In situ electrochemical TEM and 4D-STEM to study the lithiation dynamics in battery materials

Abstract number
Corresponding Email
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EMAG - Energy Materials
Mr. Nicolas Folastre (1), Mr. Junhao Cao (1), Dr. Sorina Cretu (1), Dr. Partha Pratim Das (2), Dr. Edgar Rauch (3), Dr. Stavros Nicolopoulos (2), Pr. Muriel Veron (3), Dr. Arnaud Demortiere (1)
2. NanoMegas

in situ electrochemical TEM, 4D-STEM, image processing, machine learning, lithiation dynamics, battery materials

Abstract text

The structural accommodations necessary for Li-ion insertion into battery materials are greatly influenced by the kinetics of the electrochemical reaction. To better understand the degradation of Li-ion battery retention in capacity, it is important to quantify the structural inhomogeneities, interfaces, and their changes during electrochemical cycling and state of charge.  A complete picture of the phenomena occurring during material operation can be obtained by understanding the spatial distribution of phase, orientation, grain boundary, and strain.

New in situ/operando analytical tools have been developed to monitor structural and chemical transformations during battery operation. In situ electrochemical TEM holder is a developing device that allows materials to be imaged and analyzed while immersed in liquid and electrochemically activated. This technique allows for direct imaging of key phenomena during battery operation, and helps to relate structural and compositional changes to electrochemical behaviors. Various electrochemical systems, such as Li-O2 [1], LiFePO4 [2] and solid-state [3], have been studied using these techniques.

To obtain crystallographic information on active material crystals and to correlate it with the spatial occupancy of Li-ion during the electrochemical cycle, 4D-STEM techniques [4] have been used to yield structure and strain maps. This 4D-STEM method using the ASTAR-ACOM system [5] (quasi-parallel beam) allows for the creation of maps of crystalline phase and orientation, enabling the determination of crystallinity, microstructures, structural deformation, and grain boundaries using scanning nano-diffraction with nanometer resolution.

The recent use of sensitive, high-speed and pixelated detectors such as CMOS cameras or hybrid-pixel detectors enabled larger areas to be scanned, and a high resolution close to 1 nm with a dwell-time below 1 ms. However, using a CMOS camera in the column implies strong changes in the acquired images in comparison to the use of a NanoMegas conventional external optical camera, as the quality of the image improves with the increased electron sensitivity and the resolution. 

This work presents an adaptive data processing method that enhances the accuracy and reduces overfitting in pattern matching processes used for determining crystalline orientations and phases. The method is applied to a large dataset of 4D-STEM electron diffraction patterns that were captured using a CMOS Oneview camera. The developed code (ePattern_Registration) [6] registers and reconstructs the diffraction signal contained in the dataset using linear filters, such as mean and Gaussian blur, and dedicated metrics are used to quantify the reconstruction quality. The resulting compression rate of 103 is suitable for the era of big data and leads to a significant improvement in numerical performance for the entire ACOM data processing method. This work demonstrates the positive impact of this data preparation on the quality of the resulting image as well as the confidence level of the analysis results for determining crystal orientation and phase.

Modifications inside diffraction pattern are estimated through image quality metrics such as signal-over-noise ratio (SNR), peak signal-over-noise (PSNR), structural similarity index measure (SSIM), mean absolute error (MAE), and root-mean-square error (RMSE). Second, the quality of the pattern matching process on filtered and reconstructed images is evaluated using index and orientation reliability, defined in the ASTAR software [7]. We demonstrate that the experimental data preparation provides great advantages for the pattern matching quality result, as it reduces noise overfitting, improves structural similarity index measure, and increases the orientation reliability by a factor from 2 to 3.

The essential information of each reflection of a dataset (200*200*512*512) such as intensity, size, and position are recorded in a few minutes with an accuracy of the order 10-2 px, with a data reduction factor of the order 102. The data reduction method applied in this work is a registration and reconstruction code for diffraction pattern, which is connected with NMF clustering [8] approach and allows the extraction of displacement/strain maps. 

In this work, we show that the 4D-STEM mapping of crystal structures and orientations provides essential information for the determination of individual particle lithiation mechanisms of cathode materials for Li-ion and Na-ion batteries, as NaxMnV(PO4)3 or all solid-state LAGP based electrolyte. 


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[5] Rauch, E. F., & Véron, M. J. M. C. (2014). Automated crystal orientation and phase mapping in TEM. Materials Characterization, 98, 1-9.

[6] Nicolas Folastre, Junhao Cao, Sunkyu Park, Arash Jamali, François Weill, Christian Masquelier, Laurence Croguennec, Muriel Veron, Edgar F. Rauch, Arnaud Demortière. Adaptative Diffraction Image Registration for 4D-STEM to optimize ACOM Pattern Matching, Microscopy and Microanalysis 2023 (in submission).

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[8] Junhao Cao, Nicolas Folastre, Gozde Oney, Partha Pratim Das, Stavros Nicolopoulos, Arnaud Demortiere, Multi-clustering detection of 4DSTEM diffraction patterns using NMF approach. NPJ computational Materials (in submission)