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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Inferring microbial interactions with their environment from genomic and metagenomic data

Microbial communities assemble through a complex set of interactions between microbes and their environment, and the resulting metabolic impact on the host ecosystem can be profound. Microbial activity is known to impact human health, plant growth, water quality, and soil carbon storage which has lead to the development of many approaches and products meant to manipulate the microbiome. In order to understand, predict, and improve microbial community engineering, genome-scale modeling techniques have been developed to translate genomic data into inferred microbial dynamics. However, these techniques rely heavily on simulation to draw conclusions which may vary with unknown parameters or initial conditions, rather than more robust qualitative analysis. To better understand microbial community dynamics using genome-scale modeling, we provide a tool to investigate the network of interactions between microbes and environmental metabolites over time. Using our previously developed algorithm for simulating microbial communities from genome-scale metabolic models (GSMs), we infer the set of microbe-metabolite interactions within a microbial community in a particular environment. Because these interactions depend on the available environmental metabolites, we refer to the networks that we infer as metabolically contextualized , and so name our tool MetConSIN: Met abolically Con textualized S pecies I nteraction N etworks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Inaugural Symposium of the Institute for Sustainable Energy and Environment, Virginia Commonwealth University

The Inaugural Symposium of the Institute for Sustainable Energy and Environment (ISEE) was held in Virginia Commonwealth University, Richmond, Virginia during April 26 – 28, 2023. The symposium addressed issues and challenges in energy and environment and highlighted the use of clean renewable energy to mitigate the adverse effects of fossil fuels and the greenhouse gases it produces. These issues are complex as the involve educating the public that the climate change is real, it is caused by the use of fossil fuels, it requires scientists and engineers to understand the fundamental science in the production and storage of renewable energy, it requires industries to manufacture and distribute the equipment necessary for commercial application of these technologies, it requires policy makers to put together policies and guidelines to make the transition from fossil fuels to renewable energies efficient, and it requires social scientists and educators to address environmental justice issues and create a workforce to sustain the transition. The symposium considered all the above issues with a diverse list of speakers from the federal and state government, industrial partners, and experts from science and technology to social sciences and public policy. Speakers from the White House Office of Science and Technology Policy, Department of Energy, universities, and non-governmental agencies discussed the challenges and solutions in Plenary as well as Panel sessions. The talks were recorded with permission from the speakers and the panelists and were put in the ISEE website. The participants included students, postdoctoral fellows, and professors from Virginia Commonwealth University and local area Historically Black College and Universities (HBCU) such as Virginia State University and Norfolk State University. The $5000 fund awarded by the Department of Energy were used to support the participation of students, postdoctoral fellows, and faculty from the HBCU institutions. The technical program the list of participants is provided later in the document.

08 HYDROGEN↗
DOE OSTI2025

Enhanced Laser-Induced Graphene Microfluidic Integrated Sensors (LIGMIS) for On-Site Biomedical and Environmental Monitoring

The convergence of microfluidic and electrochemical biosensor technologies offers significant potential for rapid, in-field diagnostics in biomedical and environmental applications. Traditional systems face challenges in cost, scalability, and operational complexity, especially in remote settings. Addressing these issues, laser-induced graphene microfluidic integrated sensors (LIGMIS) are presented as an innovative platform that integrates microfluidics and electrochemical sensors both comprised of laser-induced graphene. This study advances the LIGMIS concept by resolving issues of uneven fluid transport, increased hydrophobicity during storage, and sensor biofunctionalization challenges. Key innovations include Y-shaped reservoirs for consistent fluid flow, hydrophilic polyethyleneimine coatings to maintain wettability, and separable microfluidic and electrochemical components enabling isolated electrode nanoparticle metallization and biofunctionalization. Multiplexed electrochemical detection of the neonicotinoid imidacloprid and nitrate ions in environmental water samples yields detection limits of 707 nm and 10 -5.4 m with wide sensing ranges of 5–100 µm and 10 -5 –10 -1 m, respectively. Similarly, uric acid and calcium ions are detected in saliva, demonstrating detection limits of 217 nm and 10 -5.3 m with sensing ranges of 10–50 µm, and 10 -5 –10 -2.5 m, respectively. Overall, this biosensing demonstrates the capability of the LIGMIS platform for multiplexed detection in biologically complex solutions, with applications in environmental water quality monitoring and oral cancer screening.

