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DOE OSTI2017

Initial results from safety testing of US AGR-2 irradiation test fuel

Two cylindrical compacts containing tristructural isotropic (TRISO)-coated particles with kernels that contained a mixture of uranium carbide and uranium oxide (UCO) and two compacts with UO 2 -kernel TRISO particles have undergone 1600°C safety testing. These compacts were irradiated in the US Advanced Gas Reactor Fuel Development and Qualification Program's second irradiation test (AGR-2). The time-dependent releases of several radioisotopes ( 110m Ag, 134 Cs, 137 Cs, 154 Eu, 155 Eu, 90 Sr, and 85 Kr) were monitored while heating the fuel specimens to 1600°C in flowing helium for 300 h. The UCO compacts behaved similarly to previously reported 1600°C-safety-tested UCO compacts from the AGR-1 irradiation. No failed TRISO or failed SiC were detected (based on krypton and cesium release), and cesium release through intact SiC was very low. Release behavior of silver, europium, and strontium appeared to be dominated by inventory originally released through intact coating layers during irradiation but retained in the compact matrix until it was released during safety testing. Both UO 2 compacts exhibited cesium release from multiple particles whose SiC failed during the safety test. Europium and strontium release from these two UO 2 compacts appeared to be dominated by release from the particles with failed SiC. Silver release was characteristically like the release from the UCO compacts in that an initial release of the majority of silver trapped in the matrix occurred during ramping to 1600°C. However, additional silver release was observed later in the safety testing due to the UO 2 TRISO with failed SiC. Failure of the SiC layer in the UO 2 fuel appears to have been dominated by CO corrosion, as opposed to the palladium degradation observed in AGR-1 UCO fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS2006

Model-Based Safety Analysis

System safety analysis techniques are well established and are used extensively during the design of safety-critical systems. Despite this, most of the techniques are highly subjective and dependent on the skill of the practitioner. Since these analyses are usually based on an informal system model, it is unlikely that they will be complete, consistent, and error free. In fact, the lack of precise models of the system architecture and its failure modes often forces the safety analysts to devote much of their effort to gathering architectural details about the system behavior from several sources and embedding this information in the safety artifacts such as the fault trees. This report describes Model-Based Safety Analysis, an approach in which the system and safety engineers share a common system model created using a model-based development process. By extending the system model with a fault model as well as relevant portions of the physical system to be controlled, automated support can be provided for much of the safety analysis. We believe that by using a common model for both system and safety engineering and automating parts of the safety analysis, we can both reduce the cost and improve the quality of the safety analysis. Here we present our vision of model-based safety analysis and discuss the advantages and challenges in making this approach practical.

Joshi, Anjali↗

DOE OSTI2025

Structure evolution and tin redistribution during oxidation of Zircaloy-4 at 500°C

Zirconium (Zr) alloys are widely used as fuel cladding in nuclear power reactors due to their thermal stability, mechanical durability, corrosion resistance, and low neutron absorption cross-section. However, their performance is challenged by oxidation in reactor environments, making the study of Zr alloy corrosion behavior crucial for ensuring the safety, longevity, and economic viability of nuclear power systems. While the oxidation behavior of Zr-based cladding materials has been extensively studied since the 1950s, a mechanistic understanding into the relationship between structure evolution, solute element redistribution, and properties remains elusive. Valuable insights may be obtained through advanced experimental methods, such as in-situ and high resolution microscopy techniques. Here, in this study, the oxidation behavior of Zircaloy-4 at 500 °C in O 2 is characterized using a multimodal advanced characterization approach. Using in-situ X-ray diffraction, the phase evolution from metastable to stable oxides is tracked in real time. Complementary high-resolution techniques, including electron microscopy and atom probe tomography, reveal nanoscale insights into the microstructural changes and solute redistribution across the oxide/metal interface. Nanohardness mapping across the oxide/metal interface highlights localized mechanical property variations that may be linked to changes in microstructure and crystal structure within the oxide layer. These findings offer valuable insights into the microstructure and property evolution of Zircaloy-4 during oxidation, contributing to a better understanding of microstructural changes in Zr-based alloys under oxidative environments.

