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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Electrically-driven IMT and volatile memristor behavior in NdNiO 3 films

Abstract Transition metal oxides with insulator-metal transitions (IMTs) are uniquely suited for volatile memristor devices that mimic the spiking of biological neurons. Unlike most non-volatile memristors, which often operate via ion migration into filaments, volatile devices utilize a reversible phase change that returns to a ground state in the absence of applied stimulus. In these devices, Joule heating triggers the IMT and changes the bulk resistivity rather than influencing conduction through defects, as in previous studies. This volatile resistive switching behavior has previous been leveraged in niobium and vanadium oxides, but not in rare-earth nickelates, despite their tunable transition temperatures. This study demonstrates an electrically driven IMT in the prototypical rare-earth nickelate, NdNiO 3 , in large area devices. While previous work examining the electrically-driven IMT in NdNiO 3 suggests defect-dominated conduction, this study shows clear s-type negative differential resistance (NDR) consistent with temperature-dependent resistivity measurements. The NDR peak-to-valley voltage scales linearly with temperature as expected for conductivity pathways dominated by bulk IMT behavior. Unlike other transition metal oxides, which are modeled using the insulator-metal phase fraction as the internal state variable, a thermoelectric model with temperature as the internal state variable is found to more accurately describe the current–voltage characteristic of NdNiO 3 volatile memristors. Overall, we report the synthesis, fabrication, and characterization of NdNiO 3 volatile memristors with resistivity dominated by bulk-like IMT behavior which is scalable and not dependent upon oxygen vacancy migration or defect mediated conduction pathways.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Comparison of machine learning and electrical resistivity arrays to inverse modeling for locating and characterizing subsurface targets

Here, this study evaluates the performance of multiple machine learning (ML) algorithms and electrical resistivity (ER) arrays for inversion with comparison to a conventional Gauss-Newton numerical inversion method. Four different ML models and four arrays were used for the estimation of only six variables for locating and characterizing hypothetical subsurface targets. The combination of dipole-dipole with Multilayer Perceptron Neural Network (MLP-NN) had the highest accuracy. Evaluation showed that both MLP-NN and Gauss-Newton methods performed well for estimating the matrix resistivity while target resistivity accuracy was lower, and MLP-NN produced sharper contrast at target boundaries for the field and hypothetical data. Both methods exhibited comparable target characterization performance, whereas MLP-NN had increased accuracy compared to Gauss-Newton in prediction of target width and height, which was attributed to numerical smoothing present in the Gauss-Newton approach. MLP-NN was also applied to a field dataset acquired at U.S. DOE Hanford site.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

An Interface–Type Memristive Device for Artificial Synapse and Neuromorphic Computing

Interface-type (IT) metal/oxide Schottky memristive devices have attracted considerable attention over filament-type (FT) devices for neuromorphic computing because of their uniform, filament-free, and analog resistive switching (RS) characteristics. The most recent IT devices are based on oxygen ions and vacancies movement to alter interfacial Schottky barrier parameters and thereby control RS properties. However, the reliability and stability of these devices have been significantly affected by the undesired diffusion of ionic species. Herein, a reliable interface-dominated memristive device is demonstrated using a simple Au/Nb-doped SrTiO 3 (Nb:STO) Schottky structure. The Au/Nb:STO Schottky barrier modulation by charge trapping and detrapping is responsible for the analog resistive switching characteristics. Because of its interface-controlled RS, the proposed device shows low device-to-device, cell-to-cell, and cycle-to-cycle variability while maintaining high repeatability and stability during endurance and retention tests. Furthermore, the Au/Nb:STO IT memristive device exhibits versatile synaptic functions with an excellent uniformity, programmability, and reliability. A simulated artificial neural network with Au/Nb:STO synapses achieves a high recognition accuracy of 94.72% for large digit recognition from MNIST database. These results suggest that IT resistive switching can be potentially used for artificial synapses to build next-generation neuromorphic computing.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Geophysical methods reveal the soil architecture and subsurface stratigraphic heterogeneities across land-lake interfaces along Lake Erie

