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At least 613 records · Page 34

All sources NASA NTRS DOE OSTI

DOE OSTI2020

An Open-Cell Environmental Transmission Electron Microscopy Technique for In Situ Characterization of Samples in Aqueous Liquid Solutions

The desire to image specimens in liquids has led to the development of open-cell and closed-cell techniques in transmission electron microscopy (TEM). The closed-cell approach is currently more common in TEM and has yielded new insights into a number of biological and materials processes in liquid environments. The open-cell approach, which requires an environmental TEM (ETEM), is technically challenging but may be advantageous in certain circumstances due to fewer restrictions on specimen and detector geometry. Here, we demonstrate a novel approach to open-cell liquid TEM, in which we use salt particles to facilitate the in situ formation of droplets of aqueous solution that envelope specimen particles coloaded with the salt. This is achieved by controlling sample temperature between 1 and 10°C and introducing water vapor to the ETEM chamber above the critical pressure for the formation of liquid water on the salt particles. Our use of in situ hydration enables specimens to be loaded into a microscope in a dry state using standard 3 mm TEM grids, allowing specimens to be prepared using trivial sample preparation techniques. Furthermore, our future aim will be to combine this technique with an in situ light source to study photocorrosion in aqueous environments.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

A Deep Learning-Driven Sampling Technique to Explore the Phase Space of an RNA Stem-Loop

The folding and unfolding of RNA stem-loops are critical biological processes; however, their computational studies are often hampered by the ruggedness of their folding landscape, necessitating long simulation times at the atomistic scale. Here, we adapted DeepDriveMD (DDMD), an advanced deep learning-driven sampling technique originally developed for protein folding, to address the challenges of RNA stem-loop folding. Although tempering- and order parameter-based techniques are commonly used for similar rare-event problems, the computational costs or the need for a priori knowledge about the system often present a challenge in their effective use. DDMD overcomes these challenges by adaptively learning from an ensemble of running MD simulations using generic contact maps as the raw input. DeepDriveMD enables on-the-fly learning of a low-dimensional latent representation and guides the simulation toward the undersampled regions while optimizing the resources to explore the relevant parts of the phase space. We showed that DDMD estimates the free energy landscape of the RNA stem-loop reasonably well at room temperature. Our simulation framework runs at a constant temperature without external biasing potential, hence preserving the information on transition rates, with a computational cost much lower than that of the simulations performed with external biasing potentials. Here, we also introduced a reweighting strategy for obtaining unbiased free energy surfaces and presented a qualitative analysis of the latent space. This analysis showed that the latent space captures the relevant slow degrees of freedom for the RNA folding problem of interest. Finally, throughout the manuscript, we outlined how different parameters are selected and optimized to adapt DDMD for this system. We believe this compendium of decision-making processes will help new users adapt this technique for the rare-event sampling problems of their interest.

Gupta, Ayush↗

DOE OSTI2025

Lithium–Sulfur Batteries: 3D Printed Tools and Assembly Techniques for Repeatable Lab-Scale Coin Cell Manufacturing

The coin cell form factor has become a standard in energy storage research owing to its relatively low cost, low quantity of requisite electroactive material, fast experimental turnover, and capability to generate publishable quality data. Coin cell assembly at the lab-scale requires precise manual alignment of the many internal components of a cell to yield a properly functioning device. Truly repeatable device assembly presents a significant challenge to the acquisition of reliable data. In this report, we present a three-dimensional (3D) printable design for a coin cell alignment device and describe techniques for a generalized workflow to achieve reproducible coin cell assembly of lithium-based batteries. Standardized assembly techniques using readily accessible, purpose-built tools were demonstrated for labscale production of lithium−sulfur (Li−S) coin cells. Li−S batteries fabricated using the alignment device as well as conductive adhesive incorporated workflows afforded coin cells with up to 35% reduction of assembly times, comparable or better functional cell yields, and improved electrochemical device performance with more consistent and up to 150 mAh/g higher average discharge capacities coupled with Coulombic efficiency increases of up to 2.0% over manually aligned cell assembly techniques.

Batteries↗

DOE OSTI2022

How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections?