environmental monitoring↗
DOE OSTI2025

Introduction: Neuromorphic Materials

The explosive growth in data collection and the need to process it efficiently, as well as the desire to automate increasingly complex tasks in transportation, medical care, manufacturing, security and many other fields have motivated a growing interest in neuromorphic computing. Unlike the binary, transistorbased ON/OFF logic gates and separate logic and memory functionalities employed in digital computing, neuromorphic computing is inspired by animal brains that use interconnected synapses and neurons to perform processing, storage and transmission of information at the same location, while only consuming ~20 W or less of power. Motivated by the brain’s efficiency, adaptability, self-learning and resiliency qualities, neuromorphic computing can be broadly defined as an approach to processing and storing information using hardware and algorithms inspired by models of biological neural systems. Present research in neuromorphic computing encompasses approaches that vary significantly in their degree of neuro-inspiration, from systems that only incorporate features such as asynchronous, event-driven operation or use crossbar arrays of non-volatile memory (NVM) elements to accelerate deep neural networks (DNNs), to designs that embrace the extreme parallelism, sparsity, reconfigurability, adaptability, complexity and stochasticity observed in nervous systems. The term ‘neuromorphic’ computing is often credited to Carver Mead, who in the 1980s investigated Si-based analog electronics to replicate functions of the animal retina. Earlier important advances in this field include the work of Frank Rosenblatt, who proposed the concept of the perceptron, Bernard Widrow, who used this concept to build one of the first analog neural networks, the Adaline and many other researchers (see ref. 6 for an historical perspective on neuromorphic computing). With the recent increase in the use of artificial intelligence and large language models, and rising concerns over the associated energy costs, interest in neuromorphic hardware has expanded rapidly. According to some estimates, driven largely by the drastic growth in the training use of artificial intelligence (AI) models using the current computing architectures, the energy cost of computing is projected to reach the energy supply worldwide by 2045. Furthermore, while this is not a realistic outcome, it means that, if more efficient computing technologies are not developed -- soon -- the world will soon become one where demand for energy and market constraints limit the continued increase of societal access to AI and cloud services from data centers. Data centers used for training and use of these models consume hundreds of terawatt hours of electricity, already past 4% of the US electricity demand.

Circuits↗
DOE OSTI2023

Modeling, analysis, and optimization of complex nuclear processes and facilities via computational methods: The HALEU process case study

Improving and adapting industrial systems to timely meet changing programmatic and market demands is an important goal to achieve, including when operating and maintaining complex nuclear processes and facilities. However, changes to these complex systems are costly, particularly when they are already in place and bounded to stringent requirements and constraints such as when handling radioactive material and contaminated equipment. These conditions often exist when treating spent nuclear fuel remotely within shielded nuclear radiation chambers, commonly referred as hot cells, to condition nuclear material and/or fabricate products for utilization in other nuclear enterprises such as in the manufacture of advanced nuclear fuel. The illustrative case considered here is the production of high assay low enriched uranium (HALEU) products supporting the deployment of advanced nuclear reactors. For the HALEU program, resources invested were and are being systematically analyzed so that these investments are maximized in a facility that is nearly 60 years old. A methodology that has effectively enabled optimized and improvements in the Spent Fuel Treatment (SFT) program, and consequently the HALEU program, involves discrete event simulation as addressed in this article. Here, the quantification of multiple productivity metrics, including material processing rates, cycle times, bottlenecks, number of material transfers as well as equipment, workstation, and material handling utilization, has resulted in a myriad of diverse discoveries and data-informed decisions regarding process layout and constituent, labor levels and schedules, selection of new process units, storage needs, and other critical process configurations. This article describes such a computational capability being applied for decision-making, illustrates its application to an actual process and program, provides illustrative results, and argues how computational methods for the modeling, analysis, and optimization of complex processes and facilities does lead to informed decisions derived from data and not only from intuition.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Atomic structural mechanism for ferroelectric-antiferroelectric transformation in perovskite NaNbO 3