Abstract Purpose The land-lake interface is a unique zone where terrestrial and aquatic ecosystems meet, forming part of the Earth’s most geochemically and biologically active zones. The unique characteristics of this interface are yet to be properly understood due to the inherently high spatiotemporal variability of subsurface properties, which are difficult to capture with the traditional soil sampling methods. Geophysical methods offer non-invasive techniques to capture variabilities in soil properties at a high resolution across various spatiotemporal scales. Methods We combined electromagnetic induction (EMI), electrical resistivity tomography (ERT), and ground penetrating radar (GPR) with data from soil cores and in situ sensors to investigate hydrostratigraphic heterogeneities across land-lake interfaces along the western basin of Lake Erie. Results EMI revealed high spatial heterogeneities in ECa distribution across the land-lake interfaces, with higher values in the wetland and transition zones compared to the upland zone. Soil ECa maps matched soil maps from a public database with the hydric soil units delineated as high conductivity zones (ECa > 40 mS/m). ERT and GPR showed vertical variation in soil properties with clear stratigraphic boundaries, and correlation of ERT profiles with lithologs from piezometers revealed the stratigraphic units of silt–clay and till sequence down to 3.5 m depth which are consistent with the surficial geology of the study area. Conclusions These results validate the use of multiple geophysical methods for extrapolating soil properties and mapping stratigraphic structures at land-lake interfaces, thereby providing the missing information required to improve the earth system model (ESM) of coastal interfaces.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Adaptive Discovery and Mixed-Variable Optimization of Next Generation Synthesizable Microelectronic Materials

Design of new microelectronic materials is characterized by several challenges such as high-dimensionality of the atomic structure-composition variable space, formidable cost of directly using high-fidelity simulations for design optimization, dispersity in literature-reported similar materials and synthesis methods, complex physical mechanisms, and mixed qualitative and quantitative design variables that lead to a disjointed design space. Even though machine learning (ML) techniques have been employed to expedite materials innovation, existing methods treat ML and design optimization as two separate processes, failing to resolve the fundamental challenges associated with high dimensionality and mixed-variable complexity. We have developed a ML enhanced mixed-variable material design optimization framework to efficiently extract useful information from existing data in literature and physics-based simulations to guide the autonomous search for optimal materials. Our proposed framework is composed of four computational modules: (1) a natural language processing (NLP) based virtual screening module, (2) classification based concept exploration module, (3) a density functional theory (DFT)-based high-fidelity evaluation model, and (4) a novel latent-variable Gaussian process (LVGP) ML model for mixed-variable problems with uncertainty quantification, which seamlessly integrates with Bayesian Optimization (BO) and achieves superb efficiency through embedded physics-based dimension reduction. Our approach is demonstrated and validated using the testbed of functional materials exhibiting metal-insulation transitions (MITs), with the targeted reversible resistivity changes (∼10^5) near room temperature. At the end of the 30-month project, we have developed a series of new ML techniques using NLP, conditional variational autoencoders, active learning, latent-variable Gaussian processes, integrated with Bayesian optimization. Our project has resulted in new predicted MITs compounds and improved understanding of MITs microscopic mechanisms, which in turn will revolutionize microelectronics science to provide energy-saving solutions. Our research has improved both creativity and efficiency in transforming rare-event discoveries of new functional materials to persistent innovations. In addition to open-sourcing the online MIT database and the classification model, the LVGP open source code has been downloaded more than 15,000 times within two years. More than 40 MIT compounds have been identified and many have been pursued experimentally via collaborators. The research results are published in close to 20 collaborative papers in high-impact journals, such as Chem. Mater., Appl. Phys. Rev., Sci. Rep., among others of design space.

36 MATERIALS SCIENCE↗
DOE OSTI2024

End-Use Savings Shapes Measure Documentation: Variable Refrigerant Flow With 25% Upsizing Allowance for Heating

