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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Leveraging a Neural Network-Enhanced Reproducing Kernel Particle Method for Multiphysics Degradation Modeling of Energy Storage Materials

Energy storage materials exhibit strong electro-chemo-mechanical coupling and highly anisotropic material properties, contributing to the formation and propagation of micro-cracking during charge/discharge cycling and resulting in reduced performance and service life. A coupled electro-chemo-mechanical reproducing kernel particle method (RKPM) formulation has been developed to analyze this system. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based model construction by RKPM is used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electro-chemo-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in meshing such complex geometries and capturing crack propagation due to mesh dependency. Here, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1, 2] is introduced to effectively model damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. NN-RKPM is additionally used to inform how crack opening and closure in turn affect the coupled chemical equations and material microstructure. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022. [2] Baek, J., Chen, J. S., "A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture", Computer Methods in Applied Mechanics and Engineering Vol. 410, 116590, 2024.

degradation↗
DOE OSTI2022

Understanding Lithium Local Environments in LiMn 0.5 Ni 0.5 O 2 Cathodes: A DFT-Supported 6 Li Solid-State NMR Study

LiMn 0.5 Ni 0.5 O 2 has been plagued by the structural failure driven by the irreversible phase change upon subsequent cycling and the structural degradation caused by the seemingly unavoidable Li/Ni exchange. This begs for a better understanding of the correlation between lithium local structures, synthesis conditions, and overall electrochemical properties of materials. In this study, we use density functional theory (DFT) to assist experimental 6 Li solid-state magic angle spinning (MAS) nuclear magnetic resonance (NMR) spectroscopy to understand the nature and change in lithium local structures with extended annealing times (168 vs 15 h) synthesized at 900 °C. To correlate the NMR peak changes after different synthesis conditions, we calculated the NMR spectra of representative low-energy model configurations of LiMn 0.5 Ni 0.5 O 2 composition–zigzag, row, and flower structures–as well as the influence of Ni/Mn ordering and Li/Ni exchange on each spectrum using a supercell that is large enough to allow us to consider the effect of different relative configurations of adjacent transition metal (TM) layers. The analysis based on a combination of the calculated NMR and the experimental spectra suggests that most configurations contributing to the experimental spectra are in fact composed of a blend of configurations higher in energy than the classical well-ordered ground-state structures. Further, the extended annealing of LiMn 0.5 Ni 0.5 O 2 slightly enhances Ni/Mn and Li/Mn orderings while reducing the Li/Ni mixing ratio. This study shows how DFT calculations are crucial in providing a better understanding of the experimental characterization data and therefore local environments and domain structures in oxide materials, such as Li-ion battery cathodes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Enumeration as a Tool for Structure Solution: A Materials Genomic Approach to Solving the Cation-Ordered Structure of Na 3 V 2 (PO 4 ) 2 F 3

While powder diffraction methods are routinely utilized to optimize structural models for compounds whose crystal structures are known, the determination of unknown structures is far more challenging. When the unknown structure is large, structure solution can become a virtually intractable problem using standard structure solution methodologies, especially when the space group cannot be unambiguously resolved. One such system is the promising Na-ion battery cathode material Na 3 V 2 (PO 4 ) 2 F 3 whose high temperature and room temperature structures were previously solved, but whose more complex low-temperature structure could not be determined. Here, a novel materials genomic approach is demonstrated for the solution of the unknown 100 K structure of Na 3 V 2 (PO 4 ) 2 F 3 in which enumeration methods are first used to generate a large number (~3,000) of trial structures based on plausible orderings of Na ions and then automated Rietveld refinements are carried out to optimize each of these trial structures. Based on both the analysis of the ensemble of optimized trial structures and the density functional theory energy minimization of selected trial structures, the 100 K structure of Na 3 V 2 (PO 4 ) 2 F 3 is best described as belonging to the space group A2 1 am with unit cell dimensions of a = 9.01928(4), b = 27.1379(1), c = 10.73307(5). The 100 K unit cell has a large volume of 2627.07(2) Å 3 with Z = 12 and 33 independent crystallographic sites (9 Na, 3 V, 3 P, 12 O, and 6 F) that is 3x and 6x larger than the room- and high-temperature polymorphs of this phase, respectively. Finally, the novel methods described here will be generally applicable for the solution of the complex cation-ordered structures that commonly occur for battery materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Hybrid Density Functional Theory Comparison of Oxygen Release and Solvent Decomposition Kinetics on Li x NiO 2 Surfaces