APT↗

NASA NTRS1980

Performability evaluation of the SIFT computer

The paper deals with the models, techniques, and evaluation methods that were successfully used to test the performance of the SIFT degradable computing system. The performance of the computer plus its air transport mission environment is modeled as a random variable, taking values in a set of 'accomplishment level'. The levels are defined in terms of four attributes of total system (computer plus environment) behavior, namely safety, no change in mission profile, no operational penalties, and no economic penalties. The base model of the total system is a stochastic process, whose states describe the internal structure of SIFT and the relevant conditions of its computational environment. Base model state trajectories are related to accomplishment levels via a special function, and solution methods are then used to determine the performability of the total system for various parameters of the computer and environment.

Meyer, J. F.↗

NASA NTRS2000

Magnetically Actuated Propellant Orientation Experiment, Controlling Fluid Motion With Magnetic Fields in a Low-Gravity Environment

This report details the results of a series of fluid motion experiments to investigate the use of magnets to orient fluids in a low-gravity environment. The fluid of interest for this project was liquid oxygen (LO2) since it exhibits a paramagnetic behavior (is attracted to magnetic fields). However, due to safety and handling concerns, a water-based ferromagnetic mixture (produced by Ferrofluidics Corporation) was selected to simplify procedures. Three ferromagnetic fluid mixture strengths and a nonmagnetic water baseline were tested using three different initial fluid positions with respect to the magnet. Experiment accelerometer data were used with a modified computational fluid dynamics code termed CFX-4 (by AEA Technologies) to predict fluid motion. These predictions compared favorably with experiment video data, verifying the code's ability to predict fluid motion with and without magnetic influences. Additional predictions were generated for LO2 with the same test conditions and geometries used in the testing. Test hardware consisted of a cylindrical Plexiglas tank (6-in. bore with 10-in. length), a 6,000-G rare Earth magnet (10-in. ring), three-axis accelerometer package, and a video recorder system. All tests were conducted aboard the NASA Reduced-Gravity Workshop, a KC-135A aircraft.

Martin, J. J.↗

DOE OSTI2026

Biobased Flame Retardants Towards Sustainable Building Materials with Low Embodied Carbon

Incorporation of fire retardants in different types of products is one of the essential steps in the material production process to minimize fire risk and meet fire safety requirements. A variety of commonly used flame retardants based on halogen, mineral, and other compounds has gained popularity due to their efficient flame-retardant behaviors. However, especially in the case of halides, there are numerous toxicity related issues and environmental pollution effects that have urged the building sector to deviate from their use and focus on the development of non-toxic alternatives. Therefore, to enhance the safety of flame retardants, the synthesis of flame retardants from waste feedstocks using phosphorous chemistry with a dual flame-retardant mechanism has been established. A range of waste feedstocks that include cardanol, vanillin, and gallic acids has been converted into a series of flame retardants using a one-step approach by incorporating phosphorous moieties into their structures. The established pathways allow to develop a range of flame-retardant materials by converting waste feedstocks into phosphorous-based materials with inherent flame retardancy. The introduction of abundant aromatic structures from biobased feedstocks enables high charring capabilities in materials in which these biobased flame retardants have been incorporated, increasing their char yields which significantly enhances the flame suppressing properties of the designed materials. Studies show that inclusion of phosphoric moieties into structures allows the displacement of flame enhancing radicals, by releasing non-flammable and non-toxic gases, therefore inhibiting the spread of fire in the gas phase. The resulting biobased flame retardants have been incorporated in wood substrates, foam insulation, and hemp fibers where the results show that only 1-5% of loading of biobased flame retardant suppressed the flame completely. This study represents a novel approach for the development of flame retardants with high performance while utilizing waste feedstocks as a source for their design.

