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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Resilience Design Patterns: A Structured Approach to Resilience at Extreme Scale (V.2.0)

Reliability is a serious concern for future extreme-scale high-performance computing (HPC) systems. Projections based on the current generation of HPC systems and technology roadmaps suggest the prevalence of very high fault rates in future systems. The errors resulting from these faults will propagate and generate various kinds of failures, which may result in outcomes ranging from result corruptions to catastrophic application crashes. Therefore, the resilience challenge for extreme-scale HPC systems requires coordination between various hardware and software technologies that are capable of handling a broad set of fault models at accelerated fault rates. Also, due to practical limits on power consumption in future HPC systems, they are likely to embrace innovative architectures, increasing the levels of hardware and software complexities. Therefore, the techniques that seek to improve resilience must navigate the complex trade-off space between resilience and the overheads to power consumption and performance. While the HPC community has developed various resilience solutions, application-level techniques as well as system-based solutions, the solution space of HPC resilience techniques remains fragmented. There are no formal methods to integrate the various HPC resilience techniques into composite solutions, nor are there methods to holistically evaluate the adequacy and efficacy of such solutions in terms of their protection coverage, and their performance & power efficiency characteristics. Additionally, few implementations of current resilience solutions are portable to newer architectures and software environments that will be deployed on future systems. We developed a new structured approach to the management of HPC resilience using the concept of resilience-based design patterns. In general, a design pattern is a repeatable solution to a commonly occurring problem. We identified the well-known solutions that are commonly used to deal with faults, errors and failures in HPC systems. In the initial design patterns specification (version 1.0), we described the various solutions, which address specific problems in the design of resilient HPC environments, in the form of patterns. Each pattern describes a problem caused by a fault, error or failure event in an HPC environment, and then describes the core of the solution of the problem in such a way that this solution may be adapted to different systems and implemented at different layers of the system stack. The catalog of these resilience design patterns provides designers with a collection of design elements. To construct complete resilience solutions using combinations of various patterns, we defined a framework that enhances HPC designers' understanding of the important constraints and the opportunities for the design patterns to be implemented and deployed at various layers of the system stack. The design framework is also useful for establishing interfaces and mechanisms to coordinate flexible fault management across hardware and software components, as well as to consider the trade-off between performance, resilience, and power consumption when constructing a solution. The resilience design patterns specification version 1.1 included more detailed explanations of the pattern solutions, the context in which the patterns are applicable, and the implications for hardware or software design. It also provided several additional examples and detailed case studies to demonstrate the use of patterns to build realistic solutions. In version 1.2 of the specification document, we have improved the pattern descriptions, including graphical representations of the pattern components. These improvements are largely based on critical comments, feedback and suggestions received from pattern experts and readers of the previous versions of the specification. The pattern classification has been modified to further clarify the relationships between pattern categories. This version of the specification also introduces a pattern language for resilience design patterns. The pattern language presents the patterns in the catalog as a network, revealing the relations among the resilience patterns. The language provides designers with the means to explore alternative techniques for handling a specific fault model that may have different efficiency and complexity characteristics. Using the pattern language also enables the design and implementation of comprehensive resilience solutions as a set of interconnected resilience patterns that can be instantiated across layers of the system stack. The overall goal of this work is to provide hardware and software designers, as well as the users and operators of HPC systems, a systematic methodology for the design and evaluation of resilience technologies in HPC systems that keep scientific applications running to a correct solution in a timely and cost-efficient manner despite frequent faults, errors, and failures of various types. Version 2.0 expands the resilience design pattern classification and catalog to include self-stabilization patterns and reliability, availability and performance models for each structural pattern.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Performance of wind assessment datasets in United States coastal areas

