Results for “conductive composites” — Page 25

DOE OSTI2021

Alteration of pool boiling heat transfer on metallic surfaces by in situ oxidation

The critical heat flux during pool boiling has been investigated for a range of applications including electrical power generation and thermal management. Reported experimental CHF values during pool boiling of water on flat metallic surfaces, however, show a large discrepancy across studies. Here, we address this discrepancy in CHF values by accounting for oxidation of metallic surfaces during boiling. We studied the effect of in situ oxidation on flat Cu and Ni surfaces by changing the duration that samples were held in saturated water before conducting boiling experiments. The morphology and chemical composition of surfaces after the boiling experiments were analyzed by atomic force microscopy and X-ray photoelectron spectroscopy, respectively. Cu surfaces showed gradually increasing CHF values as the duration in saturated water increased, which could be attributed to the increase in roughness due to the formation of Cu 2 O nanostructures. Conversely, Ni surfaces showed relatively stable CHF and morphology as a nearly flat layer of NiO formed, with one exception: formation of a highly wetting hydroxide, Ni(OH) 2 , on a Ni coupon held in saturated water for 24 h resulted in a uniquely high CHF value, signifying the importance of surface chemistry in addition to morphology. Finally, the fundamental mechanisms resulting in the wide spread of CHF values on metallic surfaces elucidated in this work will lead to more accurate estimation of CHF as well as a deeper mechanistic understanding of CHF values on engineered surfaces.

42 ENGINEERING↗

DOE OSTI2025

Efficient Measurement of Length Distribution of 1D Nanoparticles in Solution via Optical Polarimetry

The efficient measurement of the length distribution of nanotubes, nanowires, and other one-dimensional (1D) nanoparticles in solution is important to enable their incorporation into materials and devices and to optimize their processing for properties of interest, such as thermal/electrical conductivity or mechanical strength, in suspensions and composites. We report an electric-field (E-field)-assisted optical-polarimetry technique to measure the length distribution of ensembles of high-aspect-ratio particles in dilute suspension. The degree of alignment of polarizable 1D particles suspended in a fluid under Brownian motion explicitly depends on the E-field strength and the particle length. We show that it is possible to extract the length distribution of 1D nanoparticles suspended in an insulating fluid by applying a range of E-fields and using optical polarimetry to measure the corresponding alignment order parameter. Notably, the method is relatively insensitive to the diameter of the 1D particles, which can be poorly known or vary within a sample. The technique is validated with silver nanowires and carbon nanotubes of known lengths, as well as polymer-depletion-length-separated single-wall carbon nanotube samples with length distributions independently measured with analytical ultracentrifugation. Finally, we demonstrate the ability of the optical-polarimetry technique to quantify changes in the length distribution of ultranarrow, sub-nanometer-diameter single-wall carbon nanotubes under different types and durations of ultrasonication. Within its range of applicability (polarizable 1D nanoparticles in the 0.5 to 15 μm length range, constrained by the voltage stability of the media and the suspended particles), the E-field-assisted optical-polarimetry method is a particularly efficient and accurate method to measure the length distribution of nanowires and nanotubes in suspension.

1D nanoparticles↗

DOE OSTI2025

Shrub Expansion Can Counteract Carbon Losses From Warming Tundra

Arctic warming is causing substantial compositional, structural, and functional changes in tundra vegetation including shrub and tree-line expansion and densification. However, predicting the carbon trajectories of the changing Arctic is challenging due to interacting feedbacks between vegetation composition and structure, and surface characteristics. We conduct a sensitivity analysis of the current-date to 2100 projected surface energy fluxes, soil carbon pools, and CO 2 fluxes to different shrub expansion rates under future emission scenarios (intermediate—RCP4.5, and high—RCP8.5) using the Arctic-focused configuration of E3SM Land Model (ELM). We focus on Trail Valley Creek (TVC), an upland tundra site in the western Canadian Arctic, which is experiencing shrub densification and expansion. We find that shrub expansion did not significantly alter the modeled surface energy and water budgets. However, the carbon balance was sensitive to shrub expansion, which drove higher rates of carbon sequestration as a consequence of higher shrubification rates. Thus, at low shrub expansion rates, the site would become a carbon source, especially under RCP8.5, due to higher temperatures, which deepen the active layer and enhance soil respiration. At higher shrub expansion rates, TVC would become a net CO 2 sink under both Representative Concentration Pathway scenarios due to higher shrub productivity outweighing temperature-driven respiration increase. Our simulations highlight the effect of shrub expansion on Arctic ecosystem carbon fluxes and stocks. We predict that at TVC, shrubification rate would interact with climate change intensity to determine whether the site would become a carbon sink or source under projected future climate.