Sodium niobate (NaNbO 3 or NN) is described as “the most complex perovskite system,” which exhibits transitions between, as well as coexistence of, several ferroelectrics (FE) and antiferroelectric (AFE) phases at different temperatures. Recently, solid solutions of NN with stabilized AFE phases(s) have gained attention for energy-related applications, such as high-density energy storage and electrocaloric cooling. A better understanding of the atomic mechanisms responsible for AFE/FE phase transitions in NaNbO 3 can enable a more rational design of its solid-solution systems with tunable functional properties. In this work, we have investigated changes in the average and local atomic structure of NN using a combination of x-ray/neutron diffraction and neutron pair-distribution function (PDF) analyses. The Rietveld refinement of the x-ray/neutron-diffraction patterns indicates a coexistence of the FE Q (P2 1 ma) and AFE P (Pbma) phases in the temperature range of 300 K ≤ T ≤ 615K, while PDF analysis indicated that the local structure (r < 8Å) is better described by a P2 1 ma symmetry. Above 615 K, the average structure transitions to an AFE R phase (Pmmn or Pnma), while PDF analysis shows an increased disordering of the octahedral distortions and Na displacements at the local scale. These results indicate that the average P/Q/R phase transitions in NN can be described as a result of complex ordering of distorted octahedral tilts at the nanoscale and off-centered displacements of the Na atoms.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Review of Ultrasonic Methods for Monitoring, Damage Detection, and Processing of Lithium-Ion Batteries Throughout Their Life Cycle

Lithium-ion batteries (LIBs) are the leading technology used in consumer electronics, electric vehicles, and grid-level electrochemical energy storage applications. The ever-increasing use of LIBs has highlighted a gap in understanding of their behavior throughout their life cycle. Current monitoring systems rely on electrical and sometimes temperature measurements to assess the internal state which limits information about complex electrochemical processes. In response, ultrasonic testing (UT) has shown promise for non-invasive assessment due to its ease of use and sensitivity to mechanical changes which are correlated with electrochemical changes within the battery. We summarize the research in UT methods applied to LIBs throughout their life cycle. We also discuss physics-based and data-driven modeling approaches used to interpret ultrasonic signals in the context of LIBs, with an emphasis on the existing challenge of establishing rigorous links between electrochemical behavior and elastic and poroelastic wave physics to gain insight regarding physical changes in the LIB that can be directly measured using UT. Finally, we discuss the challenges of implementing UT across the LIB life cycle and identify opportunities for further research. This review aims to provide helpful guidance to researchers and practitioners of UT in the growing field of UT for electrochemical battery systems.

25 ENERGY STORAGE↗
DOE OSTI2022

Deep reinforcement learning based optimization for a tightly coupled nuclear renewable integrated energy system

New ways to integrate energy systems to maximize efficiency are being sought to meet carbon emissions goals. Nuclear-renewable integrated energy system (NR-IES) concepts are a leading solution that couples a nuclear power plant with renewable energy, hydrogen generation plants, and energy storage systems, such that thermal and electrical power are dispatchable to fulfill grid-flexibility requirements while also producing hydrogen and maximizing revenue. Here, this paper introduces a deep reinforcement learning (DRL)-based framework to address the complex decision-making tasks for NR-IES. The objective is to maximize revenue by generating and selling hydrogen and electricity simultaneously according to their time-varying prices while keeping the energy flow in the subsystems in balance. A Python-based simulator for a NR-IES concept has been developed to integrate with OpenAI Gym and Ray/RLlib to enable an efficient and flexible computational framework for DRL research and development. Three state-of-the-art DRL algorithms have been investigated, including two-delayed deep deterministic policy gradient (TD3), soft-actor critic (SAC), proximal policy optimization (PPO), to illustrate DRL’s superiority for controlling NR-IES by comparing it with a conventional control approach, particle swarm optimization (PSO). In this effort, PPO has shown more-stable performance and also better generalization capability than SAC and TD3. Comparisons with PSO have demonstrated that, on average, PPO can achieve 13.9% more mean episode returns from the training process and 29.4% more mean episode returns from the testing process when different hydrogen-production targets are applied.

08 HYDROGEN↗
DOE OSTI2020

Temperature and Solvent Effects on H 2 Splitting and Hydricity: Ramifications on CO 2 Hydrogenation by a Rhenium Pincer Catalyst