The atmospheric dynamics that occur near the intersection of land and water offer exciting and challenging opportunities for wind energy deployment in coastal locations. New models and tools are continually being developed in support of wind resource assessment, and three recent products are explored in this work for their performance in representing characteristics of the wind resource at coastal locations: the Global Wind Atlas 3 (GWA3), the 2023 National Offshore Wind dataset (NOW-23), and the wind climate simulations that are a component of the Wind Integration National Dataset (WIND) Toolkit Long-Term Ensemble Dataset (WTK-LED Climate). These relatively new products are freely available and user-friendly so that anyone – from a utility-scale developer to a resident or business owner – can evaluate the potential for wind energy generation at their location of interest. The validations in this work provide guidance on the accuracy of wind resource assessments for coastal customers interested in installing small or midsize wind turbines (≤ 1 MW in capacity) to support energy needs at the residential, business, or community scale, such as the island and remotely located participants of the U.S. Department of Energy's Energy Transitions Initiative Partnership Project. At 23 coastal locations across the United States, dataset performance varies according to different evaluation metrics. All three recent datasets tend to overestimate the observed coastal wind resource. GWA3 produces the smallest annual average wind speed relative errors, whereas WTK-LED Climate is in best agreement in terms of representing diurnal wind speed cycles. NOW-23 is the highest performing of the datasets for representing seasonal and interannual trends in the coastal wind resource. While GWA3 and WTK-LED Climate are relatively insensitive to the dataset output heights selected for wind resource assessment at small and midsize wind turbine hub heights (20–60 m), significant variation in the NOW-23 representation of wind shear across the wind profile in the lowest 100 m of the atmosphere leads to notable differences in wind speed estimates according to the dataset output heights selected for evaluation. GWA3 exhibits challenges in the representation of observed wind speed diurnal cycles at small and midsize turbine hub heights, likely due to the dataset's consistent treatment of hourly wind speed trends regardless of altitude.

17 WIND ENERGY↗
DOE OSTI2022

Optimal Control of Biomass Feedstock Processing System Under Uncertainty in Biomass Quality

Planning of biorefinery operations is complicated by the stochastic nature of physical and chemical characteristics of biomass feedstock, such as, moisture level and carbohydrate content. Biomass characteristics affect the performance of the equipment which feed the reactor and the efficiency of the conversion process in a biorefinery. We propose a stochastic optimization model to identify a blend of feedstocks, inventory levels, and operating conditions of equipment to ensure a continuous flowing of biomass to the reactor while meeting the requirements of the biochemical conversion process. We propose a sample average approximation (SAA) of the model, and develop an efficient algorithm to solve the SAA model. A feedstock preprocessing process consists of two-stage grinding and pelleting is used to develop a case study. Extensive numerical analysis are conducted which lead to a number of observations. Our main observation is that sequencing bales based on moisture level and carbohydrate content leads to robust solutions that improve processing time and processing rate of the reactor. We provide a number of managerial insights that facilitate the implementation of the model proposed. Note to Practitioners—This paper is motivated by the challenges faced in the bioenergy industry. The focus of this paper is on plants which use the biochemical conversion process to generate liquid fuels. It has been observed that variations in biomass characteristics, such as moisture content, cause variations in feeding of the system which lead to under-utilization of equipment. A requirement of biochemical conversion process is to maintain the carbohydrate content of biomass processed by the reactor, larger than a threshold. We propose a model that identifies the inventory levels and operating conditions of equipment to ensure a continuous flowing of biomass to the reactor. The goal is to improve equipment utilization while satisfying the requirements of the conversion process. The model is tested using real-life data. We found out that by sequencing bales based on moisture level and carbohydrate content, a plant can reduce variability in the system leading to improved system reliability, higher processing rates of the reactor, and higher throughput.

09 BIOMASS FUELS↗
DOE OSTI2022

Unprecedented Multifunctionality in 1D Nb 1- x Ta x S 3 Transition Metal Trichalcogenide Alloy

One-dimensional (1D) van der Waals materials, such as nanofibers or nanoribbons are considered as the future ultimate limit of downscaling for modern electrical and electrochemical devices. Here, for the first time, we successfully synthesize nanofibers of a solid solution transition metal trichalcogenide (TMTC), Nb 1-x Ta x S 3 , with outstanding electrical, thermal, and electrochemical characteristics rivaling the performance of the-state-of-the art materials for each application. This material shows nearly unchanged sheet resistance (≈740 Ω/sq -1 ) versus bending cycles tested up to 90 cycles, stable sheet resistance in ambient conditions tested up to 60 days, remarkably high electrical breakdown current density of ≈ 30 MA cm -2 , strong evidence of successive charge density wave (CDW) transitions, and outstanding thermal stability up to ≈900 K. Additionally, this material demonstrates excellent activity and selectivity for CO2 conversion to CO reaching ≈ 350 mA cm -2 at – 0.8 V vs RHE with an outstanding turnover frequency number of 25 for CO formation. It also exhibits an excellent performance in a high-rate Li-air battery with the specific capacity of 3000 mAhg -1 at a high current density of 0.3 mAcm -2 . This study uncovers the multifunctionality in 1D TMTC alloys for a wide range of high-impact applications and opens a new direction for the design of the next generation of low-dimensional materials with high performance.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Balanced k -means clustering on an adiabatic quantum computer