Yazbeck, Theresia [The Ohio State Univ., Columbus,↗

DOE OSTI2024

The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity

Non-crystalline materials exhibit unique properties that make them suitable for various applications in science and technology, ranging from optical and electronic devices and solid-state batteries to protective coatings. However, data-driven exploration and design of non-crystalline materials is hampered by the absence of a comprehensive database covering a broad chemical space. In this work, we present the largest computed non-crystalline structure database to date, generated from systematic and accurate ab initio molecular dynamics (AIMD) calculations. We also show how the database can be used in simple machine-learning models to connect properties to composition and structure, here specifically targeting ionic conductivity. These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching non-crystalline structures provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in universal machine learning potentials, impacting design beyond that of non-crystalline materials. In addition, combining diffusion trajectories from our dataset with models that predict liquidus viscosity and melting temperature could be utilized to develop models for predicting glass-forming ability.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Machine learning-accelerated path integral molecular dynamics simulations of reactive organic electrolytes

Hydrogen bonded electrolytes that exhibit accelerated proton transport via sequential reactive hops have drawn interest for their promise in clean energy applications. Molecular dynamics simulations of these electrolytes offer the opportunity to uncover microscopic mechanistic details that could be used to design and tune the properties of candidate electrolyte technologies. However, accurately modeling the proton transfer reactions and transport properties that give rise to high charge conductivites in these electrolytes proves computationally challenging because of the need to perform lengthy condensed phase simulations, treating both the electronic and nuclear degrees of freedom quantum mechanically. In this paper, we demonstrate that such a modeling task can be efficiently achieved with the use of density functional theory (DFT)-trained machine learning potentials (MLP) to accelerate path integral molecular dynamics (PIMD) simulations. We highlight the practical utility of this approach by using it to benchmark how closely PIMD simulations employing different DFT exchange–correlation functionals reproduce the composition-dependent densities, diffusion coefficients, and electrical conductivities of mixtures consisting of imidazole and levulinic acid. Even with the speedup afforded by our MLPs, PIMD simulations remain quite expensive. Furthermore, in order to render PIMD more computationally tractable, we introduce and benchmark the accuracy of a ring polymer contraction approach that leverages a computationally efficient short-range MLP to accelerate our PIMD simulations by an additional factor of four.

Chemical bonding↗

DOE OSTI2023

Community Resilience Indicator Analysis: Commonly Used Indicators from Peer-Reviewed Research (Updated for Research Published 2003-2021)

