope-labeled substrates to visualize both the microbial and molecular fate of decomposed biomass (B and C, respectively). To accomplish our goal, we combined the expertise of a multidisciplinary group of scientists to pursue the construction of this microscope and to investigate scientific questions that would be facilitated by such a capability. We aimed to employ this spatially informative, high-resolution isotopic microscope to visualize the critical steps of biomass degradation and the molecular fate of other environmentally relevant substrates within soil mesocosms. Accordingly, this technology will enhance our understanding of the microbial and metabolic interactions occurring within soil communities that are relevant to carbon degradation and other soil processes. The instrument has been developed and housed at EMSL, where it will be available to the entire EMSL User Base, enabling a variety of related DOE-relevant systems to be interrogated in the future by diverse scientific research groups.
59 BASIC BIOLOGICAL SCIENCES↗
Developments in SRW Code and Sirepo Framework Supporting Simulation of Time-Dependent Coherent X-ray Scattering Experiments
Physical optics simulations for beamlines and experiments are essential for the effective use of synchrotron light source facilities such as NSLS-II at BNL. The SRW software package supports such source-to-detector simulations for coherent X-ray scattering and imaging experiments through its Python interface and Sirepo browser-based graphical user interface. This allows one to define custom sample models, assess the feasibility of an experiment, and estimate most appropriate beamline settings before using valuable beamtime. We discuss the recent use of general-purpose GPU resources and coherent mode decomposition algorithms in SRW to accelerate physical optics simulations with partially coherent X-rays. To illustrate these new capabilities, we describe simulations of typical time series of partially coherent scattering images used in X-ray Photon Correlation Spectroscopy (XPCS) experiments; aiming to characterize the nanoscale dynamics of a disordered sample, representing a solution of nanoparticles undergoing Brownian diffusion.
36 MATERIALS SCIENCE↗
A Green, Fire–Retarding Ether Solvent for Sustainable High–Voltage Li–Ion Batteries at Standard Salt Concentration
Lithium-ion batteries (LIBs) are increasingly encouraged to enhance their environmental friendliness and safety while maintaining optimal energy density and cost-effectiveness. Although various electrolytes using greener and safer glyme solvents have been reported, the low charge voltage (usually lower than 4.0 V vs Li/Li + ) restricts the energy density of LIBs. Herein, tetraglyme, a less-toxic, non-volatile, and non-flammable ether solvent, is exploited to build safer and greener LIBs. It is demonstrated that ether electrolytes, at a standard salt concentration (1 m), can be reversibly cycled to 4.5 V vs Li/Li + . Anchored with Boron-rich cathode-electrolyte interphase (CEI) and mitigated current collector corrosion, the LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811) cathode delivers competitive cyclability versus commercial carbonate electrolytes when charged to 4.5 V. Synchrotron spectroscopic and imaging analyses show that the tetraglyme electrolyte can sufficiently suppress the overcharge behavior associated with the high-voltage electrolyte decomposition, which is advantageous over previously reported glyme electrolytes. The new electrolyte also enables minimal transition metal dissolution and deposition. NMC811||hard carbon full cell delivers excellent cycling stability at C/3 with a high average Coulombic efficiency of 99.77%. In conclusion, this work reports an oxidation-resilient tetraglyme electrolyte with record-high 4.5 V stability and enlightens further applications of glyme solvents for sustainable LIBs by designing Boron-rich interphases.
36 MATERIALS SCIENCE↗
Predicting bulge to total luminosity ratio of galaxies using deep learning
ABSTRACT We present a deep learning model to predict the r-band bulge-to-total luminosity ratio (B/T) of nearby galaxies using their multiband JPEG images alone. Our Convolutional Neural Network (CNN) based regression model is trained on a large sample of galaxies with reliable decomposition into the bulge and disc components. The existing approaches to estimate the B/T ratio use galaxy light-profile modelling to find the best fit. This method is computationally expensive, prohibitively so for large samples of galaxies, and requires a significant amount of human intervention. Machine learning models have the potential to overcome these shortcomings. In our CNN model, for a test set of 20 000 galaxies, 85.7 per cent of the predicted B/T values have absolute error (AE) less than 0.1. We see further improvement to 87.5 per cent if, while testing, we only consider brighter galaxies (with r-band apparent magnitude <17) with no bright neighbours. Our model estimates the B/T ratio for the 20 000 test galaxies in less than a minute. This is a significant improvement in inference time from the conventional fitting pipelines, which manage around 2–3 estimates per minute. Thus, the proposed machine learning approach could potentially save a tremendous amount of time, effort, and computational resources while predicting B/T reliably, particularly in the era of next-generation sky surveys such as the Legacy Survey of Space and Time (LSST) and the Euclid sky survey which will produce extremely large samples of galaxies.