Adiabatic quantum computers are a promising platform for efficiently solving challenging optimization problems. Therefore, many are interested in using these computers to train computationally expensive machine learning models. We present a quantum approach to solving the balanced k-means clustering training problem on the D-Wave 2000Q adiabatic quantum computer. In order to do this, we formulate the training problem as a quadratic unconstrained binary optimization (QUBO) problem. Unlike existing classical algorithms, our QUBO formulation targets the global solution to the balanced k-means model. We test our approach on a number of small problems and observe that despite the theoretical benefits of the QUBO formulation, the clustering solution obtained by a modern quantum computer is usually inferior to the solution obtained by the best classical clustering algorithms. Nevertheless, the solutions provided by the quantum computer do exhibit some promising characteristics. We also perform a scalability study to estimate the run time of our approach on large problems using future quantum hardware. Finally, as a final proof of concept, we used the quantum approach to cluster random subsets of the Iris benchmark data set.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Development of an innovative process for post-combustion CO 2 capture to produce high-value NaHCO 3 nanomaterials

An innovative post-combustion process using aqueous sodium glycinates solutions (SGS) for CO 2 capture from a split flue gas stream emitted from the 600 MWe coal power plant, used in the Wolverine Clean Energy Venture (WCEV) project, was developed in Aspen Plus v.10. The flue gas flow rate used in the process was 12.43 kg/s (at 353.15 K and 101.33 kPa) and contained 0.0023 and 13.33 mol% of SO 2 and CO 2 , respectively. The overall process includes 5 main units designed to remove all SO 2 and capture more than 90 mol% of CO 2 in the flue gas stream, while producing high-value, salable sodium bicarbonate (NaHCO 3 ) nanomaterials to offset the total process costs. The hydraulics, mass transfer characteristics, and process performance obtained using Aspen Plus were discussed. Also, the capital expenditure (CAPEX), operating expenditure (OPEX), and Levelized cost of CO 2 capture (LCOC) were calculated to assess the feasibility of this process. Further, the hydraulics in the SO 2 washing and CO 2 capture units showed a pressure drop of 12 and 1 kPa, respectively, and the behaviors of the liquid holdup and normalized packing specific wetted surface area were similar in both units. The gas-side mass transfer coefficients were orders of magnitude greater than the liquid-side mass transfer coefficients. The process was able to capture 2.352 kg/s of CO 2 and produce 4.486 kg/s of valuable NaHCO 3 nanomaterials. Also, the calculated CAPEX, OPEX, and LCOC of the process for a 30-year plant lifetime were ($\$ $4,450,552), (233.00 $\$ $/h) and (35.49 $\$ $/ton of CO 2 captured), respectively.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A strategy for automated core design to increase economic viability and minimize fuel fragmentation, relocation, and dispersal susceptibility in high-burnup cores

The nuclear industry aims to increase the cycle length of pressurized water reactors from 18 to 24 months to increase power plant capacity factors and economic viability. These cycle length extensions will inherently require fuel rods to exceed the current peak rod average burnup limit of 62 GWd/MTU. A chief concern of operating beyond the current burnup limit is the fuel fragmentation, relocation, and dispersal (FFRD) phenomenon in which pulverized fuel fragments can axially relocate and escape through a burst in the cladding formed during a loss-of-coolant accident. In this work, we demonstrate an approach for automating core design employing an optimization tool based on a penalty-free, parallel simulated annealing algorithm to produce pressurized water reactor core designs with two different optimization objectives. The two objectives were to produce core designs with (1) mitigated FFRD susceptibility while achieving 24-month cycle lengths (2) maximum cycle length with no regard for the likelihood of FFRD. Batch size was considered in tandem with both cases to maximize economic viability. The PARCS nodal model was the primary reactor physics tool used in the optimizations and used nuclear cross sections calculated with 2D Polaris lattice physics models. Reactor performance and safety characteristics of the optimized cores were verified using high-fidelity Virtual Environment for Reactor Applications models. The core designs produced by the optimization tool are compared with each other and to a high-burnup core design produced and analyzed in previous works to highlight the fuel management strategies that may enhance high-burnup reactor safety and economic viability. The optimized cores satisfied their respective objective functions, producing a maximum cycle length of 720 effective full-power days in one core design and one that may reduce FFRD susceptibility by up to 50% based on the first-order approximation to FFRD risk formulated in this work. The optimized cores met most constraints but exceeded the hot channel factor limit, especially in FFRD cases where fresh fuel carried more power. Furthermore, this highlights the need for future lattice-level optimizations and broader assembly options.