In 2017, FEMA’s National Integration Center (NIC) Technical Assistance (TA) Branch identified a need to establish a data-driven basis for prioritizing locations for TA investment and guiding local emergency management planning. To achieve this goal, FEMA tasked Argonne National Laboratory (Argonne) with identifying commonly used indicators of community resilience across the landscape of published peer-reviewed research. FEMA and Argonne completed the first Community Resilience Indicator Analysis (CRIA) in 2018 and repeated the process in 2022. The CRIA process begins with a literature review and cataloguing of published peer-reviewed assessment methodologies on social vulnerability and community resilience. The literature review findings are then filtered by inclusion criteria established by the CRIA research team to ensure the methodologies are: (1) Quantitative, (2) Data and methodology are publicly available, (3) Calculated at the county level or lower, (4) Examine generalized hazard risk (rather than a singular hazard), and (5) Focused on pre-disaster community conditions. After this, the research team identifies the commonly used indicators across these methodologies and selects the best data source for each indicator. Finally, the research team bins the data for visual display, conducts a correlation analysis and creates a composite index, the FEMA Community Resilience Index (FEMA CRI). In 2018, the CRIA identified eight resilience and vulnerability assessment methodologies and 20 commonly used indicators (indicators used in three or more of the eight methodologies). The FEMA CRI in 2018 was created from these 20 indicators and was produced for at the county level. The 2022 CRIA updated the literature review to expand the list of methodologies examined and followed the same process, resulting in an analysis of 14 methodologies published between 2003 and 2021 and 22 indicators identified as commonly used (indicators used in five or more of the 14 methodologies). In 2022, the research team produced the FEMA CRI at the county and the census tract levels. To make the CRIA data more accessible and more actionable, each individual indicator and the FEMA CRI is binned and included in FEMA’s Resilience Analysis and Planning Tool (RAPT). RAPT enables emergency managers and community partners to quickly visualize relative differences in potential resilience by county, tribe and census tract. By reviewing the data for each of these 22 indicators individually, emergency managers can gain insights for targeted outreach strategies, planning, mitigation investments and response and recovery operations. Communities, regional governments and others can use this data to better understand potential challenges to resilience. As the social science field of examining and validating indicators of resilience evolves, FEMA will update RAPT to provide emergency managers and community partners with additional data and tools to inform planning, mitigation, response and recovery. It is important to understand that the role of the emergency manager is not to change or to “improve” the data, but to plan appropriately for the community characteristics reflected in the data. These datasets are community characteristics that researchers have identified as important considerations for resilience. For example, people with disabilities may have greater challenges to be resilient to disasters. If a community has a high population of people with disabilities, the emergency manager(s) may need to create tailored preparedness outreach programs and strategies to ensure those residents have support if evacuation is necessary. Rather than label these indicators as an absolute measure of resilience, FEMA considers “potential challenges to resilience” a better frame to understand these indicators. Everyone is vulnerable to disasters. While scholars theorize that certain characteristics may make an individual or a household more socially vulnerable, the data does not reflect measures that individuals and/or communities have taken to address potential challenges, such as emergency management planning and outreach or household preparedness measures. To aid emergency managers in understanding how to use these indicators, calling them potential challenges to resilience supports a more positive and strategic application of the data in all phases of emergency management.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

Site-specific Design Case Study for Wet Waste Hydrothermal Liquefaction and Biocrude Upgrading to Hydrocarbon Fuels