Demchuk, Zoriana [ORNL] (ORCID:0000000326292235)↗

DOE OSTI2022

Anomalous Thermal Decomposition Behavior of Polycrystalline LiNi 0.8 Mn 0.1 Co 0.1 O 2 in PEO-Based Solid Polymer Electrolyte

we report that replacing liquid electrolytes (LEs) with polymer electrolytes has been considered a promising approach to developing next-generation lithium-ion batteries (LIBs) with high energy density and superior safety. Nevertheless, compared with the extensive research on the electrochemical stability of the cathode/polymer electrolyte interfaces, reports on their thermal behaviors are rare to date. Herein, this work systematically investigates the thermal stability of two typical layered oxide cathodes, LiCoO 2 (LCO) and LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811), with poly(ethylene oxide) (PEO) electrolyte and with carbonate LEs, respectively. It is found that the oxygen release from the cathodes plays a central role in thermal runaway. Replacing the LE with PEO electrolyte can considerably improve the thermal stability of LCO, but surprisingly, deteriorate that of NMC811. The reason is that the surface of single-crystalline LCO particles can be effectively passivated by the PEO electrolyte during heating, but PEO cannot sufficiently passivate all the primary particles of NMC811 owing to insufficient interface wettability of PEO electrolyte within the polycrystalline secondary NMC811 particles. The findings in this work collectively formulate valuable guidance for improving the safety of polymer-electrolyte-based as well as other types of all-solid-state lithium-ion batteries.

25 ENERGY STORAGE↗

DOE OSTI2018

Hydrogen Quantitative Risk Assessment

DOE has identified consistent safety, codes, and standards as a critical need for the deployment of hydrogen technologies, with key barriers related to the availability and implementation of technical information in the development of regulations, codes, and standards. Advances in codes and standards have been enabled by risk-informed approaches to create and implement revisions to codes, such as National Fire Protection Association (NFPA) 2, NFPA 55, and International Organization for Standardization (ISO) Technical Specification (TS)-19880-1. This project provides the technical basis for these revisions, enabling the assessment of the safety of hydrogen fuel cell systems and infrastructure using QRA and physics-based models of hydrogen behavior. The risk and behavior tools that are developed in this project are motivated by, shared directly with, and used by the committees revising relevant codes and standards, thus forming the scientific basis to ensure that code requirements are consistent, logical, and defensible.

08 HYDROGEN↗

DOE OSTI2019

Hydrogen Quantitative Risk Assessment (Annual Progress Report)

08 HYDROGEN↗

NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗

DOE OSTI2023

Light Water Sustainability Program: Optimizing Information Automation Using a New Method Based on System-Theoretic Process Analysis

This report describes the interim progress for research supporting the design and optimization of information automation systems for nuclear power plants. Much of the domestic nuclear fleet is currently focused on modernizing technologies and processes, including transitioning toward digitalization in the control room and elsewhere throughout the plant, along with a greater use of automation, artificial intelligence, robotics, and other emerging technologies. While there are significant opportunities to apply these technologies toward greater plant safety, efficiency, and overall cost-effectiveness, optimizing their design and avoiding potential safety and performance risks depends on ensuring that human-performance-related organizational and technical design issues are identified and addressed. This report describes modeling tools and techniques, based on sociotechnical system theory, to support these design goals and their application in the current research effort. The report is intended for senior nuclear energy stakeholders, including regulators, corporate management, and senior plant management. We have developed and employed a method to design an optimized information automation ecosystem (IAE) based on the systems-theoretic constructs underlying sociotechnical systems theory in general and the Systems-Theoretic Accident Modeling and Processes (STAMP) approach in particular. We argue that an IAE can be modeled as an interactive information control system whose behavior can be understood in terms of dynamic control and feedback relationships amongst the system’s technical and organizational components. Up to this point, we have employed a Causal Analysis based on STAMP (CAST) technique to examine a performance- and safety-related incident at an industry partner’s plant that involved the unintentional activation of an emergency diesel generator. This analysis provided insight into the behavior of the plant’s current information control structure within the context of a specific, significant event. Our ongoing analysis is focused on identifying near-term process improvements and longer-term design requirements for an optimized IAE system. The latter analyses will employ a second STAMP-derived technique, System-Theoretic Process Analysis (STPA). STPA is a useful modeling tool for generating and analyzing actual or potential information control structures. Finally, we have begun modeling plantwide organizational relationships and processes. Organizational system modeling will supplement our CAST and STPA findings and provide a basis for mapping out a plantwide information control architecture. CAST analysis findings indicate an important underlying contributor to the incident under investigation, and a significant risk to information automation system performance, was perceived schedule pressure, which exposed weaknesses in interdepartmental coordination between and within responsible plant organizations and challenged the resilience of established plant processes, until a human caused the initiating event. These findings are discussed in terms of their risk to overall system performance and their implications for information automation system resilience and brittleness. We present two preliminary information automation models. The proactive issue resolution model is a test case of an information automation concept with significant near-term potential for application and subsequent reduction in significant plant events. The IAE model is a more general representation of a broader, plantwide information automation system. From our results, we have generated a set of preliminary system-level requirements and safety constraints. These requirements will be further developed over the remainder of our project in collaboration with nuclear industry subject matter experts and specialists in the technical systems under consideration. Additionally, we will continue to pursue the system analyses initiated in the first part of our effort, with a particular emphasis on STPA as the main tool to identify weak or weakening control structures that affect the resilience of organizations and programs. Our intent is to broaden the scope of the analysis from an individual use case to a related set of use cases (e.g., maintenance tasks, compliance tasks) with similar human-system performance challenges. This will enable more generalized findings to refine the Proactive Issue Resolution and IAE models, as well as their system-level requirements and safety constraints. We will use organizational system modeling analyses to supplement STPA findings and model development. We conclude the report with a set of summary recommendations and an initial draft list of system-level requirements and safety constraints for optimized information automation systems.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2019