Cycle length↗
DOE OSTI2020

Nano-Ceramic Cathodes via Co-sputtering of Gd–Ce Alloy and Lanthanum Strontium Cobaltite for Low-Temperature Thin-Film Solid Oxide Fuel Cells

We report the electrochemical performance and structural characteristics of porous nanostructured ceramic cathodes for thin-film solid oxide fuel cells (TF-SOFCs) based on yttria-stabilized zirconia (YSZ) electrolytes. The nanostructured cathode is obtained through magnetron co-sputtering of gadolinium–cerium (Gd–Ce) alloy and lanthanum strontium cobaltite perovskite targets. The resultant nanostructure and composition of the ceramic cathode are controlled by adjusting the co-sputtering conditions. The peak power densities in our fabricated TF-SOFCs are the highest reported values for YSZ-based electrolyte SOFCs, showing 0.14, 0.48, 1.21, 2.56, and 3.01 W/cm2 at 450, 500, 550, 600, and 650 °C, respectively, operating under air and pure hydrogen fuel. The results show that the porosity and composition of the cathode greatly affect the resulting peak power densities. This work illustrates the capability of sputtering to produce stable, scalable, nano-ceramic cathodes with superb peak power densities when integrated in TF-SOFCs.

25 ENERGY STORAGE↗
DOE OSTI2023

Impact of Dopants on Suppressing Gas Evolution from High-Nickel Layered Oxide Cathodes

Gas release from high-Ni layered-oxide cathodes (LiNi x Mn 1-x-y-z Co y Al z O 2 ; x > 0.8) can jeopardize the overall performance and safety characteristics of the cell. A comprehensive assessment of rational cathode design with common dopants, such as Ni, Co, Al, and Mn, to suppress gas evolution is crucial for battery safety, yet it remains to be conducted. Furthermore, we present an in-situ gas analysis on nine high-Ni cathode materials with online electrochemical mass spectrometry (OEMS). We show that regardless of the dopant, reactive oxygen release from the cathode lattice remains a critical process for gas evolution. A series of comparisons reveals that the intensity and onset point of gas release is strongly dependent on the cathode composition. Notably, Al and Mn are the most effective dopants to suppress gas evolution from the cathode at 4.4 V. We further highlight lattice stability limits, across these nine compositions, between 85 % to 93 % state of charge.

25 ENERGY STORAGE↗
DOE OSTI2021

Single-Step Direct Laser Writing of Multimetal Oxygen Evolution Catalysts from Liquid Precursors

We investigate a laser direct-write method to synthesize and deposit metastable, mixed transition metal oxides and evaluate their performance as oxygen evolution reaction catalysts. This laser processing method enabled the rapid synthesis of diverse heterogeneous alloy and oxide catalysts directly from cost-effective solution precursors, including catalysts with a high density of nanocrystalline metal alloy inclusions within an amorphous oxide matrix. The nanoscale heterogenous structures of the synthesized catalysts were consistent with reactive force-field Monte Carlo calculations. By evaluating the impact of varying transition metal oxide composition ratios, we created a stable Fe 0.63 Co 0.19 Ni 0.18 O x /C catalyst with a Tafel slope of 38.23 mV dec -1 and overpotential of 247 mV, a performance similar to that of IrO 2 . Synthesized Fe 0.63 Co 0.19 Ni 0.18 O x /C and Fe 0.14 Co 0.46 Ni 0.40 O x /C catalysts were experimentally compared in terms of catalytic performance and structural characteristics to determine that higher iron content and a less crystalline structure in the secondary matrix decreases the charge transfer resistance and thus is beneficial for electrocatalytic activity. Furthermore, this conclusion is supported by density-functional theory calculations showing distorted active sites in ternary metal catalysts are key for lowering overpotentials for the oxygen evolution reaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Understanding the Impacts of Support–Polymer Interactions on the Dynamics of Poly(ethyleneimine) Confined in Mesoporous SBA-15

