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Register a new userConformal Three-Dimensional Interphase of Li Metal Anode Revealed by Low Dose Cryo-Electron Microscopy
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Using cryogenic transmission electron microscopy, we revealed three dimensional (3D) structural details of the electrochemically plated lithium (Li) flakes and their solid electrolyte interphase (SEI), including the composite SEI skin-layer and SEI fossil pieces buried inside the Li matrix. As the SEI skin-layer is largely comprised of nanocrystalline LiF and Li2O in amorphous polymeric matrix, when complete Li stripping occurs, the compromised SEI three-dimensional framework buckles, forming nanoscale bends and wrinkles. We showed that the flexibility and resilience of the SEI skin-layer plays a vital role in preserving an intact SEI 3D framework after Li stripping. The intact SEI network enables the nucleation and growth of the newly plated Li inside the previously formed SEI network in the subsequent cycles, preventing additional large amount of SEI formation between newly plated Li metal and the electrolyte. In addition, cells cycled under the accurately controlled uniaxial pressure can further enhance the repeated utilization of the SEI framework and improve the coulombic efficiency (CE) by up to 97%, demonstrating an effective strategy of reducing the formation of additional SEI and inactive dead Li. The identification of such flexible and porous 3D SEI framework clarifies the working mechanism of SEI in lithium metal anode for batteries. The insights provided in this work will inspire researchers to design more functional artificial 3D SEI on other metal anodes to improve rechargeable metal battery with long cycle life.
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Crystallography, the primary method for determining the three-dimensional (3D) atomic positions in crystals, has been fundamental to the development of many fields of science. However, the atomic positions obtained from crystallography represent a global average of many unit cells in a crystal. Here, we report, for the first time, the determination of the 3D coordinates of thousands of individual atoms and a point defect in a material by electron tomography with a precision of ~19 picometers, where the crystallinity of the material is not assumed. From the coordinates of these individual atoms, we measure the atomic displacement field and the full strain tensor with a 3D resolution of ~1nm^3 and a precision of ~10^-3, which are further verified by density functional theory calculations and molecular dynamics simulations. The ability to precisely localize the 3D coordinates of individual atoms in materials without assuming crystallinity is expected to find important applications in materials science, nanoscience, physics and chemistry.
The solid electrolyte interphase (SEI) is regarded as the most complex but the least understood constituent in secondary batteries using liquid and solid electrolytes. The nanostructures of SEIs were recently reported to be equally important to the chemistry of SEIs for stabilizing Li metal in liquid electrolyte. However, the dearth of such knowledge in all-solid-state battery (ASSB) has hindered a complete understanding of how certain solid-state electrolytes, such as LiPON, manifest exemplary stability against Li metal. Characterizing such solid-solid interfaces is difficult due to the buried, highly reactive, and beam-sensitive nature of the constituents within. By employing cryogenic electron microscopy (cryo-EM), the interphase between Li metal and LiPON is successfully preserved and probed, revealing a multilayer mosaic SEI structure with concentration gradients of nitrogen and phosphorous, materializing as crystallites within an amorphous matrix. This unique SEI nanostructure is less than 80 nm and is shown stable and free of any organic lithium containing species or lithium fluoride components, in contrast to SEIs often found in state-of-the-art organic liquid electrolytes. Our findings reveal insights on the nanostructures and chemistry of such SEIs as a key component in lithium metal batteries to stabilize Li metal anode.
We develop in this paper a novel intrinsic classification algorithm -- multi-frequency class averaging (MFCA) -- for classifying noisy projection images obtained from three-dimensional cryo-electron microscopy (cryo-EM) by the similarity among their viewing directions. This new algorithm leverages multiple irreducible representations of the unitary group to introduce additional redundancy into the representation of the optimal in-plane rotational alignment, extending and outperforming the existing class averaging algorithm that uses only a single representation. The formal algebraic model and representation theoretic patterns of the proposed MFCA algorithm extend the framework of Hadani and Singer to arbitrary irreducible representations of the unitary group. We conceptually establish the consistency and stability of MFCA by inspecting the spectral properties of a generalized local parallel transport operator through the lens of Wigner $D$-matrices. We demonstrate the efficacy of the proposed algorithm with numerical experiments.
The 3D ternary Li_4Ti_5O_12, the Li+-based battery anode, presents the unusual lattice symmetry (a triclinic crystal), band structure, charge density, and density of states, under the first-principles calculations. It belongs to a large direct-gap semiconductor of E_g^d~ 2,98 eV. The atom-dominated valence and conduction bands, the spatial charge distribution and the atom- and orbital-decomposed van Hove singularities are available in the delicate identifications of multi-orbital hybridizations in Li-O and Ti-O bonds. The extremely non-uniform chemical environment, which induce the very complicated hopping integrals, directly arise from the large bonding fluctuations and the highly anisotropic configurations. Also, the developed theoretical framework is very useful for fully understanding the cathodes and electrolytes of oxide compounds.
Next generation batteries based on lithium (Li) metal anodes have been plagued by the dendritic electrodeposition of Li metal on the anode during cycling, resulting in short circuit and capacity loss. Suppression of dendritic growth through the use of solid electrolytes has emerged as one of the most promising strategies for enabling the use of Li metal anodes. We perform a computational screening of over 12,000 inorganic solids based on their ability to suppress dendrite initiation in contact with Li metal anode. Properties for mechanically isotropic and anisotropic interfaces that can be used in stability criteria for determining the propensity of dendrite initiation are usually obtained from computationally expensive first-principles methods. In order to obtain a large dataset for screening, we use machine learning models to predict the mechanical properties of several new solid electrolytes. We train a convolutional neural network on the shear and bulk moduli purely on structural features of the material. We use AdaBoost, Lasso and Bayesian ridge regression to train the elastic constants, where the choice of the model depended on the size of the training data and the noise that it can handle. Our models give us direct interpretability by revealing the dominant structural features affecting the elastic constants. The stiffness is found to increase with a decrease in volume per atom, increase in minimum anion-anion separation, and increase in sublattice (all but Li) packing fraction. Cross-validation/test performance suggests our models generalize well. We predict over 20 mechanically anisotropic interfaces between Li metal and 6 solid electrolytes which can be used to suppress dendrite growth. Our screened candidates are generally soft and highly anisotropic, and present opportunities for simultaneously obtaining dendrite suppression and high ionic conductivity in solid electrolytes.
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