This documentation focuses on a single end-use savings shape upgrade - a variable refrigerant flow with heat recovery (VRF HR) heating and cooling system coupled with a dedicated outdoor air system (DOAS) for ventilation and where 25% upsizing (or 125% of the original size) is allowed for heating in colder climates (i.e., heating dominant regions). This document will primarily discuss the additional changes to the sizing algorithm and modeling approach, while a comprehensive overview of the fundamental modeling methodology and background of the VRF HR DOAS upgrade, including applicability and other key assumptions can be found in the original documentation: Variable Refrigerant Flow with Heat Recovery and Dedicated Outdoor Air System. To provide more context on the 25% upsizing algorithm, if the building is a cooling dominant (e.g., design cooling load higher than design heating load ), then the outdoor unit capacity of the VRF heat pump is sized based on the design cooling load. However, if the building is heating dominant, then this building is a candidate for the 25% upsizing allowance. Once the 25% upsizing is allowed, if the 25% upsized capacity (or 125% from the original size) represented with design condition exceeds the design heating load, then the design heating load is used for calculating the rated capacity of the outdoor unit. And if the 25% upsized capacity represented with the design condition does not exceed the design heating load, then the 25% upsized capacity represented with rated condition is used for the capacity of the outdoor unit while the remaining heating load is handled with the supplemental/backup electric resistance coil.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Chemically Amplified, Dry-Develop Poly(aldehyde) Photoresist

The catalytic decomposition of poly(phthalaldehyde) with a photoacid generator can be used as dry-develop photoresist, where the exposed film depolymerizes into small molecules to allow the development of features via controlled vaporization. Higher temperatures enabled shorter dry-development times, but also promoted faster photoacid diffusion that compromised pattern fidelity. Trihexylamine was used as a base quencher to counteract acid diffusion in a phthalaldehyde-propanal co-polymer photoresist. The propanal co-monomer in the polymer improves the vaporization rate because it has a higher vapor pressure than phthalaldehyde. Addition of the base quencher was found to improve the contrast, pattern fidelity, and ease-of-handling of the dry-develop resist in a direct-write UV lithography tool. The dry-development of 4 μ m features was achieved with no appreciable residue. For large area features, a spatially variable exposure method was used to direct the residue away from the exposed area. The gradient exposure method was used to produce 100 μ m features. Plasma etching after dry-development was also used to achieve residue-free dry-developed patterns. These results show the benefits of incorporating base additives into a dry-develop depolymerizable resist system and highlight the need for addressing residue formation.

Materials Science↗
DOE OSTI2023

Predicting oxidation damage of ultra high-temperature carbide ceramics in extreme environments using machine learning

Determining the oxidation resistance of UHTC carbides in extreme environments is challenging theoretically and experimentally due to the high dimensional complexity of influencing variables and intricate testing setups. Herein we demonstrate the use of machine learning (ML) models trained with experimental literature data to predict the oxide thickness of UHTC carbides exposed to air based on composition, mean grain size, relative densification, holding time, and temperature. A multi-dimensional database with 76 occurrences is created containing experimental results of Hf, Zr, and Ta carbides plus additives. In this study, the preprocessed database is then used to train ML models to predict their oxidation behavior. The trained model predicts the oxidation damage in the form of an average oxide thickness in UHTC carbides with a Mean Absolute Error (MAE) of ±65.45 μm for samples in the testing set that developed thicknesses up to 1000 μm. The model successfully predicted oxidation damage for a recession rate lower than 60 μm/min. It is noticed that the ensemble method MAE is increased to ±134.34 μm while forecasting the oxidation of samples with a recession rate higher than the threshold. The unprecedented approach is a novel way to predict the damage through the oxidation of carbide compounds before processing for a smarter design with room for improvement.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Milone eTape Liquid Level Sensor Laboratory Calibration with a Commercial 12.2 cm HS-Flume and 3D Printed 4 cm Micro-HS-Flumes

The NGEE Arctic Rainfall Simulator (NARS) is a variable intensity rainfall simulator (RFS) with a frame design based on the Humphry et al. (2002) RFS and a water delivery system based on the Walnut Gulch (Paige et al., 2004) RFS. The NARS uses an aluminum frame that is fully deconstructable for transportation to field locations and a water system that enables variable rain intensity. Prior to field deployment, H-flume discharge and the corresponding Milone eTape Liquid Level Sensor (eTape) resistance values were measured in the laboratory so that discharge measurements from the NGEE Arctic Rainfall Simulator could be automated. eTape resistance was measured with increasing fluid heights for the full range of the eTape to calculate sensor detection limits and resolution. The eTape was calibrated to both a commercially available 12.2 cm HS-flume and a 3D printed 4 cm Micro-HS-flume. This data package contains two .csv files, one for the eTape calibration and the other for the flume calibrations, and two .stl files to 3D print the 4 cm Micro-HS-Flume design with a 1 cm x 3.6 cm stilling well opening. The .stl files can be opened in most 3D printing or CAD programs/software. Two .kml files of the laboratory location and broader area where testing was conducted are also included in this data package.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Anodic Interfacial Evolution in Extremely Fast Charged Lithium-Ion Batteries