Abstract We present an intercomparison of a suite of high‐resolution downscaled climate projections based on a six‐member General Circulation Model (GCM) ensemble from Coupled Models Intercomparison Project (CMIP6). The CMIP6 GCMs have been downscaled using dynamical and statistical downscaling techniques based on two meteorological reference observations over the conterminous United States. We use the regional climate model, RegCM4, for dynamical downscaling, double bias correction constructed analogs method for statistical downscaling, and Daymet and Livneh datasets as the reference observations for statistical training and bias‐correction. We evaluate the performances of downscaled data in both historical and future periods under the SSP585 scenario. While dynamical downscaling improves the simulation of some performance evaluation indices, it adds an extra bias in others, highlighting the need for statistical correction before its use in impact assessments. Downscaled datasets after bias‐correction compare exceptionally well with observations. However, the choice of downscaling techniques and the underlying reference observations influence the hydroclimate characteristics of downscaled data. For instance, the statistical downscaling generally preserves the GCMs climate change signal but overestimates the frequency of hot extremes. Similarly, simulated future changes are sensitive to the choice of reference observations, particularly for precipitation extremes that exhibit a higher projected increase in the ensembles trained and/or corrected by Daymet than Livneh. Overall, these results demonstrate that multiple factors, including downscaling techniques and reference observations, can substantially influence the outcome of downscaled climate projections and stress the need for a comprehensive understanding of such method‐based uncertainties.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Comparison of machine learning techniques to optimize the analysis of plutonium surrogate material via a portable LIBS device

The utilization of machine learning techniques has become commonplace in the analysis of optical emission spectra. These methods are often limited to variants of principal components analysis (PCA), partial-least squares (PLS), and artificial neural networks (ANNs). A plethora of other techniques exist and are well established in the world of data science, yet are seldom investigated for their use in spectroscopic problems. In this study, machine learning techniques were used to analyze optical emission spectra of laser-induced plasma from ceria pellets doped with silicon in order to predict silicon content. Additionally, a boosted regression ensemble model was created, and its predictive accuracy was compared to that of traditional PCA, PLS, and ANN regression models. Boosted regression tree ensembles yielded fits with R-squared (R2) values as high as 0.964 and mean-squared errors of prediction (MSEPs) as low as 0.074, providing the most accurate predictive model. Neural networks performed with slightly lower R2 values and higher MSEPs compared to the ensemble methods, thus indicating susceptibility to overfitting.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Atomic force microscopy: Emerging illuminated and operando techniques for solar fuel research

Integrated photoelectrochemical devices rely on the synergy between components to efficiently generate sustainable fuels from sunlight. The micro- and/or nanoscale characteristics of the components and their interfaces often control critical processes of the device, such as charge-carrier generation, electron and ion transport, surface potentials, and electrocatalysis. Understanding the spatial properties and structure–property relationships of these components can provide insight into designing scalable and efficient solar fuel components and systems. These processes can be probed ex situ or in situ with nanometer-scale spatial resolution using emerging scanning-probe techniques based on atomic force microscopy (AFM). In this Perspective, we summarize recent developments of AFM-based techniques relevant to solar fuel research. We review recent progress in AFM for (1) steady-state and dynamic light-induced surface photovoltage measurements; (2) nanoelectrical conductive measurements to resolve charge-carrier heterogeneity and junction energetics; (3) operando investigations of morphological changes, as well as surface electrochemical potentials, currents, and photovoltages in liquids. Opportunities for research covered here include: (1) control of ambient conditions for performing AFM measurements; (2) in situ visualization of corrosion and morphological evolution of electrodes; (3) operando AFM techniques to allow nanoscale mapping of local catalytic activities and photo-induced currents and potentials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Techniques for studying materials under extreme states of high energy density compression