The catalytic hydrogenation of carbon dioxide holds immense promise for applications in sustainable fuel synthesis and hydrogen storage. Mechanistic studies that connect thermodynamic parameters with the kinetics of catalysis can provide new understanding and guide predictive design of improved catalysts. Reported here are thermochemical and kinetic analyses of a new pincer-ligated rhenium complex ( tBu POCOP)Re(CO) 2 ( tBu POCOP = 2,6-bis(di-tert-butylphosphinito)phenyl) that catalyzes CO 2 hydrogenation to formate with faster rates at lower temperature. Because the catalyst follows the prototypical “outer sphere” hydrogenation mechanism, comprehensive studies of temperature and solvent effects on the H 2 splitting and hydride transfer steps are expected to be relevant to many other catalysts. Strikingly large entropy associated with cleavage of H2 results in a strong temperature dependence on the concentration of [( tBu POCOP)Re(CO) 2 H] – present during catalysis, which is further impacted by changing the solvent from toluene to tetrahydrofuran to acetonitrile. New methods for determining the hydricity of metal hydrides and formate at temperatures other than 298 K were developed, providing insight into how temperature can influence the favorability of hydride transfer during catalysis. These thermochemical insights guided the selection of conditions for CO 2 hydrogenation to formate with high activity (up to 364 h –1 at 1 atm or 3330 h –1 at 20 atm of 1:1 H 2 CO 2 ). In cases where hydride transfer is the highest individual kinetic barrier, entropic contributions to outer sphere H 2 splitting lead to a unique temperature dependence: catalytic activity increases as temperature decreases in tetrahydrofuran (200-fold increase upon cooling from 50 to 0 °C) and toluene (4-fold increase upon cooling from 100 to 50 °C). Furthermore, the ramifications on catalyst structure-function relationships are discussed, including comparisons between “outer sphere” mechanisms and metal–ligand cooperation mechanisms.

14 SOLAR ENERGY↗
DOE OSTI2022

Differentiable, Learnable, Regionalized Process-Based Models With Multiphysical Outputs can Approach State-Of-The-Art Hydrologic Prediction Accuracy

Predictions of hydrologic variables across the entire water cycle have significant value for water resources management as well as downstream applications such as ecosystem and water quality modeling. Recently, purely data-driven deep learning models like long short-term memory (LSTM) showed seemingly insurmountable performance in modeling rainfall runoff and other geoscientific variables, yet they cannot predict untrained physical variables and remain challenging to interpret. Here, we show that differentiable, learnable, process-based models (called δ models here) can approach the performance level of LSTM for the intensively observed variable (streamflow) with regionalized parameterization. We use a simple hydrologic model HBV as the backbone and use embedded neural networks, which can only be trained in a differentiable programming framework, to parameterize, enhance, or replace the process-based model's modules. Without using an ensemble or post-processor, δ models can obtain a median Nash-Sutcliffe efficiency of 0.732 for 671 basins across the USA for the Daymet forcing data set, compared to 0.748 from a state-of-the-art LSTM model with the same setup. For another forcing data set, the difference is even smaller: 0.715 versus 0.722. Meanwhile, the resulting learnable process-based models can output a full set of untrained variables, for example, soil and groundwater storage, snowpack, evapotranspiration, and baseflow, and can later be constrained by their observations. Both simulated evapotranspiration and fraction of discharge from baseflow agreed decently with alternative estimates. The general framework can work with models with various process complexity and opens up the path for learning physics from big data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Principles to adapt financing mechanisms for fully integrated hybrid energy systems

As the electricity sector evolves, and as all energy types (thermal, electric, chemical, etc.) become more coupled, there has been increased interest to develop and deploy hybrid energy systems (HES). This work focuses on fully integrated HES, where there are multiple energy sources and multiple energy products, often coupled through a storage buffer. A significant amount of the available literature on this work describes technology pathways for fully integrated HES; however, it is unclear how financial institutions should treat these systems. Fully integrated HES represent an increase in complexity from their stand-alone counterparts, but they also potentially mitigate financial risk and provide value to the energy system, which has not yet been accounted for in financing mechanisms that could help to enable such systems. This paper provides some examples of fully integrated HES and proposes principles to help adapt financing to adequately capture the value of such systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Observing and modeling the effects of production infrastructure in electromagnetic surveys

Electromagnetic (EM) methods are among the original techniques for subsurface characterization in exploration geophysics because of their particular sensitivity to the earth electrical conductivity, a physical property of rocks distinct yet complementary to density, magnetization, and strength. However, this unique ability also makes them sensitive to metallic artifacts — infrastructure such as pipes, cables, and other forms of cultural clutter — the EM footprint of which often far exceeds their diminutive stature when compared to that of bulk rock itself. In the hunt for buried treasure or unexploded ordnance, this is an advantage; in the long-term monitoring of mature oil fields after decades of production, it is quite troublesome indeed. In this study we consider the latter through the lens of an evolving energy industry landscape in which the traditional methods of EM characterization for the exploration geophysicist are applied toward emergent problems in well-casing integrity, carbon capture and storage, and overall situational awareness in the oil field. We introduce case studies from these exemplars, showing how signals from metallic artifacts can dominate those from the target itself and impose significant burdens on the requisite simulation complexity. Furthermore, we show how recent advances in numerical methods mitigate the computational explosivity of infrastructure modeling, providing feasible and real-time analysis tools for the desktop geophysicist. Lastly, we demonstrate through comparison of field data and simulation results that incorporation of infrastructure into the analysis of such geophysical data is, in a growing number of cases, a requisite but now manageable step.