Decoupling of heat generated from ejected and non-ejected contents of 18650-format lithium-ion cells using statistical methods

Effective thermal management systems, designed to handle the impacts of thermal runaway (TR) and to prevent cell-to-cell propagation, are key to safe operation of lithium-ion (Li-ion) battery assemblies. Critical factors for optimizing these systems include the total energy released during a single cell TR event and the fraction of the total energy that is released through the cell casing versus through the ejecta material. A unique fractional thermal runaway calorimeter (FTRC), designed to characterize said critical factors, was utilized to examine the TR behavior of 18650-format Li-ion cells representing a variety of manufacturers, chemistries, capacities, and safety features. Primarily, the impacts of bottom vent (BV) safety features, varied cell casing thickness, and Dreamweaver cellulose based separators were assessed for select cell types. A subset of cells also had an imbedded internal short circuiting (ISC) device to allow examination of TR behavior initiated at lower temperatures (i.e. closer to field failure conditions). The impact of bottom rupture on TR behavior was also examined for experiments that resulted in this non-standard failure mechanism. Statistical analysis of the results for each cell configuration reveals that a lognormal distribution effectively characterizes the variation of total TR energy release. Typically 20%-30% of the total energy yield is released through the cell casing with the remainder through the ejecta material. Higher energy cells tend to exhibit more violent ejections and less predictable TR events. Inclusion of a BV feature reduces the overall severity of the TR event and can increase the predictability. Results also suggest that the magnitude of the TR event may not be directly proportional to the stored electrical energy, but rather has additional dependence on other design and failure mode factors.

25 ENERGY STORAGE↗

DOE OSTI2025

Development of a Digital Twin for Hydrogen Dispersion and Safety Assessment in an Electrolyzer Based Hydrogen Production Facility

Digital twin models are virtual representations of physical systems that use real-time data to simulate and optimize performance. This study presents the development and initial implementation of a digital twin (DT) for the electrolyzer-based hydrogen production facility at NREL's Advanced Research on Integrated Energy Systems (ARIES), focused on enhancing safety and optimizing sensor placement through physics-based simulations and metadata integration. The DT incorporates detailed facility-specific information, including component layout, leak locations, and controlled release parameters, to model hydrogen dispersion under varying environmental conditions. Using steady-state computational fluid dynamics (CFD) simulations informed by real meteorological data, such as wind speed, direction, and vertical wind profiles, the DT enables visualization of hydrogen plume behavior and spatial concentration distributions. Comparative analysis between high and low wind speed scenarios illustrates the significant influence of wind dynamics on plume shape and extent, with horizontal momentum dominating dispersion at higher speeds, while buoyancy effects become more prominent under low wind conditions. These simulations generate a rich dataset embedded within the DT, allowing users to assess potential leak outcomes and identify optimal sensor locations based on concentration thresholds. The model supports scenario-based analysis to guide safety strategies and equipment deployment for open-area hydrogen infrastructure. The digital twin thus serves as a dynamic platform for virtual prototyping, providing predictive insight into hydrogen behavior and enhancing risk-informed decision-making. This initial phase establishes a validated foundation for future integration of transient, uncontrolled leak scenarios and real-time sensor feedback, positioning the DT as a critical tool for safety design, operational planning, and adaptive monitoring in hydrogen systems. Overall, the approach demonstrates the value of combining environmental data with digital simulations to inform safer and more efficient deployment of hydrogen technologies.