Supported amines are a promising class of CO 2 sorbents offering large uptake capacities and fast uptake rates. Additionally, among supported amines, poly(ethyleneimine) (PEI) physically impregnated in the mesopores of SBA-15 silica is widely used. Within these composite materials, the chain dynamics and morphologies of PEI strongly influence the CO 2 capture performance, yet little is known about chain and macromolecule mobility in confined pores. Here, we probe the impact of the support–PEI interactions on the dynamics and structures of PEI at the support interface and the corresponding impact on CO 2 uptake performance, which yields critical structure–property relationships. The pore walls of the support are grafted with organosilanes with different chemical end groups to differentiate interaction modes (spanning from strong attraction to repulsion) between the pore surface and PEI. Combinations of techniques, such as quasi-elastic neutron scattering (QENS), 1 H T 1 –T 2 relaxation correlation solid-state NMR, and molecular dynamics (MD) simulations, are used to comprehensively assess the physical properties of confined PEI. We hypothesized that PEI would have faster dynamics when subjected to less attractive or repulsive interactions. However, we discover that complex interfacial interactions resulted in complex structure–property relationships. Indeed, both the chain conformation of the surface-grafted chains and of the PEI around the surface influenced the chain mobility and CO 2 uptake performance. By coupling knowledge of the dynamics and distributions of PEI with CO 2 sorption performance and other characteristics, we determine that the macroscopic structures of the hybrid materials dictate the first rapid CO 2 uptake, and the rate of CO 2 sorption during the subsequent gradual uptake stage is determined by PEI chain motions that promote diffusive jumps of CO 2 through PEI-packed domains.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Artificial intelligence inferred microstructural properties from voltage–capacity curves

Abstract The quantification of microstructural properties to optimize battery design and performance, to maintain product quality, or to track the degradation of LIBs remains expensive and slow when performed through currently used characterization approaches. In this paper, a convolution neural network-based deep learning approach (CNN) is reported to infer electrode microstructural properties from the inexpensive, easy to measure cell voltage versus capacity data. The developed framework combines two CNN models to balance the bias and variance of the overall predictions. As an example application, the method was demonstrated against porous electrode theory-generated voltage versus capacity plots. For the graphite|LiMn $$_2$$ 2 O $$_4$$ 4 chemistry, each voltage curve was parameterized as a function of the cathode microstructure tortuosity and area density, delivering CNN predictions of Bruggeman’s exponent and shape factor with 0.97 $$R^2$$ R 2 score within 2 s each, enabling to distinguish between different types of particle morphologies, anisotropies, and particle alignments. The developed neural network model can readily accelerate the processing-properties-performance and degradation characteristics of the existing and emerging LIB chemistries.

25 ENERGY STORAGE↗
DOE OSTI2024

Systematic characterization of selenium speciation in coal fly ash

Millions of tons of coal fly ashes (CFAs) are produced annually during coal combustion in the U.S., which are commonly beneficially used in the concrete industry or disposed of in ash ponds. CFAs contain trace amounts of a range of toxic heavy metals including selenium (Se). Because the toxicity of Se is dependent on its speciation, investigating Se speciation in CFAs as affected by coal source and combustion conditions can help understand the related environmental and human health impacts during disposal or beneficial reuse. In this study, a set of representative CFA samples were characterized for Se speciation using synchrotron X-ray absorption spectroscopy (XAS) and micro-X-ray fluorescence spectromicroscopy (μ-XRF/XAS). Se-containing particles were highly heterogeneous, and individual particles might contain multiple oxidation states including Se(0), Se(IV), and Se(VI). Principal component analysis was performed for sample characteristics including Al 2 O 3 , SiO 2 , CaO, FeO, loss on ignition, average particle size, Se concentration, and Se oxidation state. Selective catalytic reduction (SCR), which is used to limit nitrogen oxide (NO x ) emissions during coal combustion, was found to be associated with the presence of reduced Se oxidation states, with up to 90% Se(0) observed in samples with SCR. Alongside SCR, FeO content may also influence Se speciation.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Design and analysis of a ten-turbine floating wind farm with shared mooring lines