58 GEOSCIENCES↗
DOE OSTI2022

3013 Surveillance Program Interim Summary Report (FY16-FY21)

The K-Area Documented Safety Analysis (DSA) requires the facility participate in the SRS Site Surveillance Program as a part of Shipping Package Qualification and Storage Surveillance Program. The Site Surveillance Program outlines activities for field surveillance and laboratory tests that demonstrate the 3013 containers meet the functional performance requirements described in the DSA. The SRS Surveillance Program also supports the complex-wide Integrated Surveillance Program (ISP) for 3013 containers in accordance with DOESTD-3013. The purpose of this report is to provide a summary of the SRS portion of the surveillance program activities from FY16 through FY21 and formally communicate the interpretation of these results by the Surveillance Program Authority (SPA).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Argonne Leadership Computing Facility: 2021 Operational Assessment Report

This Operational Assessment Report describes how the Argonne Leadership Computing Facility (ALCF) met or exceeded every one of its goals for calendar year (CY) 2021 as an advanced scientific computing center. In CY 2021, the ALCF operated its production resource, Theta, an Intel-based Cray XC40 system (11.7-petaflops) augmented with 24 NVIDIA DGX A100-based nodes (3.9-petaflops) that supports diverse workloads, integrating data analytics with artificial intelligence (AI) training and learning in a single platform. In 2021, we began deploying Polaris, our newest 40- petaflops system, and augmented this powerful testbed system with an additional 28 nodes to support the integration of real-time experiments and HPC resources. We also deployed our two largest storage systems yet, named Grand and Eagle, that will bring new services to our users and will power data-driven research for years to come. Last year, Theta delivered a total of 20.8 million node-hours to 16 Innovative and Novel Computational Impact on Theory and Experiment (INCITE) projects and 7.2 million node-hours to ASCR Leadership Computing Challenge (ALCC) projects (32 awarded during the 2020–2021 ALCC year and 17 awarded during the 2021–2022 ALCC year), as well as substantial support to Director’s Discretionary (DD) projects (5.5 million node-hours). As Table ES.1 shows, Theta performed exceptionally well in terms of overall availability (95.1 percent), scheduled availability (99.4 percent), and utilization (98.1 percent; Table 2.1). As of the submission date of this document, ALCF’s user community has published 249 papers in high-quality, peer-reviewed journals and technical proceedings. At the 2021 International Conference for High Performance Computing, Networking, Storage and Analysis (SC’21), Argonne researchers won two HPCwire Readers’ Choice Awards and were part of a Gordon Bell Prize finalist team recognized for developing an AI-enabled, multi-resolution simulation framework for studying complex biomolecular machines. Their framework was used to observe the SARS-CoV-2 replication-transcription machinery in action, by directly integrating experimental data. ALCF also provided a comprehensive program of high-performance computing (HPC) support services to help our community make productive use of the facility’s diverse and growing collection of resources. We are now entering the exascale era, with exascale machines being planned for national laboratories across the country, including Aurora at Argonne National Laboratory (Argonne) in 2023. ALCF researchers have been leading and guiding numerous strategic activities that will push the boundaries of what’s possible in computational science and engineering and allow us to deliver science on day one.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Evaluating the roles of electrolyte components on the passivation of silicon anodes

A protocol was recently developed to compare calendar life using a constant potential while monitoring the electrical current required to maintain the potential. Here, this calendar life protocol is used with electrolyte formulations containing various mole fractions of ethylene carbonate (EC), ethyl methyl carbonate (EMC), and LiPF 6 to elucidate the role each component plays in passivation for high silicon anodes. Together, EC and LiPF 6 lead to higher currents, and thus poorer passivation, whereas EMC acts as a spectator. The variation of the components' mole fraction also changes the solid-electrolyte interphase (SEI) composition, as measured by x-ray photoelectron spectroscopy. Importantly, higher LiPF 6 content leads to increased LiF as well as increased current, indicating that higher LiF content does not enhance the passivation of the silicon surface. Finding that EC did not yield a passivating SEI, instead ethylene sulfite, sulfolane, and propylene carbonate (PC) were used in place of EC. Using ethylene sulfite and sulfolane resulted in poorer passivation compared to EC, whereas PC resulted in superior passivation. As a result, the superior passivation may be related to more stable lithium-solvent complexes.