Hydrothermal liquefaction (HTL) is a thermal process that converts wet biomass to renewable hydrocarbon fuel blendstocks (i.e., renewable naphtha, renewable diesel, and sustainable aviation fuel (SAF)). It can utilize a wide range of pure and blended wet feedstocks, including sewage sludge from water resource recovery facilities (WRRF), food and agriculture wastes, algae, fats, oils and greases (FOG) and blends of dry and wet wastes/feedstocks. Historically, techno-economic analysis (TEA) and annual state of technology (SOT) assessments with standard economic assumptions used by the Bioenergy Technologies Office (BETO) were conducted for the wet waste HTL pathway leveraging experimental data collected from Pacific Northwest National Laboratory’s (PNNL) continuous flow reactor systems. The objective of the SOT assessment has been to guide and track progress of BETO’s HTL research and development (R&D) toward reduced cost and greenhouse gas (GHG) emissions for the pathway. However, gaps exist between BETO’s traditional SOT updates and the needs of key external stakeholders that – if addressed – will accelerate technology adoption. This Business Case Study aims to bridge this gap by providing an updated design, TEA, and LCA based on PNNL’s FY23 R&D with added analyses and information that provide enhanced relevance for stakeholders of the HTL technology. This includes specific siting, regional wet waste resource inventory and transportation cost analyses, fuel market information, sustainable fuel policy impacts, economic metrics of net present value (NPV) and internal rate of return (IRR), greenhouse gas (GHG) emissions analysis, and statistical analysis of cost and technical uncertainties of the HTL plant design. The study focuses on the “Detroit combined statistical area (CSA)” region for siting of a wet waste HTL plant adjacent to the Great Lakes Water Authority (GLWA) facility with guidance from industry participants. Regional resource and siting analyses were conducted to identify feedstock availability, scale, and cost, as well as a beneficial site location. TEA with detailed rigorous capital cost estimation for the specific site application was conducted to evaluate the key economic metrics of most value to industrial partners. These include total capital investment, operating costs, minimum fuel selling price (MFSP) of the biocrude and fuel blendstock, and NPV and internal rate of return IRR with sustainable fuel credits. Life cycle analysis was conducted to evaluate the supply chain greenhouse gas (GHG) emissions for the wet waste HTL process as compared with petroleum derived diesel. This study is also informed by years of R&D and process de-risking learnings and was conducted with a basic engineering HTL plant design and costing that akin to a “first-of-a-kind” plant economics. This differs from our conventional “nth plant ” SOT assessments. Specifically, the HTL process model has been updated with more operationally reliable methods for feed heating and phase separations. Further, we have implemented additional spare equipment for redundancy, a more rigorous installed equipment cost estimation approach, and additional costs associated with feed formatting and delivery, building, piping and site development. An Excel-based cost sheet based on the basic engineering design is also released alongside the report that allows users to conduct customized TEA with their own feed composition and financial assumptions.

09 BIOMASS FUELS↗

DOE OSTI2026

Optimization of boron carbide density using direct current sintering for use as sputtering sources at the National Ignition Facility

Several experimental trials were conducted to determine optimal sintering parameters and compositions for purchased boron carbide powder using spark plasma sintering/direct current sintering. Sputtering targets used to create ablators for the National Ignition Facility have been unable to survive post processing procedures suggesting that in-house fabrication may yield better results due to extended control.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Supporting data for Site-specific Design Case Study for Wet Waste Hydrothermal Liquefaction and Biocrude Upgrading to Hydrocarbon Fuels

Hydrothermal liquefaction (HTL) is a thermal process that converts wet biomass to renewable hydrocarbon fuel blendstocks (i.e., renewable naphtha, renewable diesel, and sustainable aviation fuel (SAF)). It can utilize a wide range of pure and blended wet feedstocks, including sewage sludge from water resource recovery facilities (WRRF), food and agriculture wastes, algae, fats, oils and greases (FOG) and blends of dry and wet wastes/feedstocks. Historically, techno-economic analysis (TEA) and annual state of technology (SOT) assessments with standard economic assumptions used by the Bioenergy Technologies Office (BETO) were conducted for the wet waste HTL pathway leveraging experimental data collected from Pacific Northwest National Laboratory’s (PNNL) continuous flow reactor systems. The objective of the SOT assessment has been to guide and track progress of BETO’s HTL research and development (R&D) toward reduced cost and greenhouse gas (GHG) emissions for the pathway. However, gaps exist between BETO’s traditional SOT updates and the needs of key external stakeholders that – if addressed – will accelerate technology adoption. This Business Case Study aims to bridge this gap by providing an updated design, TEA, and LCA based on PNNL’s FY23 R&D with added analyses and information that provide enhanced relevance for stakeholders of the HTL technology. This includes specific siting, regional wet waste resource inventory and transportation cost analyses, fuel market information, sustainable fuel policy impacts, economic metrics of net present value (NPV) and internal rate of return (IRR), greenhouse gas (GHG) emissions analysis, and statistical analysis of cost and technical uncertainties of the HTL plant design. The study focuses on the “Detroit combined statistical area (CSA)” region for siting of a wet waste HTL plant adjacent to the Great Lakes Water Authority (GLWA) facility with guidance from industry participants. Regional resource and siting analyses were conducted to identify feedstock availability, scale, and cost, as well as a beneficial site location. TEA with detailed rigorous capital cost estimation for the specific site application was conducted to evaluate the key economic metrics of most value to industrial partners. These include total capital investment, operating costs, minimum fuel selling price (MFSP) of the biocrude and fuel blendstock, and NPV and internal rate of return IRR with sustainable fuel credits. Life cycle analysis was conducted to evaluate the supply chain greenhouse gas (GHG) emissions for the wet waste HTL process as compared with petroleum derived diesel. This study is also informed by years of R&D and process de-risking learnings and was conducted with a basic engineering HTL plant design and costing that akin to a “first-of-a-kind” plant economics. This differs from our conventional “nth plant ” SOT assessments. Specifically, the HTL process model has been updated with more operationally reliable methods for feed heating and phase separations. Further, we have implemented additional spare equipment for redundancy, a more rigorous installed equipment cost estimation approach, and additional costs associated with feed formatting and delivery, building, piping and site development. An Excel-based cost sheet based on the basic engineering design is also released alongside the report that allows users to conduct customized TEA with their own feed composition and financial assumptions.