08 HYDROGEN↗

DOE OSTI2026

Development of a Digital Twin for Hydrogen Dispersion and Safety Assessment in an Electrolyzer-Based Hydrogen Production Facility: Preprint

Digital twin models are virtual representations of physical systems that use real-time data to simulate and optimize performance. This study presents the development and initial implementation of a digital twin (DT) for the electrolyzer-based hydrogen production facility at the National Renewable Energy Laboratory (NREL)'s Advanced Research on Integrated Energy Systems (ARIES), focused on enhancing safety and optimizing sensor placement through physics-based simulations and metadata integration. The DT incorporates detailed facility-specific information, including component layout, leak locations, and controlled release parameters, to model hydrogen dispersion under varying environmental conditions. Using steady-state computational fluid dynamics (CFD) simulations informed by real meteorological data, such as wind speed, direction, and vertical wind profiles, the DT enables visualization of hydrogen plume behavior and spatial concentration distributions. Comparative analysis between high and low wind speed scenarios illustrates the significant influence of wind dynamics on plume shape and extent, with horizontal momentum dominating dispersion at higher speeds, while buoyancy effects become more prominent under low wind conditions. These simulations generate a rich dataset embedded within the DT, allowing users to assess potential leak outcomes and identify optimal sensor locations based on concentration thresholds. The model supports scenario-based analysis to guide safety strategies and equipment deployment for open-area hydrogen infrastructure. The digital twin thus serves as a dynamic platform for virtual prototyping, providing predictive insight into hydrogen behavior and enhancing risk-informed decision-making. This initial phase establishes a validated foundation for future integration of transient, uncontrolled leak scenarios and real-time sensor feedback, positioning the DT as a critical tool for safety design, operational planning, and adaptive monitoring in hydrogen systems. Overall, the approach demonstrates the value of combining environmental data with digital simulations to inform safer and more efficient deployment of hydrogen technologies.

08 HYDROGEN↗

DOE OSTI2022

Understanding the Effect of Cathode Composition on the Interface and Crosstalk in NMC/Si Full Cells

Crosstalk between the cathode and the anode in lithium-ion batteries has a great impact on performance, safety, and cycle lifetime. Still, no report exists for a systematic investigation on crosstalk behavior in silicon (Si)-based cells as a function of transition metal composition in cathodes. We studied the effect of crosstalk on degradation of Si-rich anodes in full cells with different cathodes having the same crystal structure but different transition metal compositions, such as LiNi 1/3 Mn 1/3 Co 1/3 O 2 (NM111), LiNi 0.5 Mn 0.3 Co 0.2 O 2 (NMC532), and LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811). We found that the transition metal composition in cathodes, especially Mn ion concentration, significantly affects electrolyte decomposition reactions, even from very early cycles. This change causes differences in the solid electrolyte interphase (SEI) chemistry of each aged Si sample. As a result, each of the aged Si samples has a different electrochemistry, in terms of initial Coulombic efficiency and the mechanism of capacity fade.