Interfacial reaction mechanisms at the anode/separator interface play a central role in the performance and safety of lithium-ion batteries during fast charging. We report a mechanistic study on the evolution and interactions of the aging mechanisms at the anode/separator interface in lithium cobalt oxide/graphite pouch cells charged with variable charging rates (1–6C) over 10 cycles. In situ electrochemical measurements, including voltage relaxation, Coulombic efficiency, and direct current internal resistance, indicated an incremental lithium loss until the C rates were ≤5C. A substantial capacity fade is observed in the first few cycles of fast charging, but the magnitude of capacity fade progressively diminishes with the number of cycles, indicating a suppression in the lithium deposition mechanism. Post-mortem film thickness, X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM) analyses were performed to elucidate the evolution of electrolyte decomposition, the solid–electrolyte interface (SEI), lithium plating, and film fracture mechanisms with C rate. XPS measurements confirmed an increasing lithium concentration in an SEI film with an increase in the C rate. SEM images showed a growth of dendritic lithium on the anode surface from 1C to 3C. Precrack formation leading to an interfacial film fracture was observed at higher C rates. Furthermore, a differential analysis of the discharge capacity indicated a possible two-phase delithiation from the anode and reduced cathodic lithiation due to lithium loss at high C rates.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A Facile Aqueous Solution Route for the Growth of Chalcogenide Perovskite BaZrS 3 Films

The prototypical chalcogenide perovskite, BaZrS 3 (BZS), with its direct bandgap of 1.7–1.8 eV, high chemical stability, and strong light–matter interactions, has garnered significant interest over the past few years. So far, attempts to grow BaZrS 3 films have been limited mainly to physical vapor deposition techniques. Here, we report the fabrication of BZS thin films via a facile aqueous solution route of polymer-assisted deposition (PAD), where the polymer-chelated cation precursor films were sulfurized in a mixed CS 2 and Ar atmosphere. The formation of a single-phase polycrystalline BZS thin film at a processing temperature of 900 °C was confirmed by X-ray diffraction and Raman spectroscopy. The stoichiometry of the films was verified by Rutherford Backscattering spectrometry and energy-dispersive X-ray spectroscopy. The BZS films showed a photoluminescence peak at around 1.8 eV and exhibited a photogenerated current under light illumination at a wavelength of 530 nm. Temperature-dependent resistivity analysis revealed that the conduction of BaZrS 3 films under the dark condition could be described by the Efros–Shklovskii variable range hopping model in the temperature range of 60–300 K, with an activation energy of about 44 meV.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Soil architecture and subsurface stratigraphic heterogeneities across land-lake interfaces along Lake Erie.

Uploaded raw datasets from electrical resistivity tomography (ERT), ground penetrating radar (GPR), electromagnetic induction (EMI) and insitu sensors were acquired across land-lake interfaces along Lake Erie. These datasets were useful to delineate the soil architecture and subsurface stratigraphic heterogeneities. We combined multiple geophysical methods with lithostratigraphic logs from borehole cores and data from soil and borehole sensors to investigate spatial heterogeneities across land-lake interfaces. The apparent electrical conductivity (Eca) maps matched closely with the soil maps from public database and also detected additional soil units that were missing in the traditional soil maps. This implies that electromagnetic induction (EMI) could be relied upon for non invasive characterization of soils in sampling restricted sites where only non-invasive measurements are feasible. Results from electrical resistivity tomography (ERT) and Ground penetrating radar (GPR) are consistent with the surficial geology of the study area, and revealed that the vertical variabilities at the sites are mainly driven by stratigraphy.The ERT data (.stg) were acquired from three different sites portage river (PTR), crane creek (CRC) and old woman creek (OWC). The ".stg" files can be opened with ERT inversion software such as EarthImager, ResIpy or Res2DInv. The GPR data (.GPZ) were also acquired from the three sites, and the ".GPZ" data files can be opened with EKKO_project 5 software from Sensors & Software Inc., or with any other standard GPR software. The EM data (.csv) were acquired from the three sites while the soil sensor data (.csv) here were acquired only from crane creek site. The ".csv) files can be opened with MS Excel or any other spread sheet software.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Harnessing Quantum Capacitance in 2D Material/Molecular Layer Junctions for Novel Electronic Device Functionality

