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DOE OSTI2023

Understanding Interfacial Electrochemical Reactions through in situ ec-STEM and IL-Cryo-STEM

A major criterion in the design of next generation materials for electrical energy storage applications is a comprehensive understanding of interfacial electrochemical reactions as well as correlating the structure and chemistry across site-specific electrode/electrolyte interfaces with electron, charge, and mass transport processes as they govern performance characteristics. Scanning transmission electron microscopy (STEM) based techniques have emerged as an indispensable materials characterization tool that provides high spatial resolution imaging and chemical analysis and has been effectively utilized to obtain an atomic to nanoscale view of the interfacial structure before and after electrochemical cycling. More recently, there have been several advances that now allows us to obtain more detailed mechanistic insight into evolving reactions through in situ ec-STEM and electrical biasing platforms such as in the understanding of the mechanisms of solid electrolyte interphase formation, lithium dendrite nucleation and growth mechanisms and ionic transport mechanisms within intercalation, conversion, and alloying electrode materials. Several major advantages of the in situ ec-STEM approach is the quantitative electrochemical measurement of charge passed during cycling with simultaneous analysis of the electrochemical processes with STEM imaging and diffraction. Here, while spectroscopic analysis of the electrochemical reactions products has been performed, there is the issue of beam sensitivity and therefore, Cryo-STEM imaging combined with electron energy loss spectroscopy (EELS) techniques have been employed to analyze the chemistry of the SEI and Li dendrites. In this talk, we discuss the potential for combining identical location (IL) STEM techniques with Cryo-EM. The advantage of using this approach is that the sample is placed on a conventional TEM grid and the exact same location of the specimen can be analyzed before and after quantitative electrochemical measurements. Moreover, since the sample is on the TEM grid, the grid itself can be prepared for further Cryo-TEM experiments by plunge freezing in liquid nitrogen then transferred to the Cryo-TEM under liquid nitrogen. Results obtain from these experiments can be used to enhance our scientific understanding of interfacial chemistry at electrode/electrolyte interfaces and may be useful in the design of new materials.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Flexible and Effective Object Tiering for Heterogeneous Memory Systems

Kammerdiener, Brandon↗

DOE OSTI2022

PeleC: An adaptive mesh refinement solver for compressible reacting flows

Reacting flow simulations for combustion applications require extensive computing capabilities. Leveraging the AMReX library, the Pele suite of combustion simulation tools targets the largest supercomputers available and future exascale machines. We introduce PeleC, the compressible solver in the Pele suite, and detail its capabilities, including complex geometry representation, chemistry integration, and discretization. We present a comparison of development efforts using both OpenACC and AMReX’s C++ performance portability framework for execution on multiple GPU architectures. We discuss relevant details that have allowed PeleC to achieve high performance and scalability. PeleC’s performance characteristics are measured through relevant simulations on multiple supercomputers. The success of PeleC’s design for exascale is exhibited through demonstration of a 160 billion cell simulation and weak scaling onto 100% of Summit, an NVIDIA-based GPU supercomputer at Oak Ridge National Laboratory. Our results provide confidence that PeleC will enable future combustion science simulations with unprecedented fidelity.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Striped Data Analysis Framework

A columnar data representation is known to be an efficient way for data storage, specifically in cases when the analysis is often done based only on a small fragment of the available data structures. A data representation like Apache Parquet is a step forward from a columnar representation, which splits data horizontally to allow for easy parallelization of data analysis. Based on the general idea of columnar data storage, working on the [LDRD Project], we have developed a striped data representation, which, we believe, is better suited to the needs of High Energy Physics data analysis. A traditional columnar approach allows for efficient data analysis of complex structures. While keeping all the benefits of columnar data representations, the striped mechanism goes further by enabling easy parallelization of computations without requiring special hardware. We will present an implementation and some performance characteristics of such a data representation mechanism using a distributed no-SQL database or a local file system, unified under the same API and data representation model. The representation is efficient and at the same time simple so that it allows for a common data model and APIs for wide range of underlying storage mechanisms such as distributed no-SQL databases and local file systems. Striped storage adopts Numpy arrays as its basic data representation format, which makes it easy and efficient to use in Python applications. The Striped Data Server is a web service, which allows to hide the server implementation details from the end user, easily exposes data to WAN users, and allows to utilize well known and developed data caching solutions to further increase data access efficiency. We are considering the Striped Data Server as the core of an enterprise scale data analysis platform for High Energy Physics and similar areas of data processing. We have been testing this architecture with a 2TB dataset from a CMS dark matter search and plan to expand it to multiple 100 TB or even PB scale. We will present the striped format, Striped Data Server architecture and performance test results.

