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Register a new userBridging Nano and Micro-scale X-ray Tomography for Battery Research by Leveraging Artificial Intelligence
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X-ray Computed Tomography (X-ray CT) is a well-known non-destructive imaging technique where contrast originates from the materials absorption coefficients. Novel battery characterization studies on increasingly challenging samples have been enabled by the rapid development of both synchrotron and laboratory-scale imaging systems as well as innovative analysis techniques. Furthermore, the recent development of laboratory nano-scale CT (NanoCT) systems has pushed the limits of battery material imaging towards voxel sizes previously achievable only using synchrotron facilities. Such systems are now able to reach spatial resolutions down to 50 nm. Given the non-destructive nature of CT, in-situ and operando studies have emerged as powerful methods to quantify morphological parameters, such as tortuosity factor, porosity, surface area, and volume expansion during battery operation or cycling. Combined with powerful Artificial Intelligence (AI)/Machine Learning (ML) analysis techniques, extracted 3D tomograms and battery-specific morphological parameters enable the development of predictive physics-based models that can provide valuable insights for battery engineering. These models can predict the impact of the electrode microstructure on cell performances or analyze the influence of material heterogeneities on electrochemical responses. In this work, we review the increasing role of X-ray CT experimentation in the battery field, discuss the incorporation of AI/ML in analysis, and provide a perspective on how the combination of multi-scale CT imaging techniques can expand the development of predictive multiscale battery behavioral models.
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To meet growing energy demands, degradation mechanisms of energy storage devices must be better understood. As a non-destructive tool, X-ray Computed Tomography (CT) has been increasingly used by the battery community to perform $textit{in-situ}$ experiments that can investigate dynamic phenomena. However, few have used X-ray CT to study representative battery systems over long cycle lifetimes (>100 cycles). Here, we report the $textit{in-situ}$ CT study of Zn-Ag batteries and demonstrate the effects of current collector parasitic gassing over long-term storage and cycling. We design performance representative $textit{in-situ}$ CT cells that can achieve >250 cycles at a high areal capacity of $mathrm{12.5;mAh/cm^2}$. Combined with electrochemical experiments, the effects of current collector parasitic gassing are revealed with micro-scale CT (MicroCT). The volume expansion and evolution of ZnO and Zn depletion is quantified with cycling and elevated temperature testing. The experimental insights are then utilized to develop larger form-factor $mathrm{4;cm^2}$ cells with electrochemically compatible current collectors. With this, we demonstrate over 325 cycles at a high capacity of $mathrm{12.5;mAh/cm^2}$ for a $mathrm{4;cm^2}$ form-factor. This work demonstrates that $textit{in-situ}$ X-ray CT used in long cycle-lifetime studies can be applied to examine a multitude of other battery chemistries to improve their performances.
The elastic properties of materials derive from their electronic and atomic nature. However, simulating bulk materials fully at these scales is not feasible, so that typically homogenized continuum descriptions are used instead. A seamless and lossless transition of the constitutive description of the elastic response of materials between these two scales has been so far elusive. Here we show how this problem can be overcome by using Artificial Intelligence (AI). A Convolutional Neural Network (CNN) model is trained, by taking the structure image of a nanoporous material as input and the corresponding elasticity tensor, calculated from Molecular Statics (MS), as output. Trained with the atomistic data, the CNN model captures the size- and pore-dependency of the materials elastic properties which, on the physics side, can stem from surfaces and non-local effects. Such effects are often ignored in upscaling from atomistic to classical continuum theory. To demonstrate the accuracy and the efficiency of the trained CNN model, a Finite Element Method (FEM) based result of an elastically deformed nanoporous beam equipped with the CNN as constitutive law is compared with that by a full atomistic simulation. The good agreement between the atomistic simulations and the FEM-AI combination for a system with size and surface effects establishes a new lossless scale bridging approach to such problems. The trained CNN model deviates from the atomistic result by 9.6% for porosity scenarios of up to 90% but it is about 230 times faster than the MS calculation and does not require to change simulation methods between different scales. The efficiency of the CNN evaluation together with the preservation of important atomistic effects makes the trained model an effective atomistically-informed constitutive model for macroscopic simulations of nanoporous materials and solving of inverse problems.
Soft X-ray magnetic vector tomography has been used to visualize with unprecedented detail and solely from experimental data the 3D magnetic configuration of a ferrimagnetic Gd12Co88/Nd17Co83/Gd24Co76 multilayer with competing anisotropy, exchange and magnetostatic interactions at different depths. The trilayer displays magnetic stripe domains, arranged in a chevron pattern, which are imprinted from the central Nd17Co83 into the bottom Gd12Co88 layer with a distorted closure domain structure across the thickness. Near the top Gd24Co76 layer, local exchange springs with out-of-plane magnetization reversal, modulated ripple patterns and magnetic vortices and antivortices across the thickness are observed. The detailed analysis of the magnetic tomogram shows that the effective strength of the exchange spring at the NdCo/GdCo interface can be finely tuned by GdxCo1-x composition and anisotropy (determined by sample fabrication) and in-plane stripe orientation (adjustable), demonstrating the capability of 3D magnetic visualization techniques in magnetic engineering research.
We present an x-ray absorption study of the oxidation states of transition-metal-ions of LiMnO2 and its related materials, widely used as cathodes in Li-ion batteries. The comparison between the obtained spectrum and the configuration-interaction cluster-model calculations showed that the Mn3+ in LiMnO2 is a mixture of the high-spin and low-spin states. We found that Li deficiencies occur in the case of Cr substitution, whereas there are no Li deficiencies in the case of Ni substitution. We conclude that the substitution of charge-transfer-type Ni or Cu is effective for LiMnO2 battery materials.
In this paper, the implementation of optical elements in the form of Pancharatnam-Berry optics is considered. With respect to 3D bulk and diffractive optics, acting on the dynamic phase of light, Pancharatnam-Berry optical elements transfer a phase which is geometric in nature by locally manipulating the polarization state of the incident beam. They can be realized as space-variant sub-wavelengths gratings that behave like inhomogeneous form-birefringent materials. We present a comprehensive work of simulation, realization, and optical characterization at the telecom wavelength of 1310 nm of the constitutive linear grating cell, whose fabrication has been finely tuned in order to get a {pi}-phase delay and obtain a maximum in the diffraction efficiency. The optical design in the infrared region allows the use of silicon as candidate material due to its transparency. In order to demonstrate the possibility to assemble the single grating cells for generating more complex phase patterns, the implementation of two Pancharatnam-Berry optical elements is considered: a blazed grating and an optical vortices demultiplexer.
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