The properties of materials under extreme conditions of pressure and density are of key interest to a number of fields, including planetary geophysics, materials science, and inertial confinement fusion. In geophysics, the equations of state of planetary materials, such as hydrogen and iron, under ultrahigh pressure and density provide a better understanding of their formation and interior structure [Celliers et al., “Insulator-metal transition in dense fluid deuterium,” Science 361, 677–682 (2018) and Smith et al., “Equation of state of iron under core conditions of large rocky exoplanets,” Nat. Astron. 2, 591–682 (2018)]. The processes of interest in these fields occur under conditions of high pressure (100 GPa–100 TPa), high temperature (>3000 K), and sometimes at high strain rates (>103 s−1) depending on the process. With the advent of high energy density (HED) facilities, such as the National Ignition Facility (NIF), Linear Coherent Light Source, Omega Laser Facility, and Z, these conditions are reachable and numerous experimental platforms have been developed. To measure compression under ultrahigh pressure, stepped targets are ramp-compressed and the sound velocity, measured by the velocity interferometer system for any reflector diagnostic technique, from which the stress-density of relevant materials is deduced at pulsed power [M. D. Knudson and M. P. Desjarlais, “High-precision shock wave measurements of deuterium: Evaluation of exchange-correlation functionals at the molecular-to-atomic transition,” Phys. Rev. Lett. 118, 035501 (2017)] and laser [Smith et al., “Equation of state of iron under core conditions of large rocky exoplanets,” Nat. Astron. 2, 591–682 (2018)] facilities. To measure strength under high pressure and strain rates, experimenters measure the growth of Rayleigh–Taylor instabilities using face-on radiography [Park et al., “Grain-size-independent plastic flow at ultrahigh pressures and strain rates,” Phys. Rev. Lett. 114, 065502 (2015)]. The crystal structure of materials under high compression is measured by dynamic x-ray diffraction [Rygg et al., “X-ray diffraction at the national ignition facility,” Rev. Sci. Instrum. 91, 043902 (2020) and McBride et al., “Phase transition lowering in dynamically compressed silicon,” Nat. Phys. 15, 89–94 (2019)]. Medium range material temperatures (a few thousand degrees) can be measured by extended x-ray absorption fine structure techniques, Yaakobi et al., “Extended x-ray absorption fine structure measurements of laser-shocked V and Ti and crystal phase transformation in Ti,” Phys. Rev. Lett. 92, 095504 (2004) and Ping et al., “Solid iron compressed up to 560 GPa,” Phys. Rev. Lett. 111, 065501 (2013), whereas more extreme temperatures are measured using x-ray Thomson scattering or pyrometry. This manuscript will review the scientific motivations, experimental techniques, and the regimes that can be probed for the study of materials under extreme HED conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

A super-resolution technique to analyze single-crystal inelastic neutron scattering measurements using direct-geometry chopper spectrometers

Direct-geometry time-of-flight chopper neutron spectroscopy is instrumental in studying dynamics in liquid, powder, and single crystal systems. We report here that real-space techniques in optical imagery can be adapted to obtain reciprocal-space super resolution dispersion for phonon or magnetic excitations from single-crystal neutron spectroscopy measurements. The procedure to reconstruct super-resolution energy dispersion of excitations relies on an accurate determination of the momentum and energy-dependent point spread function and a dispersion correction technique inspired by an image disparity calculation technique commonly used in stereo imaging. Here by, applying these methods to spinwave dispersion data from a virtual neutron experiment demonstrates ~5-fold improvement over nominal energy resolution.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

An updated technique to obtain explosive kinetics data on microsecond timescales

There are few techniques available for chemists to obtain time-to-explosion data with known temperature inputs at the early stages of the design and synthesis of new explosives. In the 1960s, a technique was developed to rapidly heat milligram-quantities of confined explosives to ~1000 K on microsecond timescales. Wenograd loaded explosives inside stainless steel hypodermic needles, connected them to a fireset and rapidly discharged a capacitor through the steel. He obtained the temperature by measuring the needle resistance in a Wheatstone bridge arrangement and the time to explosion from a needle rupture. However, owing to the narrow-gauge needles used in the original research, the experiment was only possible with melt-castable explosives; it was never replicated, and modern diagnostics are now available with advances beyond the 1960s. Here, we report the development of the High Explosives Initiation Time (HEIT) test, which utilizes a 250 J pulsed power system to heat the needles. This work extends the Wenograd approach by using optical diagnostics, computational modeling, and advanced techniques to measure needle resistance and needle rupture. Preliminary rate information for pentaerythritol tetranitrate (PETN) will be presented.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2026

Optimization techniques in self-similar compressible flow

We investigate the one-dimensional (1D) inviscid compressible flow equations for an ideal gas through the lens of optimization techniques. It is the case that, to our knowledge, optimization analysis applied to the so-called “linear velocity” solutions of the Euler compressible flow equations has not been previously conducted. Through both gradient-based and variational techniques, new variants of well-studied flow scenarios, i.e., self-similar, 1D, linear velocity solution class to idealized inviscid compressible flow equations, are determined, as encoded in both the kinematic and thermodynamic properties of this self-similar solution class. With the kinematics of the said solutions being driven by a self-similar “scale radius” and the thermodynamics being driven separately through the appearance of an arbitrary function, a myriad of new solution classes is possible. Acting as a guide to more realistic physical circumstances as well as discovery, it is the hope that the presented cases serve as the framework for future investigations into the intersection of self-similarity and optimization techniques. Fields of study that may find this work to be of interest include aerodynamic design, flow control, inertial confinement fusion, physics-informed neural networks, and other related areas of interest.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Interfacial solvation-structure regulation for stable Li metal anode by a desolvation coating technique