25 ENERGY STORAGE↗
DOE OSTI2025

Ice storage model-predictive control in an office building with PV: scenario, error and sensitivity analysis

Thermal energy storage (TES) can enable more building-sited renewable electricity generation and lower utility bill costs for buildings owners and occupants, especially when there are high demand and variable time-of-use (TOU) charges. A model predictive control (MPC) strategy can offer additional savings over a schedule-based control with added complexity and reliance on forecasts. Here, this study examines savings for medium office buildings with chiller plants in three locations with building-installed solar photovoltaics (PV) to understand the impact of MPC. Control setpoints are fixed by a schedule-based control or optimized by nonlinear MPC. These control setpoints are actuated within EnergyPlus building models to simulate the utility cost of the chiller plant. NLP solutions can be unstable or unrealistic, but our results show that by regularizing the NLP, the solutions can be reasonably followed by the building model. MPC models make simplifications that lead to errors once the controller is participating in and changing the operation of the building. These errors average 9 % across the cases, showing that the most important parts of the system are represented. The no-thermal load costs are computed to show that the optimization can in some cases achieve both the minimum TOU and minimum monthly demand costs by demand management while reducing TOU energy costs by energy arbitrage. The MPC saves 35–66 % in the annual chiller plant operating costs, which is an additional savings above the schedule by 1–33 %. PV and TES are complementary and mostly independent, but a load with PV often results in better performance for the schedule. Our case study and sensitivity analysis show the importance of modeling and optimization for complex rates, but also the circumstances wherein a simpler strategy achieves the same performance with less potential for error.

14 SOLAR ENERGY↗
DOE OSTI2021

Structural and chemical evolution in layered oxide cathodes of lithium-ion batteries revealed by synchrotron techniques

Abstract Rechargeable battery technologies have revolutionized electronics, transportation and grid energy storage. Many materials are being researched for battery applications, with layered transition metal oxides (LTMO) the dominating cathode candidate with remarkable electrochemical performance. Yet, daunting challenges persist in the quest for further battery developments targeting lower cost, longer lifespan, improved energy density and enhanced safety. This is, in part, because of the intrinsic complexity of real-world batteries, featuring sophisticated interplay among microstructural, compositional and chemical heterogeneities, which has motivated tremendous research efforts using state-of-the-art analytical techniques. In this research field, synchrotron techniques have been identified as a suite of effective methods for advanced battery characterization in a non-destructive manner with sensitivities to the lattice, electronic and morphological structures. This article provides a holistic overview of cutting-edge developments in synchrotron-based research on LTMO battery cathode materials. We discuss the complexity and evolution of LTMO’s material properties upon battery operation and review recent synchrotron-based research works that address the frontier challenges and provide novel insights in this field. Finally, we formulate a perspective on future directions of synchrotron-based battery research, involving next-generation X-ray facilities and advanced computational developments.

25 ENERGY STORAGE↗
DOE OSTI2021

An Overview of RF Systems for the EIC

The Electron Ion Collider (EIC) to be constructed at Brookhaven National Laboratory in the USA will be a complex system of accelerators providing high luminosity, high polarization, variable center of mass energy collisions between electrons and protons or ions. To achieve this a variety of RF systems are required. They must provide for capture, formation and storage of Ampere-class beams in the electron and hadron storage rings (ESR and HSR), fast acceleration of high-charge polarized electron bunches in the rapid cycling synchrotron (RCS), provision of cold high current electron bunches in the high-energy cooler ERL and precise high-gradient crabbing of electrons and hadrons either side of the interaction point. The challenges include strong HOM damping in the storage ring cavities and cooler ERL, very high fundamental mode power in the ESR and cooler injector, extremely stable low-noise operation of the crab cavities, mitigation of transient beam loading from gaps, and operating over a wide range of energies and beam currents. We describe the high-level system parameters and principal design choices made and progress on the R&D plan to develop these state of the art systems.

Rimmer, R. A.↗