High-nickel-content layered oxides are among the most promising electric vehicle battery cathode materials. However, their interfacial reactivity with electrolytes and tendency toward oxygen release (possibly yielding reactive 1 O 2 ) remain degradation concerns. Elucidating the most relevant (i.e., fastest) interfacial degradation mechanism will facilitate future mitigation strategies. We apply screened hybrid density functional (HSE06) calculations to compare the reaction kinetics of Li x NiO 2 surfaces with ethylene carbonate (EC) with those of O 2 release. Further, on both the (001) and (104) facets, EC oxidative decomposition exhibits lower activation energies than O 2 release. Our calculations, coupled with previously computed liquid-phase reaction rates of 1 O 2 with EC, strongly question the role of “reactive 1 O 2 ” species in electrolyte oxidative degradation. The possible role of other oxygen species is discussed. To deal with the challenges of modeling Li x NiO 2 surface reactivity, we emphasize a “local structure” approach instead of pursuing the global energy minimum.

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

Assessing the coating implications of slurry formulations in NCMA and LFMP blend cathodes for lithium-ion batteries

This paper investigates the implications of slurry formulations and electrode processing on the performance of lithium-ion battery (LIB) cathodes, focusing on Nickel Cobalt Manganese Aluminum (NCMA) and Lithium Ferro Manganese Phosphate (LFMP) blends. Through a comprehensive examination of electrode processing steps, from material selection to coating application, we elucidate the critical role of processing parameters in shaping electrode morphology and electrochemical behavior. Rheological studies reveal the influence of slurry composition on viscosity and flow behavior, highlighting the importance of achieving optimal rheological properties for uniform coating deposition. Electrochemical characterization, including cyclic voltammetry and rate capability tests, unveils the electrochemical behavior of NCMA, LFMP, and their blend, showcasing the synergistic effects of material blending on battery performance. In conclusion, our findings underscore the intricate relationship between slurry formulation, electrode processing, and LIB performance, offering valuable insights for the design and optimization of high-performance electrode materials for next-generation batteries.

25 ENERGY STORAGE↗
DOE OSTI2023

Stabilizing lattice oxygen redox in layered sodium transition metal oxide through spin singlet state

Abstract Reversible lattice oxygen redox reactions offer the potential to enhance energy density and lower battery cathode costs. However, their widespread adoption faces obstacles like substantial voltage hysteresis and poor stability. The current research addresses these challenges by achieving a non-hysteresis, long-term stable oxygen redox reaction in the P3-type Na 2/3 Cu 1/3 Mn 2/3 O 2 . Here we show this is accomplished by forming spin singlet states during charge and discharge. Detailed analysis, including in-situ X-ray diffraction, shows highly reversible structural changes during cycling. In addition, local CuO 6 Jahn-Teller distortions persist throughout, with dynamic Cu-O bond length variations. In-situ hard X-ray absorption and ex-situ soft X-ray absorption study, along with density function theory calculations, reveal two distinct charge compensation mechanisms at approximately 3.66 V and 3.99 V plateaus. Notably, we observe a Zhang-Rice-like singlet state during 3.99 V charging, offering an alternative charge compensation mechanism to stabilize the active oxygen redox reaction.