Grover, Harsh (ORCID:0000000321338142)↗
Random projection using random quantum circuits
The random sampling task performed by Google's Sycamore processor gave us a glimpse of the “quantum supremacy era.” This has definitely shed some light on the power of random quantum circuits in this abstract task of sampling outputs from the (pseudo)random circuits. In this paper, we explore a practical near-term use of local random quantum circuits in dimensional reduction of large low-rank data sets. We make use of the well-studied dimensionality reduction technique called the random projection method. This method has been extensively used in various applications such as image processing, logistic regression, entropy computation of low-rank matrices, etc. We prove that the matrix representations of local random quantum circuits with sufficiently shorter depths [ ∼ O ( n ) ] serve as good candidates for random projection. We demonstrate numerically that their projection abilities are not far off from the computationally expensive classical principal components analysis on MNIST and CIFAR-100 image datasets. We also benchmark the performance of quantum random projection against the commonly used classical random projection in the tasks of dimensionality reduction of image data sets and computing von Neumann entropies of large low-rank density matrices. And finally, using variational quantum singular value decomposition, we demonstrate a near-term implementation of extracting the singular vectors with dominant singular values after quantum random projecting a large low-rank matrix to lower dimensions. All such numerical experiments unequivocally demonstrate the ability of local random circuits to randomize a large Hilbert space at sufficiently shorter depths with robust retention of properties of large data sets in reduced dimensions. Published by the American Physical Society 2024
Kumaran, Keerthi (ORCID:0009000949125721)↗
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↗
Stability of Calcium Ion Battery Electrolytes: Predictions from Ab Initio Molecular Dynamics Simulations
Multivalent batteries, such as magnesium-ion, calcium-ion, and zinc-ion batteries, have attracted significant attention as next-generation electrochemical energy storage devices to complement conventional lithium-ion batteries (LIBs). Among them, calcium-ion batteries (CIBs) are the least explored due to the difficult reversible Ca deposition-dissolution. In this work, we examined the stability of four different Ca salts with weakly coordinating anions and three different solvents commonly employed in existing battery technologies to identify suitable candidates for CIBs. By employing Born-Oppenheimer molecular dynamics (BOMD) simulations on salt-Ca and solvent-Ca interfaces, we find that the tetraglyme solvent and carborane salt are promising candidates for CIBs. Due to the strong reducing nature of the calcium surface, the other salts and solvents readily decompose. Further, we explain the microscopic mechanisms of salt/solvent decomposition on the Ca surface using time-dependent projected density of states, time-dependent charge-transfer plots, and climbing-image nudged elastic band calculations. Collectively, this work presents the first mechanistic assessment of the dynamical stability of candidate salts and solvents on a Ca surface using BOMD simulations, and provides a predictive path toward designing stable electrolytes for CIBs.
Born-Oppenheimer molecular dynamics↗
Three-dimensional reconstruction of neutron, gamma-ray, and x-ray sources using a cylindrical-harmonics expansion
Inertial confinement fusion capsule implosions produce neutron, gamma-ray, and x-ray emission, which are recorded by a variety of detectors, both time integrated and time resolved, to determine the performance of the implosion. Further, two-dimensional emission images from multiple directions can now be combined to infer three-dimensional structures in the implosion, such as the distribution of thermonuclear fuel density, carbon ablator, and impurities. Because of the cost and complexity of the imaging systems, however, only a few measurements can be made, so reconstructions of the source must be made from a limited number of views. Here, a cylindrical-harmonics decomposition technique to reconstruct the three-dimensional object from two views in the same symmetry plane is presented. In the limit of zero order, this method recovers the Abel inversion method. The detailed algorithms used for this characterization and the resulting reconstructed neutron source from an experiment collected at the National Ignition Facility are presented.
47 OTHER INSTRUMENTATION↗