Gutsche, Oliver↗

DOE OSTI2025

Flexible and Effective Object Tiering for Heterogeneous Memory Systems

Computing platforms that package multiple types of memory, each with their own performance characteristics, are quickly becoming mainstream. To operate efficiently, heterogeneous memory architectures require new data management solutions that are able to match the needs of each application with an appropriate type of memory. As the primary generators of memory usage, applications create a great deal of information that can be useful for guiding memory management, but the community still lacks tools to collect, organize, and leverage this information effectively. To address this gap, this work introduces a novel software framework that collects and analyzes object-level information to guide memory tiering. The framework includes tools to monitor the capacity and usage of individual data objects, routines that aggregate and convert this information into tier recommendations for the host platform, and mechanisms to enforce these recommendations according to user-selected policies. Moreover, the developed tools and techniques are fully automatic, work on standard Linux systems, and do not require modification or recompilation of existing software. Using this framework, this study evaluates and compares the impact of a variety of design choices for memory tiering, including different policies for prioritizing objects for the fast memory tier as well as the frequency and timing of migration events. In conclusion, the results, collected on a modern Intel platform with conventional DDR4 SDRAM as well as Intel Optane NVRAM, show that guiding data tiering with object-level information can enable significant performance and efficiency benefits compared with standard hardware- and software-directed data-tiering strategies for a diverse set of memory-intensive workloads.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

High–Performance NiCo 2 O 4 /Graphene Quantum Dots for Asymmetric and Symmetric Supercapacitors with Enhanced Energy Efficiency

For the sustainable growth of future generations, energy storage technologies like supercapacitors and batteries are becoming more and more common. However, reliable and high-performance materials’ design and development is the key for the widespread adoption of batteries and supercapacitors. Quantum dots with fascinating and unusual properties are expected to revolutionize future technologies. However, while the recent discovery of quantum dots honored with a Nobel prize in Chemistry, their benefits for the tenacious problem of energy are not realized yet. In this context, herein, chemical-composition tuning enabled exceptional performance of NiCo 2 O 4 (NCO)/graphene quantum dots (GQDs) is reported, which outperform the existing similar materials, in supercapacitors. A comprehensive study is performed on the synthesis, characterization, and electrochemical performance evaluation of highly functional NCO/GQDs in supercapacitors delivering enhanced energy efficiency. The high-performance, functional NCO/GQDs electrode materials are synthesized by the incorporation of GQDs into NCO. The effect of variable amount of GQDs on the energy performance characteristics of NCO/GQDs in supercapacitors is studied systematically. In-depth structural and chemical bonding analyses using X-ray diffraction (XRD) and Raman spectroscopic studies indicate that all the NCO/GQDs composites crystallize in the spinel cubic phase of NiCo 2 O 4 while graphene integration evident in all the NCO/GQDs. The scanning electron microscopy imaging analysis reveals homogeneously distributed spherical particles with a size distribution of 5–9 nm validating the formation of QDs. The high-resolution transmission electron microscopy analyses reveal that the NCOQDs are anchored on graphene sheets, which provide a high surface area of 42.27 m 2 g –1 and high mesoporosity for the composition of NCO/GQDs-10%. In addition to establishing reliable electrical connection to graphene sheets, the NCOQDs provide reliable 3D-conductive channels for rapid transport throughout the electrode as well as synergistic effects. Chemical-composition tuning, and optimization yields NCO/GQDs-10% to deliver the best specific capacitance of 3940 Fg –1 at 0.5 Ag –1 , where the electrodes retain ≈98% capacitance after 5000 cycles. The NCO/GQD-10%//AC asymmetric supercapacitor device demonstrates outstanding energy density and power density values of 118.04 Wh kg –1 and 798.76 W kg –1 , respectively. The NCO/GQDs-10%//NCO/GQDs-10% symmetric supercapacitor device delivers excellent energy and power density of 24.30 Wh kg –1 and 500 W kg –1 , respectively. These results demonstrate and conclude that NCO/GQDs are exceptional and prospective candidates for developing next-generation high-performance and sustainable energy storage devices.