Rechargeable lithium (Li) metal batteries face challenges in achieving stable cycling due to the instability of the solid electrolyte interphase (SEI). The Li-ion solvation structure and its desolvation process are crucial for the formation of a stable SEI on Li metal anodes and improving Li plating/stripping kinetics. This research introduces an interfacial desolvation coating technique to actively modulate the Li-ion solvation structure at the Li metal interface and regulate the participation of the electrolyte solvent in SEI formation. Through experimental investigations conducted using a carbonate electrolyte with limited compatibility to Li metal, the optimized desolvation coating layer, composed of 12-crown-4 ether-modified silica materials, selectively displaces strongly coordinating solvents while simultaneously enriching weakly coordinating fluorinated solvents at the Li metal/electrolyte interface. This selective desolvation and enrichment effect reduce solvent participation to SEI and thus facilitate the formation of a LiF-dominant SEI with greatly reduced organic species on the Li metal surface, as conclusively verified through various characterization techniques including XPS, quantitative NMR, operando NMR, cryo-TEM, EELS, and EDS. The interfacial desolvation coating technique enables excellent rate cycling stability (i.e., 1C) of the Li metal anode and prolonged cycling life of the Li||LiCoO 2 pouch cell in the conventional carbonate electrolyte (E/C 2.6 g/Ah), with 80% capacity retention after 333 cycles.

42 ENGINEERING↗

DOE OSTI2021

Noise suppression for MHD characterization with electron cyclotron emission imaging 1D technique

Significant noise suppression for magnetohydrodynamics (MHD) mode characterization in the spatial and spectral domain is achieved by processing two-dimensional (2D) electron cyclotron emission imaging (ECEI) data with a one-dimensional (1D) ECEI technique using a short time window ($1\;{\text{ms}}$). The technique is applied to detect toroidal Alfven eigenmodes (TAEs) in the temporal spectrum and fit their radial envelope using the data from the DIII-D tokamak W-band 2D ECEI system. Using the data length (time window) of only 1 ${\text{ms}}$, the 1D ECEI can clearly detect the TAEs (~100 ${\text{kHz}}$) on the spectrum, while similar spectrum quality requires ~10 ${\text{ms}}$ data length with the cross power spectrum between two midplane ECEI channels. The 1D ECEI technique also effectively avoids biased fitting when resolving the fine structure of the TAE's radial envelope. The radially spatial resolution of 1D ECEI is constrained by the finite ECE radiation volume of the ECEI receiver. Here, with forward radiation modeling, we find the DIII-D ECEI system can sensitively measure the even parity MHD activities, for which the mode width is >15 mm, and tearing modes (odd parity MHD activities), for which the island full width is >30 mm.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Novel fabrication technique for high-resolution spherical crystal analyzers using a microporous aluminium base

Modern inelastic X-ray spectrometers employ curved, bent and diced analyzers to capture sufficiently large solid angles of radially emitted scattered radiation emanating from the sample. Fabricating these intricate analyzers, especially when a high energy resolution of a few millielectronvolts is required, is very time-consuming, expensive and often a hit-or-miss affair. A novel fabrication technique is introduced, utilizing a concave-spherical, microporous aluminium base to hold an assembly of a thin glass substrate with a diced crystal wafer bonded to it. Under uniform vacuum forces, the glass substrate is drawn into the aluminium base, achieving the desired bending radius, while dicing of the diffracting crystal layer prevents bending strain from being imposed on the individual crystal pixels. This technique eliminates the need for permanently bonding the crystal assembly to the concave lens, offering the opportunity for correcting figure errors, avoiding long-term degradation of the permanent bond, and making both lens and crystal reusable. Process and material costs are thus substantially decreased. Two analyzers, Si(844) and Ge(337) with intrinsic resolutions of 14.6 meV and 36.5 meV, respectively, were produced in this fashion and characterized in resonant inelastic X-ray scattering (RIXS) measurements. The achieved overall energy resolutions for both analyzers were 29.4 meV for Si(844) and 56.6 meV for Ge(337). Although the RIXS technique is veru sensitive to analyzer imperfections, the analyzers were found to be equal, if not superior, in quality to their traditional, permanently bonded counterparts.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Rapid Prototyping Techniques for Organic Direct-Bonded-Copper Power Modules