Li, Shuyun↗

DOE OSTI2021

Lithiated and passivated lithium ion battery anodes

An electrode material includes a lithium active material composition. The lithium active material composition includes lithium and an active anode material. The lithium active material composition is coated with a lithium ion conducting passivating material, such that the electrode material is lithiated and pre-passivated. An electrode and a battery are also disclosed. Methods of making an electrode material, electrode and battery that are lithiated and pre-passivated are also disclosed.

Sacci, Robert L.↗

DOE OSTI2025

Effect of moisture ingression on material performance and particle generation in molten salts

This report summarizes activities conducted to understand and mitigate the negative consequences that moisture ingressions have upon the operations of pyroprocessing equipment. The objectives of the work conducted in FY25 were to characterize (1) the physicochemical properties of particles generated as a result of moisture ingressions, and (2) the corrosion of relevant structural materials with the introduction of moisture. Compositional and morphological analyses of the generated particles were conducted through a suite of characterization technologies, including X-ray diffraction, microscopy, and particle size analysis. The results gathered this year were compared to those obtained in FY24 when studies with oxygen ingressions were conducted on CeCl 3 -LiCl-KCl and UCl 3 -LiCl-KCl systems. Investigations involving performance evaluations of industrially relevant structural alloys were also conducted under moisture ingress conditions. In-line electrochemical monitoring of the bulk salt was augmented with microscopy of alloy samples to quantify the concentration and accumulation of corrosion products in the bulk salt. The results reported in this work provide a more holistic understanding of the effects of atmospheric ingressions on molten chloride salt chemistry such that effective redox control strategies may be implemented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Fabrication of nanoporous doped metal oxide coatings via selective infiltration of a block copolymer template: The case of Al-doped zinc oxide

Conductive nanoporous conformal coatings are important for various applications including touchscreens, displays, and electrochromic windows, though design of such coating with controlled porosity and composition remains challenging. We propose a sequential infiltration synthesis (SIS) method for the fabrication of nanoporous conductive aluminum-doped zinc oxide (AZO) films by infiltrating block copolymer (BCP) templates, specifically polystyrene-block-polyvinyl pyridine (PS-b-P4VP), with diethyl zinc and trimethyl aluminum precursors. Tunability of both porosity and electrical conductivity is achieved by precisely controlling the swelling behavior of the polymer templates and adjusting the number of SIS cycles. Further, our results reveal that porosity levels can reach up to 80 %, with resistivity as low as ~7.83 Ωcm. Using conductive atomic force microscopy (C-AFM) and Hall effect measurements, we show that lower concentrations of aluminum doping relative to zinc (around 1: 17) results in higher conductivity of the AZO films. Additionally, AZO is based on zinc oxide (ZnO) which is cost-efficient, abundant and less toxic than commonly used transparent conductive oxides such as indium tin oxide (ITO) and fluorine-doped tin oxide (FTO). Therefore, this novel approach opens new possibilities for creating highly porous, conformal, conductive AZO coatings with customizable porosity and composition, making them ideal candidates for diverse optoelectronic applications.