25 ENERGY STORAGE↗

DOE OSTI2022

Potassium carbonate decomposition modeling within rotating detonation engines for direct power extraction applications

We report that recently, there has been a significant interest in detonation-based combustion systems, such as rotating detonation engines (RDE), due to potential performance advantages in propulsion and energy applications. This includes improvements in thermodynamic efficiency, inherently high gas velocities, and the possibility of achieving an increase in total pressure (i.e. pressure gain) through the combustion process. Due to approximating a constant volume combustion process, these devices also tend to exhibit extremely high local gas temperatures relative to a comparable constant pressure combustor. These advantages overlap well with desirable performance characteristics of direct power extraction (DPE) technologies, such as a magnetohydrodynamic (MHD) generator. Typically, in DPE systems hot combustion products are seeded with an easily ionizable material such as potassium carbonate (K 2 CO 3 ) in order to boost the electrical conductivity. However, due to the short gas residence times within an RDE, it was unclear whether forming an electrically conductive combustion plasma would be feasible for integration with a downstream MHD generator. A model is presented which describes the heating, decomposition, and ionization of solid K 2 CO 3 particles and aqueous solutions of K 2 CO 3 in water, for a given initial particle diameter. This model was combined with available computational fluid dynamics (CFD) data for an oxygen-methane RDE in a one-way coupled Eulerian-Lagrangian framework to predict particle trajectories and the corresponding heating, decomposition, and ionization histories. Electrical conductivities were computed using a previously developed model, and a method was proposed to determine an equivalent average electrical conductivity. Results show that particle sizes below ~30 µm are able to fully decompose before reaching the exit of the RDE. While the one-way coupled nature of the simulations precluded rigorous evaluation of the effects of seed material on detonability, a substantial temperature reduction is expected at the detonation wave front due to heating and decomposition. A preliminary comparison is presented between the RDE and an equivalent constant pressure adiabatic combustor, showing a potential performance advantage for the RDE.

42 ENGINEERING↗

DOE OSTI2026

Intrinsic energy and time resolution of the Jefferson Lab Hall C Neutral Particle Spectrometer

The Neutral Particle Spectrometer (NPS) is an advanced calorimeter designed to measure neutral electro- magnetic particles with high precision in energy, time, and position, under conditions of high luminosity and significant background. Integrated into the experimental setup of Hall C at Thomas Jefferson National Accelerator Facility, the NPS plays a critical role in studies of nucleon structure through exclusive and semi- inclusive reaction channels. Here, this paper presents an assessment of the detector’s performance characteristics, specifically its energy and timing resolution, derived from elastic electron–proton scattering data. We report an energy resolution between 1.2% and 1.3% in the 4.5–7.3 GeV range, and an intrinsic timing resolution better than 200 ps for energies above 500 MeV. These results serve as a reference for current and future precision measurements in hadronic physics.

Detector performance↗

DOE OSTI2022

Molybdenum Carbide Electrocatalyst In Situ Embedded in Porous Nitrogen–Rich Carbon Nanotubes Promotes Rapid Kinetics in Sodium–Metal–Sulfur Batteries