25 ENERGY STORAGE↗
DOE OSTI2020

Evolution of lithium ordering with (de)-lithiation in β-LiVOPO 4 : insights through solid-state NMR and first principles DFT calculations

The lithium-ion battery cathode material β-VOPO 4 is capable of intercalating more than one Li ion per transition metal ion due to the accessibility of both the V 5+ /V 4+ and V 4+ /V 3+ redox couples at ~4.5 V and ~2.3 V vs. Li, respectively, giving a theoretical capacity greater than ~300 mA h g -1 . The ability to perform full and reversible two Li-ion intercalation in this material, however, has been a matter of debate and the poor crystallinity of the fully lithiated phase has thus far precluded its complete structural characterisation by conventional diffraction-based methods. In this work, 7 Li and 31 P NMR spectroscopy, in combination with first principles DFT calculations, indicate that chemical lithiation results in a single phase β-Li 2 VOPO 4 exhibiting a complex Li ordering scheme with lithium ions occupying multiple disordered environments. 2D NMR 7 Li correlation experiments were used to deduce the most likely Li ordering for the β-Li 2 VOPO 4 phase from amongst several DFT optimised structures. In contrast, electrochemically lithiated β-Li 2- x VOPO 4 discharged to 1.6 V exhibits, in addition to β-Li 2 VOPO 4 , a β-Li 1.5 VOPO 4 phase. Additionally, the existence of β-Li 1.5 VOPO 4 is not reflected in the flat galvanostatic charge and discharge curves nor is evident from diffraction-based methods due to the very close structural similarity between the β-Li 1.5 VOPO 4 phase and β-Li 2 VOPO 4 phases. We demonstrate that solid state NMR spectroscopy, in combination with DFT results, provides a powerful tool for identifying intermediate states formed during charge/discharge of these complex phosphates as these phases can be distinguished from the end member phases primarily by the nature of the lithium ordering.

25 ENERGY STORAGE↗
DOE OSTI2024

Machine-learning-assisted deciphering of microstructural effects on ionic transport in composite materials: A case study of Li 7 La 3 Zr 2 O 12 -LiCoO 2

The effective diffusivity of ionic species in multiphase materials is critical for the design and function of composite materials for electrochemical energy storage. In practice, effective diffusivity depends sensitively not only on the intrinsic diffusivities of constituting materials but also on their topological arrangement; nevertheless, these coupled contributions are oversimplified in most analytical models. Here, we combine atomistically informed mesoscale modeling and machine learning (ML) analysis to unravel how such features affect effective diffusivity in two-phase composites. Using the Li 7 La 3 Zr 2 O 12 -LiCoO 2 composite solid-state battery cathode as a model system, we compute effective diffusivity for 600 distinct dense polycrystalline microstructures with different topological configurations of grains, grain boundaries, and heterointerfaces. We verify that in addition to atomic-scale variabilities, microstructural feature diversity can significantly impact effective transport properties. Across the ensemble of test microstructures, this often results in bimodal distributions of effective diffusivity that encompass two qualitatively distinct operating mechanisms, which we identify via flux analysis. An ML approach reveals that the most critical determining factors for effective diffusivity are the connectivity of bulk phases and their heterointerfaces. The role of ionic mobility at the heterointerfaces is also discussed. These insights highlight the combined importance of microstructure and interface engineering in tuning the transport properties of ionic species in composite materials. In conclusion, our framework can also be extended for understanding generic microstructure-property relationships in other complex multiphase materials.

25 ENERGY STORAGE↗
DOE OSTI2023

Neutron diffraction probing hydrogen in monoclinic H 2 VOPO 4

Light hydrogen atoms are resolvable with neutrons; however, the massive incoherent background inhibits the diffraction quality. To improve the signal, isotope treatment by deuteration becomes a prerequisite for a successful crystallographic understanding of hydrogen-containing materials by neutron diffraction. Thanks to the low-background and high-resolution time-of-flight neutron diffractometer, this work demonstrates a direct and successful measurement of high-quality neutron diffraction patterns of H 2 VOPO 4 powders, a precursor of a high-capacity alkali-ion battery cathode. The Rietveld refinement identifies the hydrogen coordinates, occupancy, and thermal parameters in the H 2 VOPO 4 lattice. Finally, the result highlights the unique capability of neutron diffraction for the structure characterization of hydrogen-containing materials, potentially without requiring costly and possibly artifact-inducing deuteration for neutron diffraction.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Identifying point defects and ordering in the high-entropy layered oxide Li 1.5 MO 3-δ (M=Mn, Al, Fe, Co, Ni) for energy storage applications