36 MATERIALS SCIENCE↗

DOE OSTI2023

TRACER-CAT-UCDAVIS Aerosol Optical Properties and Sizing - smps

Aerosol size distribution and optical properties collected in La Port TX, during TRACER-CAT-UCDavis, July 2022. Speciated particle composition measurements are reported separately. All sampling was conducted with an identical aerosol inlet as the standard ARM-AOS systems. The TRacking Aerosol Convection interactions ExpeRiment Carbonaceous Aerosols Thrust by UC Davis (TRACER-CAT-UCDavis, July 2022) was designed to complement the larger ARM TRACER science goals to understand convective cloud lifecycles and aerosol-convection interactions. TRACER-CAT-UCDavis is focused on understanding the relationship between particle composition and light absorption and the influence of water uptake on this relationship. This aerosol data can be used to understand the relationship between particle composition and light absorption and the influence of water uptake on this relationship. Instruments include: UC Davis two-wavelength cavity ringdown-photoacoustic spectrometer (CRD-PAS); Brechtel scanning electrical mobility sizer (SEMS); modified humidified cavity attenuated phase shift spectrometer with single scatter albedo (H-CAPS-SSA)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

TRACER-CAT-UCDAVIS Aerosol Optical Properties and Sizing - crdps

Aerosol size distribution and optical properties collected in La Port TX, during TRACER-CAT-UCDavis, July 2022. Speciated particle composition measurements are reported separately. All sampling was conducted with an identical aerosol inlet as the standard ARM-AOS systems. The TRacking Aerosol Convection interactions ExpeRiment Carbonaceous Aerosols Thrust by UC Davis (TRACER-CAT-UCDavis, July 2022) was designed to complement the larger ARM TRACER science goals to understand convective cloud lifecycles and aerosol-convection interactions. TRACER-CAT-UCDavis is focused on understanding the relationship between particle composition and light absorption and the influence of water uptake on this relationship. This aerosol data can be used to understand the relationship between particle composition and light absorption and the influence of water uptake on this relationship. Instruments include: UC Davis two-wavelength cavity ringdown-photoacoustic spectrometer (CRD-PAS); Brechtel scanning electrical mobility sizer (SEMS); modified humidified cavity attenuated phase shift spectrometer with single scatter albedo (H-CAPS-SSA)

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Nanoscale Ion Transport Enhances Conductivity in Solid Polymer-Ceramic Lithium Electrolytes

The predictive design of flexible and solvent-free polymer electrolytes for solid-state batteries requires an understanding of the fundamental principles governing the ion transport. In this work, we establish a correlation among the composite structures, polymer segmental dynamics, and lithium ion (Li + ) transport in a ceramic-polymer composite. Elucidating this structure–property relationship will allow tailoring of the Li + conductivity by optimizing the macroscopic electrochemical stability of the electrolyte. The ion dissociation from the slow polymer segmental dynamics was found to be enhanced by controlling the morphology and functionality of the polymer/ceramic interface. The chemical structure of the Li + salt in the composite electrolyte was correlated with the size of the ionic cluster domains, the conductivity mechanism, and the electrochemical stability of the electrolyte. Polyethylene oxide (PEO) filled with lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) or lithium bis(fluorosulfonyl) imide (LiFSI) salts was used as a matrix. A garnet electrolyte, aluminum substituted lithium lanthanum zirconium oxide (Al-LLZO) with a planar geometry, was used for the ceramic nanoparticle moieties. Further, the dynamics of the strongly bound and highly mobile Li + were investigated using dielectric relaxation spectroscopy. The incorporation of the Al-LLZO platelets increased the number density of more mobile Li + . The structure of the nanoscale ion-agglomeration was investigated by small-angle X-ray scattering, while molecular dynamics (MD) simulation studies were conducted to obtain the fundamental mechanism of the decorrelation of the Li + in the LiTFSI and LiFSI salts from the long PEO chain.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Irreversible Multielement Diffusion and the Resulting Compositional and Processing Flexibility in the Synthesis and Densification of Lithium Aluminum Lanthanum Zirconium Oxide