This work is the first report of a molybdenum carbide-based electrocatalyst for sulfur-based sodium metal batteries (SMBs/NMBs). MoC/Mo 2 C is in-situ grown on nitrogen-doped carbon nanotubes in parallel with formation of extensive nanoporosity. Sulfur impregnation (50 wt% S) results in unique triphasic architecture termed MoC/Mo 2 C@PCNT-S. Quasi-solid-state phase transformation to Na 2 S is promoted in carbonate electrolyte, with in-situ time-resolved Raman, XPS and optical analysis demonstrating minimal soluble polysulfides. MoC/Mo 2 C@PCNT-S cathodes delivered among the most promising rate performance characteristics in literature, achieving 987 mAh g -1 at 1 Ag -1 , 818 mAh g -1 at 3 A g -1 , and 621 mAh g -1 at 5 A g -1 . The cells deliver superior cycling stability, retaining 650 mAh g -1 after 1000 cycles at 1.5 Ag -1 , corresponding to 0.028% capacity decay per cycle. High mass loading cathodes (64 wt% S, 12.7 mg cm -2 ) also show cycling stability, with anode degradation due to deep plating/stripping driving capacity decay. Density functional theory (DFT) demonstrates that formation energy of Na 2 S x (1 ≤ x ≤ 4) on surface of MoC/Mo 2 C is significantly lowered compared to analogous redox in liquid. Strong binding of Na 2 S x (1 ≤ x ≤ 4) on MoC/Mo 2 C surfaces results from charge transfer between the sulfur and Mo sites on carbides' surface.

25 ENERGY STORAGE↗

DOE OSTI2023

Perovskite Solar Module: Promise and Challenges in Efficiency, Meta‐Stability, and Operational Lifetime

Abstract Perovskite photovoltaics (PVs) are an emerging solar energy generation technology that is nearing commercialization. Despite the unprecedented progress in increasing power conversion efficiency (PCE) for perovskite solar cells (PSCs), up‐scaling lab‐made cells to solar modules remains a challenge. In this work, the recent progress of making perovskite mini‐modules is reviewed. In particular, a database summarizing the module size, performance, hysteresis, and operational lifetimes reported in the literature is built. After analyzing the performance losses from scaling PSCs to mini‐modules based on the data collected from the literature, the current key to high‐performance perovskite mini‐modules is found to be the coating method optimization. If the perovskite layer quality is well reserved, a >24% mini‐module efficiency is projected by only considering the losses from lateral resistivity and laser scribing area. Next, performance characteristics are explored including hysteresis and meta‐stable power outputs that must be overcome to correctly characterize perovskite modules. Finally, current challenges associated with the long‐term stability of perovskite modules are examined and the importance of such durability for commercialization is discussed. It is hoped that the findings in this review provide a bridge for the development of perovskite modules that will lead to commercialization in the near future.

36 MATERIALS SCIENCE↗

DOE OSTI2022

The Importance of Surface Oxygen for Lithiation and Morphology Evolution during Calcination of High-Nickel NMC Cathodes

Nanoscale morphology has a direct impact on the performance of materials for electrochemical energy storage. Despite this importance, little is known about the evolution of primary particle morphology nor its effect on chemical pathways during synthesis. In this work, operando characterization is combined with atomic-scale and continuum simulations to clarify the relationship between morphology of cathode primary particles and their lithiation during calcination of LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC-811). This combined approach reveals a key role for surface oxygen adsorption in facilitating the lithiation reaction by promoting metal diffusion and oxidation, and simultaneously providing surface sites for lithium insertion. Furthermore, oxygen surface termination is shown to increase the activation energy for sintering, leading to smaller primary particle sizes at intermediate temperatures. Smaller particles provide both shorter diffusion lengths for lithium incorporation and increased surface site density for lithium insertion. These insights provide a foundation for more tailored syntheses of cathode materials with optimized performance characteristics.

25 ENERGY STORAGE↗

DOE OSTI2022

Oxide‐Based Solid‐State Batteries: A Perspective on Composite Cathode Architecture

Abstract The garnet‐type phase Li 7 La 3 Zr 2 O 12 (LLZO) attracts significant attention as an oxide solid electrolyte to enable safe and robust solid‐state batteries (SSBs) with potentially high energy density. However, while significant progress has been made in demonstrating compatibility with Li metal, integrating LLZO into composite cathodes remains a challenge. The current perspective focuses on the critical issues that need to be addressed to achieve the ultimate goal of an all‐solid‐state LLZO‐based battery that delivers safety, durability, and pack‐level performance characteristics that are unobtainable with state‐of‐the‐art Li‐ion batteries. This perspective complements existing reviews of solid/solid interfaces with more emphasis on understanding numerous homo‐ and heteroionic interfaces in a pure oxide‐based SSB and the various phenomena that accompany the evolution of the chemical, electrochemical, structural, morphological, and mechanical properties of those interfaces during processing and operation. Finally, the insights gained from a comprehensive literature survey of LLZO–cathode interfaces are used to guide efforts for the development of LLZO‐based SSBs.