Organic Direct-Bonded-Copper (ODBC) is a novel packaging technology for power modules which allows higher flexibility in layout design. In this work, a set of rapid prototyping techniques are developed for ODBC modules based on a polyimide dielectric material. These techniques enable fast and low-cost fabrication of modules with 3-Dimensional (3D) layout features. An example half-bridge (HB) silicon carbide (SiC) metal oxide semiconductor field effect transistor (MOSFET) module is designed and prototyped using the proposed techniques aiming at ultra-low power loop inductance. Finite element analysis (FEA)-circuit co-simulations results and experimental results validate an approximately 0.71nH power loop inductance for the example module design.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Standard Characterization Techniques for Inorganic Phase Change Materials

Salt hydrates are promising candidates for phase change materials (PCMs) because of their low cost and high energy storage capacity. However, practical applications of salt hydrate based PCMs are limited due to their poor thermal cycling performance caused by phase separation and supercooling. Various techniques, including differential scanning calorimetry (DSC) and transient plane source (TPS) have been used to characterize salt hydrate based PCMs but may not provide full understanding of the degradation mechanism. In this manuscript, salt hydrate and graphite composites are prepared. Temperature history (T-History) method and X-ray diffraction (XRD) are utilized for measuring melting temperature, energy storage, and latent heat degradation of the PCM composites. Highlighting the importance of each technique and how each technique plays an important role in analyzing salt hydrates performance accurately. Temperature history, and X-ray diffraction are also used in conjunction to precisely predict the cause of degradation in salt hydrate PCMs with thermal cycling, as in most literatures the cause of degradation in salt hydrate are attributed to phase segregation, supercooling, and change in stichometry water concentration without any scientific validation.

Kumar, Navin↗

DOE OSTI2021

Comparative Analysis of ML Techniques for Data-Driven Anomaly Detection, Classification and Localization in Distribution System

High penetration of Distributed Energy Resources (DERs), fundamental load behavior changes, controllable loads, and significant increase in Electrical Vehicles (EVs) lead to complex dynamic behavior of the electric distribution system. Increasing number of components also means more measurements, more data and more data anomalies. Detecting, classifying and localizing these anomalies are important for situational awareness, and at the same time, very challenging given increasing complexity of the system. Highly accurate and high-resolution analytical techniques are needed to support anomaly detection, classification and localization (AD-C-L) for monitoring, root cause analysis and decision making. This paper provides comprehensive review and analysis of the existing spatio-temporal AD-C-L techniques within the distribution system. Challenges for specific problems in AD-C- L have been also discussed in this paper. Existing AD-C- L techniques have been categorized and synthesized for specific merits and limitations of multiple Machine Learning (ML) methodologies using common developed metrics of performance. The comparative analysis is summarized and presented with the open research challenges and path forward for future research needs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

A Performance Model of In-Situ Techniques

The computational capacity of High-Performance Computing (HPC) systems increases continuously with the rapid development of central processing units (CPUs) and graphic processing units (GPUs), while the in-/output (IO) subsystem develops relatively slowly and storage capacity is also limited. Data-intensive applications, which are designed to leverage the high computational capacity of HPC resources, typically generate a considerable amount of data for post-processing visualizations and data analytics. The limited IO speed and storage space could lead to constraints in the actual performance of these applications and, therefore, scientific discovery. In-situ techniques, where data is visualized/analysed while still in memory rather than through disk, can contribute to alleviating these problems as they can reduce or even fully avoid data writing/reading through the IO subsystem to/from storage. However, the overall efficiency of insitu techniques crucially depends on the characteristics of both the in-situ tasks and the applications, and the resource distribution among them. Therefore, choosing the right in-situ approach (synchronous, asynchronous, or hybrid) and resource allocation is essential to minimize overhead and maximize the benefits of concurrent execution. In this paper, we present a performance model of in-situ techniques to find the most beneficial in-situ approach and the preferred resource configuration. We verify the high accuracy of our approach with over 6800 measurements and provide use cases with different applications.

Ju, Yi [Max Planck Computing and Data Facility, Ga↗

DOE OSTI2020

Novel Technique for Developing Linearized Convex System Models from Experimentally Derived Data

This paper presents a novel technique for generating a convex system model from an experimentally derived data set which features variance among repeated measurements. The convex system model developed as a test case characterizes the dynamic system losses of a vanadium redox flow battery as a function of the active power output and the battery state of charge. The technique hinges on a pre-cleaning via clustering procedure which precedes the formation of a planar convex hull comprised of triangular simplices. The clustering procedure efficiently reduces the experimental data set while mitigating variance among repeated measurements and removes outliers. Ultimately, the lower evelope of the planar convex hull serves as the desired convex system model. The proposed technique reduces systematic model error which is otherwise present when directly developing a planar convex hull model based on an unreduced data set.

24 POWER TRANSMISSION AND DISTRIBUTION↗