Despite the broad interest in Al-doped lithium lanthanum zirconium oxide (LLZO), the wide range of reported ionic conductivities suggests that the effect of processing parameters on the resulting phase purity and bulk conductivity is not well-understood. Here, in this work, we synthesized six separate series of LLZO with variations in Al concentration, Li excess, Li addition order, Li source, densification method, and mother powder to determine the effect on the composition, phase purity, density, and bulk conductivity. We found that a wider range of compositions (6.08–7.61 mol of Li and 0.06–0.23 mol of Al) than previously reported can result in cubic phase stability and that nearly all elements (Li, Al, Zr, and La) are lost to the MgO crucible during calcination and sintering. The manner in which different elements are lost is affected by the processing parameters. We observed that Al-doped LLZO shows great compositional flexibility in stabilizing the cubic phase, offering an explanation for the range of electrochemical performance metrics reported in the literature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Interconnected cathode-electrolyte double-layer enabling continuous Li-ion conduction throughout solid-state Li-S battery

All-solid-state lithium (Li) batteries with high energy density are a promising solution for the next-generation energy storage systems in large-scale devices. To simultaneously overcome the challenges of poor ionic conduction of solid electrolytes and shuttling of active materials, here we introduce a functional electrolyte-cathode bilayer framework with interconnected LLAZO channels from the electrolyte into the cathode for advanced solid-state Li-S batteries. Differing from the traditional solid-state batteries with separated layer compositions, the introduced bilayer framework provides ultrafast and continuous ion/electron conduction. Instead of transferring Li+ across the polymer and garnet phases which involve huge interfacial resistance, Li+ is directly conducted through the LLAZO channels created continuously from the cathode layer to the solid electrolyte layer, significantly shortening the diffusion distance and facilitating the redox reaction of sulfur and sulfides. A stable cycle life is demonstrated in the prototype Li-S solid-state batteries assembled with the introduced LLAZO-LLAZO@CNF interconnected bilayer framework. High capacity is obtained at room temperature, indicating the superior electrochemical properties of the bilayer framework that result from the unique design of the interconnected LLAZO garnet phase.

25 ENERGY STORAGE↗

DOE OSTI2021

Rate-dependent strength and deformation heterogeneity of B4C-reinforced Al composite: Time-resolved imaging with synchrotron X-rays

Dynamic (up to 5500 s -1 ) and quasi-static compression tests are conducted on a 15 wt% B4C particle-reinforced Al (B 4 C/Al) composite. In situ, high-speed synchrotron X-ray phase contrast imaging and digital image correlation are employed to map mesoscale deformation fields at μm and μs scales. The bulk stress–strain curves show significant strain and strain-rate hardening under dynamic compression. The strain-rate sensitivity exponent is an order of magnitude higher at high strain rates (> 10 3 s -1 ) than that at low strain rates (< 10 -2 s -1 ). Strain field mapping demonstrates distinct compressive strain localizations for both quasi-static and dynamic loading. Nevertheless, compressive strain localizations appear denser in spacing under dynamic loading, owing to spontaneous dislocation nucleation in both weak and strong zones. This results in a higher density of geometrically necessary dislocations, which contributes to the higher strain and strain-rate hardening of B 4 C/Al under dynamic loading. Additionally, the ratio of the maximum local strain to the bulk average is 1.5, and the local strain-rate enhancement cannot explain the increased rate sensitivity of B 4 C/Al under dynamic loading. Therefore, the rate-dependent deformation heterogeneity dominates the strain-rate hardening of B 4 C/Al. Postmortem analyses help correlate deformation features to particle- and grain-scale microstructures, yielding consistent results with mesoscale strain fields.

36 MATERIALS SCIENCE↗