Two-dimensional (2D) materials promise advances in electronic devices beyond Moore’s scaling law through extended functionality, such as non-monotonic dependence of device parameters on input parameters. However, the robustness and performance of effects like negative differential resistance (NDR) and anti-ambipolar behavior have been limited in scale and robustness by relying on atomic defects and complex heterojunctions. In this paper, we introduce a novel device concept that utilizes the quantum capacitance of junctions between 2D materials and molecular layers. We realized a variable capacitance 2D molecular junction (vc2Dmj) diode through the scalable integration of graphene and single layers of stearic acid. The vc2Dmj exhibits NDR with a substantial peak-to-valley ratio even at room temperature and an active negative resistance region. The origin of this unique behavior was identified through thermoelectric measurements and ab initio calculations to be a hybridization effect between graphene and the molecular layer. The enhancement of device parameters through morphology optimization highlights the potential of our approach toward new functionalities that advance the landscape of future electronics.

2D materials↗
DOE OSTI2025

Dielectric-Engineered Monolayer MoS 2 Memtransistors for Brain-Inspired Computing with High Recognition Accuracy

Two-dimensional transition metal dichalcogenides (2D-TMDs)-based memtransistors have emerged as promising candidates for neuromorphic hardware due to their exceptional ability to emulate synaptic behavior. However, many existing 2D-TMDs memtransistors rely on polycrystalline channels with grain boundaries or defects introduced through postgrowth treatments, raising concerns about material integrity and the preservation of intrinsic properties. Here, in this work, we demonstrate a monocrystalline monolayer MoS 2 memtransistor fabricated on a silicon nitride (SiN X ) substrate, achieving a large resistive switching ratio of 10 4 , a dynamic range exceeding 90, along with highly linear and symmetric weight updates, minimal cycle-to-cycle variability, and low device-to-device variability. These attributes are critical for enabling high-performance neuromorphic hardware. Based on experimental data, we further show that these artificial synapses enable a recognition accuracy of more than 97% on the MNIST handwritten digits data set. Our findings present a straightforward approach to realizing 2D-TMDs memtransistors through dielectric engineering, offering a promising platform for next-generation neuromorphic computing systems.

2D TMDs↗
DOE OSTI2022

Thermal transport properties of IrSbSe

Here we report a thermal transport study of IrSbSe, which crystallizes in a noncentrosymmetric cubic structure with the P2 1 3 space group and shows a narrow-gap semiconducting behavior. The large discrepancy between the activation energy for conductivity [E ρ = 128(2) meV] and for thermopower [E S = 17.7 (9) meV] from 200 to 300 K indicates a polaronic transport mechanism. The electrical resistivity varies as exp (T 0 /T) 1/4 and thermopower varies as T 1/2 at low temperatures, indicating that it evolves into Mott variable-range hopping dominant conduction with decreasing temperature. IrSbSe shows a relatively low value of thermal conductivity (~1.65 W/Km) and thermopower of about 0.24 mV/K around 100 K, yet poor electrical conductivity. On the other hand, a high vacancy defect concentration on both Ir and Sb atomic sites of up to 15% suggests a high defect tolerance and points to the possibility of a future improvement of carrier density by chemical substitution or defect optimization.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Field Validation of Innovative Air-Source Heat Pumps for Cold-Climate Heating Applications