This paper discusses the development and analysis of a novel design for a 10-turbine floating wind farm with shared mooring lines. Shared mooring lines tether adjacent floating platforms together, reducing the number of anchors required but increasing system complexity. We present a systematic, multistage design process for shared mooring systems, involving linearized analysis of array layout options, quasi-static mooring line optimization, and design refinement based on coupled dynamic loads analysis. The design developed from this process is thought to represent one of the most advantageous shared-mooring configurations for this scale of floating wind array. It features perpendicular anchor line pairs and allows shared, multiline anchors, making it an example of a shared-mooring-and-anchor array. Comparing the performance and cost characteristics of the shared-mooring design with more conventional three-line individual mooring systems shows equivalent dynamic response characteristics and stationkeeping system cost savings of 25% when using shared mooring lines and shared anchors. The design is also advantageous in the case of a mooring line failure, with offsets and redundancy characteristics similar to four-line individual mooring systems.

17 WIND ENERGY↗
DOE OSTI2020

Design of a 100 kW Surface Permanent Magnet Machine with Wide Constant Power Speed Ratio for Traction Applications

Surface permanent magnet (SPM) machines are considered to be strong candidates for traction applications due to their high power density and high efficiency. By adopting fractional-slot concentrated windings, SPM machines can achieve a wide constant power speed range. A carbon fiber sleeve is applied to ensure the retainment of the magnets at high speed operating conditions. The purpose of this paper is to present the design of a high speed SPM machine for traction applications with 25 kW/Liter active power density and a wide constant-power speed range that is consistent with guidelines and targets presented in the Electrical and Electronics Technical Team Roadmap published by the US Department of Energy. The predicted electromagnetic performance and structural characteristics are presented based on both analytical and FEA results.

42 ENGINEERING↗
DOE OSTI2022

Optimal band selection for target detection with a LWIR multispectral imager

Multispectral imaging can offer many benefits in cost, complexity, resolution, size, weight, and power, relative to hyperspectral imaging. When designing a multispectral system, spectral bandpasses can be selected using optimization algorithms configured to maximally separate target detection scores between target and background regions. A hyperspectral image (HSI) can serve as the source of data from which band groupings can be tested for optimality. The output of an adaptive cosine estimator target detection algorithm is used in an objective function. Three optimization algorithms are compared: particle swarm, dual annealing, and differential evolution. A global optimum is also found using a brute force approach on the Livermore Computing Syrah supercomputer. Three materials are investigated: calcite, gypsum, and limestone. This is done for 3-, 4-, and 5-band systems. The data originate from a longwave infrared HSI of a material display board. The optimization algorithms were run 30 times for every scenario. Performance statistics (maximum, minimum, mean, standard deviation, and median) based on the separation values are given. Additional characterization was performed using receiver operator characteristic (ROC) curves and the area under the ROC curve. While good performance was obtained for the three optimization algorithms, the dual annealing algorithm produced the highest and most consistent detection separation scores on average.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Unravelling electro-chemo-mechanical interplay in layered oxide cathode degradation in solid-state batteries

Solid-state batteries (SSBs) hold notable promise for advancing energy storage technologies. However, their commercial viability is limited by the poor cycle stability and complex degradation mechanism. This study underscores the pivotal role of electro-chemo-mechanical interactions in driving the failure of SSBs. Leveraging advanced x-ray imaging and spectroscopy techniques, we analyzed LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811) cathodes from cycled Li x In||Li 6 PS 5 Cl (LPSC)||NMC811 SSBs, uncovering the interplay between microstructure, chemical heterogeneity, mechanical characteristics, and electrochemical performance. Our results show that revealing electro-chemo-mechanical interactions is essential to develop strategies to suppress the degradation of SSBs. Particularly, we revisit a LiNbO 3 (LNO) coating layer to mitigate electrochemical degradation. The LNO@NMC811 cathode retains 116 milliampere-hours per gram after 200 cycles, showing excellent stability, while the uncoated NMC811 cathode keeps degrading over time, with suppressed chemical heterogeneity and mechanical failure. This work highlights the importance of synergizing advanced material design with coating techniques, ensuring uniform lithium flux and improving mechanical properties to achieve stable, high-performance SSBs.

Zheng, Xueli [SLAC National Accelerator Laboratory↗