25 ENERGY STORAGE↗

DOE OSTI2023

Multi-Functional Applications of H-Glass Embedded with Stable Plasmonic Gold Nanoislands

Metal nanoparticles (MNPs) are synthesized using various techniques on diverse substrates that significantly impact their properties. However, among the substrate materials investigated, the major challenge is the stability of MNPs due to their poor adhesion to the substrate. Herein, it is demonstrated how a newly developed H-glass can concurrently stabilize plasmonic gold nanoislands (GNIs) and offer multifunctional applications. The GNIs on the H-glass are synthesized using a simple yet, robust thermal dewetting process. The H-glass embedded with GNIs demonstrates versatility in its applications, such as i) acting as a room temperature chemiresistive gas sensor (70% response for NO 2 gas); ii) serving as substrates for surface-enhanced Raman spectroscopy for the identifications of Nile blue (dye) and picric acid (explosive) analytes down to nanomolar concentrations with enhancement factors of 4.8 × 10 6 and 6.1 × 10 5 , respectively; and iii) functioning as a nonlinear optical saturable absorber with a saturation intensity of 18.36 × 10 15 W m -2 at 600 nm, and the performance characteristics are on par with those of materials reported in the existing literature. In conclusion, this work establishes a facile strategy to develop advanced materials by depositing metal nanoislands on glass for various functional applications.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Risk-Aware Framework Development for Disruption Prediction: Alcator C-Mod and DIII-D Survival Analysis

Abstract Survival regression models can achieve longer warning times at similar receiver operating characteristic performance than previously investigated models. Survival regression models are also shown to predict the time until a disruption will occur with lower error than other predictors. Time-to-event predictions from time-series data can be obtained with a survival analysis statistical framework, and there have been many tools developed for this task which we aim to apply to disruption prediction. Using the open-source Auton-Survival package we have implemented disruption predictors with the survival regression models Cox Proportional Hazards, Deep Cox Proportional Hazards, and Deep Survival Machines. To compare with previous work, we also include predictors using a Random Forest binary classifier, and a conditional Kaplan-Meier formalism. We benchmarked the performance of these five predictors using experimental data from the Alcator C-Mod and DIII-D tokamaks by simulating alarms on each individual shot. We find that developing machine-relevant metrics to evaluate models is an important area for future work. While this study finds cases where disruptive conditions are not predicted, there are instances where the desired outcome is produced. Giving the plasma control system the expected time-to-disruption will allow it to determine the optimal actuator response in real time to minimize risk of damage to the device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Stochastic finite volume method for uncertainty quantification of transient flow in gas pipeline networks

We develop a weakly intrusive framework to simulate the propagation of uncertainty in solutions of generic hyperbolic partial differential equation systems on graph-connected domains with nodal coupling and boundary conditions. The method is based on the Stochastic Finite Volume (SFV) approach and can be applied for uncertainty quantification (UQ) of the dynamical state of fluid flow over actuated transport networks. The numerical scheme has specific advantages for modeling intertemporal uncertainty in time-varying boundary parameters, which cannot be characterized by strict upper and lower (interval) bounds. We describe the scheme for a single pipe, and then formulate the controlled junction Riemann problem (JRP) that enables the extension to general network structures. In conclusion, we demonstrate the method's capabilities and performance characteristics using a standard benchmark test network.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Theoretical modeling of a bottom-raised oscillating surge wave energy converter structural loadings and power performances