Air-source heat pumps (ASHPs) have historically found application in milder climates in the United States (US). Their application in cold climates has been hindered by reduced performance as outdoor temperatures fall below freezing and the need for backup or auxiliary electric resistance heaters to meet peak heating loads. Recent advances in cold-climate air-source heat pumps (ccASHPs) with features such as variable-speed compressors, multistage systems, and highly optimized thermal design have improved performance by increasing the coefficient of performance (COP), low-ambient-temperature capacity, and heating seasonal performance factor (HSPF) dramatically. Despite their benefits, ccASHPs are still not widely prescribed for cold climate conditions. This is due in part to a lack of verified, demonstrated performance and analysis in cold climates. Ultimately, high-efficiency ccASHPs play a key role in emerging energy/grid renovation efforts in addition to their potential energy savings and avoided carbon emissions, but their actual performance in various cold climate field settings must be better understood. The primary objective of this project was to measure the in-field performance of variable-capacity air-source heat pumps in cold climates with the goal of enabling the development of field-based performance maps. Specifically, the study looked at how the heat pumps operated in the field, the frequency of cycling, the frequency of defrost events, and the time spent in each mode of operation. The results are intended to be used by DOE to inform research and development of energy-efficient equipment and to develop guidelines for optimizing primary energy savings when using air-source heat pumps in heating-dominated regions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Two-Dimensional Silk Crystal Films as Matrix Layer for High-Performance Microelectronics

This study explores a bio-inspired approach for memristive devices by combining Keggin-type polyoxometalates (POMs)-[SiW 12 O 40 ] 4 (POM-T) and [PW 12 O 40 ] 3 (POM-P), with silk fibroin (SF) to create 2D SF–POM layers on highly ordered pyrolytic graphite (HOPG) as resistive switching layers for memristors. We propose that the ordered SF layer template 0D POMs facilitate the formation of conductive filaments, thereby enhancing the variability of the manufactured memristors. AFM analysis revealed that both SF and SF–POM layers shared similar morphologies, while SF–POM–T formed larger aggregates, likely due to the stronger acidity of POM-T, which probably caused SF to aggregate and alter its secondary structure. Scanning Kelvin probe microscopy (SKPM) revealed that POMs reduced the contact potential difference of HOPG, resulting in lower work functions. Compared to an SF device, the SF–POM–P device showed improved memristive behavior, with a larger current gap and good repeatability over multiple sweeps; whereas the SF–POM–T device did not exhibit memristor activity, likely due to acidity-induced disruption of the SF template’s order and CF formation. More importantly, SF–POM–P devices also demonstrated programmable memristive states. Finally, combining simulation-driven memristor modeling, we showcase a co-design workflow for advancing bioinspired memristors through new materials design, synthesis, and device modeling and development.

36 MATERIALS SCIENCE↗
DOE OSTI2022

The effects of aluminum concentration on the microstructural and electrochemical properties of lithium lanthanum zirconium oxide

Cubic lithium lanthanum zirconium oxide (Li 7-x Al x La 3 Zr 2 O 12 , LLZO) garnet has gained attention as a promising next-generation electrolyte for lithium batteries due to its high ionic conductivity and chemical stability with lithium metal. The high conductivity can be achieved through doping over a range of aluminum concentrations. In this study, we hot-pressed samples to achieve <2% nominal porosity with aluminum concentrations from x = 0.25–0.55 mol to understand the effect of aluminum on microstructure and electrochemistry. It was observed that beyond the aluminum solubility limit (x = ~0.40), resistive secondary phases formed at the grain boundaries. As a result, the percent grain boundary resistance increased from 17.6 to 41.2% for x = 0.25 and x = 0.55, respectively. Both the grain boundary and bulk activation energies remained relatively constant as the aluminum concentrations increased (~0.44 eV and ~0.39 eV, respectively). It was, therefore, surmised that the mobility term of the Nernst–Einstein equation was roughly independent of aluminum concentration and the major variable controlling bulk conductivity was the number of lithium charge carriers. As a result, as the aluminum concentration increased from x = 0.25 to x = 0.55 the bulk conductivity decreased from 0.56 to 0.15 mS cm -1 . Following these trends of increasing grain boundary resistance and decreasing bulk conductivity with increasing aluminum concentration, x = 0.25 had the highest total conductivity (0.46 mS cm -1 ). We demonstrated that aluminum concentration has a significant effect on the microstructure and electrochemical properties of LLZO. In conclusion, we believe this work could help understand how to link processing, microstructure, and electrochemical properties to guide the manufacturing of LLZO for use in solid-state batteries.

25 ENERGY STORAGE↗