High-entropy layered oxides (HELOs) represent a very promising class of next-generation battery cathodes, combining the well-studied properties of layered cathode materials such as LiCoO 2 with the chemical tunability and stability of high-entropy materials. HELO materials often form particles with complex defects and domain structures, complicating accurate characterization of structure and cation mixing. Understanding disorder and order in HELO materials is necessary for understanding their performance and utility as cathodes. Here we demonstrate the characterization of the HELO Li 1.5 MO 3-δ (M = Mn, Al, Fe, Co, Ni), wherein X-ray powder diffraction, transmission electron microscopy imaging, and electron diffraction patterns are analyzed to reveal the presence of ordering in the HELO, with imaging and diffraction simulation employed to compare experimental results to atomic modeling. Finally, without rigorous characterization at the atomic scale, important features such as defect ordering can be easily overlooked and therefore remain unconsidered when interpreting experimental results.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Access to Ru(IV)–Ru(V) and Ru(V)–Ru(VI) Redox in Layered Li 7 RuO 6 via Intercalation Reactions

The push to increase the capacity of oxides to store charge as Li-ion battery cathodes has challenged our knowledge of the boundaries of redox chemistry and its relationship to the atomic structure. Layered phases with Ru are now classical models in the quest for refined theories. Current understanding dictates that the highest formal state that can be reached in them is Ru(V), after which activity centered at the oxide anions is triggered. Yet, this picture is challenged by this investigation of the intercalation chemistry of Li 7 RuO 6 . Coupling structural and spectroscopic analysis with computational simulations, we show that Li 7 RuO 6 is capable of highly reversible lithiation to Li 8 RuO 6 through the Ru(V)/Ru(IV) redox couple. Further, Li 7 RuO 6 can also undergo anodic Li deintercalation, showing clear evidence of the reversible formation of Ru(VI) with octahedral coordination, an unusual state that is not accessible in other layered oxides. The results highlight the versatility of the Ru-O bond to undergo distinct redox transitions depending on the specific layered arrangements. They enrich our understanding of redox chemistry in solids while underscoring the need for descriptions of charge compensation that reflect the nuance of covalent interactions.

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

Evaluation of Chemical and Structural Homogeneity in Single Particles of Li 1-x Ni 0.33 Mn 0.33 Co 0.33 0 2

Identifying irreversible phenomena at single particles of battery cathode materials is an important step to understanding degradation pathways upon cycling. LiNi 0.33 Mn 0.33 Co 0.33 O 2 (LiNMC(111)) single particles of size 200 x 300 nm were studied before and after cycling using Scanning X-ray Diffraction Microscopy (SXDM) and ptychographic microscopy. Here, with resolutions of 30 and 5 nm, respectively, it was possible to map the (003) reflection and nickel oxidation state changes in single particles at different states of charge. While the compositions measured by the c lattice parameter from SXDM were found to be homogeneous, mapping at higher resolution using ptychography revealed that Ni oxidation states decrease sharply 0-25 nm from the edge, then trail into a steady oxidation state toward the interior of the particle. Within the 30 nm length scale, we conclude that LiNi 0.33 Mn 0.33 Co 0.33 O 2 single particles are highly structurally reversible.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Impact of Charge Voltage on Factors Influencing Capacity Fade in Layered NMC622: Multimodal X-ray and Electrochemical Characterization