Here, this study presents theoretical formulations to evaluate the fundamental parameters and performance characteristics of a bottom-raised oscillating surge wave energy converter (OSWEC) device. Employing a flat plate assumption and potential flow formulation in elliptical coordinates, closed-form equations for the added mass, radiation damping, and excitation forces/torques in the relevant pitch-pitch and surge-pitch directions of motion are developed and used to calculate the system's response amplitude operator and the forces and moments acting on the foundation. The model is benchmarked against numerical simulations using WAMIT and WEC-Sim, showcasing excellent agreement. The sensitivity of plate thickness on the analytical hydrodynamic solutions is investigated over several thickness-to-width ratios ranging from 1:80 to 1:10. The results show that as the thickness of the benchmark OSWEC increases, the deviation of the analytical hydrodynamic coefficients from the numerical solutions grows from 3% to 25%. Differences in the excitation forces and torques, however, are contained within 12%. While the flat plate assumption is a limitation of the proposed analytical model, the error is within a reasonable margin for use in the design space exploration phase before a higher-fidelity (and thus more computationally expensive) model is employed. A parametric study demonstrates the ability of the analytical model to quickly sweep over a domain of OSWEC dimensions, illustrating the analytical model's utility in the early phases of design.

13 HYDRO ENERGY↗

DOE OSTI2021

Towards ternary binders involving limestone additions — A review

The review summarises literature to examine the transition from portland limestone cement system to composite ternary binder systems involving limestone. Interest in limestone addition as an ideal component in multicomponent binder systems has surged as evident from the large volume of literature published in the recent past. A ternary blended system, with co-substitution of limestone, has the potential to complement the reaction of the supplementary cementitious materials (SCMs). The direct addition of limestone powder helps to attain higher substitution levels of portland cement clinker, improve early age properties, and supplement SCM's reactivity. However, the dilution of hydrates could hamper the long-term benefits. In this review, the interaction of fine limestone is classified and elaborated under two broad umbrellas: physical and chemical interactions. The physical interactions can manifest in three ways, namely, filler action, shearing action and improved packing, which alters reaction rate and extent at early ages. The chemical interactions also modify the reaction kinetics and phase assemblage due to nucleation of C-S-H on calcite surfaces, preservation of the ettringite phase and formation of carboaluminates. Two different forms of carboaluminate hydrates — hemicarboaluminate and mono-carboaluminate can be present in the hydrate matrix depending on the balance between carbonate ions and aluminates in the pore solution. Several factors such as replacement level, particle size, choice of SCM, its reactivity and reactive aluminates content, sulphate levels, curing temperature, and duration of curing can control the carboaluminate formation, reaction degree of SCMs and chemical interaction from limestone additions. A combination of physical and chemical effects makes fine limestone a potential material for co-substitution with aluminosilicate based SCMs, mainly fly ash, slag, and calcined clay. In this review, the factors affecting limestone-SCM composites are summarised based on a detailed literature survey. The effects of SCM-limestone cement composites on hydration kinetics, reaction chemistry, the reactivity of SCMs, the stability of hydrated phases, and contribution to the physical structure development and macroscopic properties by evaluating hydration and mechanical properties are discussed. The importance of AFm (Al{sub 2}O{sub 3}–Fe{sub 2}O{sub 3}-mono) phases in various deterioration mechanisms in concrete and their influence on performance characteristics in different exposure environment is critically appraised.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Data-driven methods for diffusivity prediction in nuclear fuels

The growth rate of structural defects in nuclear fuels under irradiation is intrinsically related to the diffusion rates of the defects in the fuel lattice. The generation and growth of atomistic structural defects can significantly alter the performance characteristics of the fuel. This alteration of functionality must be accurately captured to qualify a nuclear fuel for use in reactors. Predicting the diffusion coefficients of defects and how they impact macroscale properties such as swelling, gas release, and creep is therefore of significant importance in both the design of new nuclear fuels and the assessment of current fuel types. In this article, we apply data-driven methods focusing on machine learning (ML) to determine various diffusion properties of two nuclear fuels—uranium oxide and uranium nitride. We show that using ML can increase, often significantly, the accuracy of predicting diffusivity in nuclear fuels in comparison to current analytical models. We also illustrate how ML can be used to quickly develop fuel models with parameter dependencies that are more complex and robust than what is currently available in the literature. In conclusion, these results suggest there is potential for ML to accelerate the design, qualification, and implementation of nuclear fuels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