25 ENERGY STORAGE↗

NASA NTRSDate not supplied

Opposed-flow Spreading Flames: Effect of Sub-atmospheric Pressure on Spread and Burning Rates

Flame spread over solid fuels is a canonical problem in fire science, due to its direct implications on material flammability and importance in fire development. Flames in a microgravity environment can behave very differently than on Earth, posing additional risks for spaceflight life safety. Sub-atmospheric pressures in ground-based experiments have been proposed to approximately replicate the burning behavior of solid fuels in reduced gravity conditions because of similar effects on heat and mass transfer mechanisms from the flame to the solid. In opposed-flow flame spread, the solid fuel is heated by the flame ahead of its leading edge, and this process is strongly affected by the ambient conditions. In this work, we consider flames spreading over flat thin acrylic samples exposed to a forced flow of 20 cm/s, and pressures between 30 and 100 kPa. When radiation losses and chemical kinetic effects are neglected, experimental and theoretical results suggest an independence of the spread rate on pressure. However, the spread rate shows a decreasing trend with lower pressure in the range 30-60 kPa. The mass burning rate, calculated from the samples weight measured before and after the experiments, shows a similar trend. The flame length, on the other hand, shows a non-monotonic behavior, reaching a maximum at 60 kPa. Additionally, gas emissions measured during the experiments are used to estimate the heat release rate of the spreading flames. The comparison of the heat release rate obtained from the measured emissions and the estimated mass burning rates, suggests that chemical kinetics is not driving the decrease in flame spread observed at low pressures, whereas radiation losses could be responsible in analogy to what has been observed in micro and partial gravity studies. These results could provide more information to guide future Earth-based material flammability testing for spacecraft applications.

fire↗

DOE OSTI2017

Kriging-Based Inverse Uncertainty Quantification of BISON Fission Gas Release Model

Nuclear reactor fuel performance analysis studies the thermo-mechanical behavior of fuel rods and verify their compliance with safety criteria under both normal operation and accidental conditions. One of the major concerns in fuel performance analysis is the behavior of the fission gases xenon and krypton in uranium dioxide fuel, which significantly affect the thermo-mechanical performance of the nuclear fuel rods employed in current LWRs. Previously, the modeling of fission gas release (FGR) in nuclear fuel performance codes have been relying on empirical models which are not applicable beyond their range of calibration. Recently a more efficient and flexible physics-based FGR and swelling model that can describe a wider range of reactor operation conditions have been implemented in fuel performance code BISON. In this work, we performed Bayesian calibration to inversely quantify the uncertainties associated with the 5 tuning parameters in BISON FGR model, based on the time-dependent FGR measurement data from Riso-AN3 benchmark. It takes around 1 hour and 40 minutes for BISON to simulate the Riso-AN3 benchmark with a moderate mesh size using 32 processors. As hundreds of thousands of BISON simulations are needed during Markov Chain Monte Carlo (MCMC) sampling, running BISON directly is not a viable solution. Therefore we propose to use Kriging-based metamodels to replace BISON during MCMC sampling. Surrogate model is an approximation of the input/output relation of a computer code/model. It is also called metamodel, response surface, emulator. Surrogate models usually take much less computational time than the original model (like BISON) while maintaining the input/output relation of the original model to a desirable accuracy. Constructing the metamodels normally requires small number of executions of the original model. Once validated, metamodels can be used to perform uncertainty and sensitivity analysis, Bayesian calibration, optimization, etc.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

A new mechanism of stabilizing SEI of Si anode driven by crosstalk behavior and its potential for developing high performance Si-based batteries

Stabilizing solid electrolyte interphase (SEI) is a key factor for determining cell performance of Silicon (Si) anode, such as safety, cycle lifetime, and calendar lifetime. Here, we found a new potential for stabilizing SEI of the Si anode, driven by crosstalk with cathode material. Here we investigated the effect of crosstalk on the chemistry of SEI of the Si anode as a function of three different, representative cathode materials: LiNi 0.5 Mn 0.3 Co 0.2 O 2 (NMC532), LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811), and LiFePO 4 (LFP). Specifically, we observed that crosstalk significantly affected the formation and growth mechanism of SEI layer on the Si anodes. Unexpectedly, dissolved Fe ions from the LFP cathode has a positive impact on the chemistry and electrochemical stability of the SEI layer of Si anode compared to the other cathodes, resulting in better electrochemical performance in terms of initial coulombic efficiency and capacity fade.

25 ENERGY STORAGE↗