Ni-rich NMC is an attractive Li-ion battery cathode due to its combination of energy density, thermal stability, and reversibility. While higher delivered energy density can be achieved with a more positive charge voltage limit, this approach compromises sustained reversibility. Improved understanding of the local and bulk structural transformations as a function of charge voltage, and their associated impacts on capacity fade are critically needed. Through simultaneous operando synchrotron X-ray diffraction (XRD) and X-ray absorption spectroscopy (XAS) of cells cycled at 3–4.3 or 3–4.7 V, this study presents an in-depth investigation into the effects of voltage window on local coordination, bulk structure, and oxidation state. These measurements are complemented by ex situ X-ray fluorescence (XRF) mapping and scanning electrochemical microscopy mapping (SECM) of the negative electrode, X-ray photoelectron spectroscopy (XPS) of the positive electrode, and cell level electrochemical impedance spectroscopy (EIS). Initially, cycling between 3 and 4.7 V leads to greater delivered capacity due to greater lithium extraction, accompanied by increased structural distortion, moderately higher Ni oxidation, and substantially higher Co oxidation. Continued cycling at this high voltage results in suppressed Ni and Co redox, greater structural distortion, increased levels of transition metal dissolution, higher cell impedance, and 3× greater capacity fade.

capacity fading mechanisms↗
DOE OSTI2022

Lithium-Ion Battery Materials as Tunable, “Redox Non-Innocent” Catalyst Supports

The development of general strategies for the electronic tuning of a catalyst’s active site is an ongoing challenge in heterogeneous catalysis. To this end, herein, we describe the application of Li-ion battery cathode and anode materials as redox non-innocent catalyst supports that can be continuously modulated as a function of lithium intercalation. A zero-valent nickel complex was oxidatively grafted onto the surface of lithium manganese oxide (Li x Mn 2 O 4 ) to yield isolated Ni2+ occupying the vacant interstitial octahedral site in the Li diffusion channel on the surface and subsurface of the spinel structure (Ni/Li x Mn 2 O 4 ). Additionally, the activity of Ni/Li x Mn 2 O 4 for olefin hydrogenation, as a representative probe reaction, was found to increase monotonically as a function of support reductive lithiation. Simulation of Ni/Li x Mn 2 O 4 reveals the dramatic impact of surface redox states on the viability of the homolytic oxidative addition mechanism for H 2 activation. Catalyst control through support lithiation was extended to an organotantalum complex on Li x TiO 2 , demonstrating the generality of this phenomenon.

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

Sub-10 second fly-scan nano-tomography using machine learning

Abstract X-ray computed tomography is a versatile technique for 3D structure characterization. However, conventional reconstruction algorithms require that the sample not change throughout the scan, and the timescale of sample dynamics must be longer than the data acquisition time to fulfill the stable sample requirement. Meanwhile, concerns about X-ray-induced parasite reaction and sample damage have driven research efforts to reduce beam dosage. Here, we report a machine-learning-based image processing method that can significantly reduce data acquisition time and X-ray dose, outperforming conventional approaches like Filtered-Back Projection, maximum-likelihood, and model-based maximum-a-posteriori probability. Applying machine learning, we achieve ultrafast nano-tomography with sub-10 second data acquisition time and sub-50 nm pixel resolution in a transmission X-ray microscope. We apply our algorithm to study dynamic morphology changes in a lithium-ion battery cathode under a heating rate of 50 o C min −1 , revealing crack self-healing during thermal annealing. The proposed method can be applied to various tomography modalities.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Navigating phase diagram complexity to guide robotic inorganic materials synthesis

Abstract Efficient synthesis recipes are needed to streamline the manufacturing of complex materials and to accelerate the realization of theoretically predicted materials. Often, the solid-state synthesis of multicomponent oxides is impeded by undesired by-product phases, which can kinetically trap reactions in an incomplete non-equilibrium state. Here we report a thermodynamic strategy to navigate high-dimensional phase diagrams in search of precursors that circumvent low-energy, competing by-products, while maximizing the reaction energy to drive fast phase transformation kinetics. Using a robotic inorganic materials synthesis laboratory, we perform a large-scale experimental validation of our precursor selection principles. For a set of 35 target quaternary oxides, with chemistries representative of intercalation battery cathodes and solid-state electrolytes, our robot performs 224 reactions spanning 27 elements with 28 unique precursors, operated by 1 human experimentalist. Our predicted precursors frequently yield target materials with higher phase purity than traditional precursors. Robotic laboratories offer an exciting platform for data-driven experimental synthesis science, from which we can develop fundamental insights to guide both human and robotic chemists.

Chen, Jiadong (ORCID:0009000476038838)↗