Operando Liquid-Electrochemical TEM

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Operando X-ray Computed Tomography

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Scientist at CNRS in 3D/4D imaging, computer vision and AI-DeepLearning
LRCS laboratoryRS2E network (Amiens,France)

My research topics focus on the development of new methods in 3D/4D imaging and  in AI-DeepLearning to study the dynamical phenomena occuring inside Li-ion batteries and new generation of electrochemical storage systems

 

Image treatments using convolutional neural network, which are essential for statistical approach of big data, is developed in the team to process 3D tomographic and dynamical dataset.

- Microscopy Platform Manager in RS2E network

- Leader of Image and diffraction team (I&D team) in LRCS lab

Email: arnaud.demortiere@cnrs.fr

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Publication Highlight

January 2021

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Despite significant progress in the field of tomography, capturing the carbon binder domain (CBD) morphology presented in the Li-ion electrode remains challenging, due to its low attenuation coefficient.

 

In this work, quantitative phase contrast X-ray nano-holotomography is used as a straightforward approach that provides a large reconstructed volume, where the CBD can be resolved along with the active materials and the pore space. As a result, a complete quantitative analysis of the microstructures of three LiNi0.5Mn0.3Co0.2O2 high energy density electrodes, including the characterization of each phase separately along with the statistical quantification of their inter-connectivity at particle scale, is performed.

The microstructural heterogeneities are quantified and comparison between different electrodes is done. The results from this work suggest reasons for the negative impacts of the CBD excess on the electrode performance at high C-rates. Those results are true in the case of high energy density electrodes, and are due to the reduction of the electrochemical active surface area. This sheds light on the optimization of the electrode design to improve the power rate of high energy density electrodes.

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August 2020

This work demonstrates that the impedance spectra simulated from 3D microstructures may deviate from the conventional behavior simulated by a TLM or Newman’s P2D model in one of two scenarios: either the control volume is not large enough to be representative of the material, or the system is not conventionally homogeneous, e.g., the accessible interfacial area varies significantly with depth. As well as a qualitative comparison, it is possible to conceive of various metrics that could be used to quantify the degree of agreement between simulated EIS spectrum of the symmetric cell and TLM fit, thus providing a direct way to assess the degree to which porous electrode theory applies to a particular electrode microstructure. All of these concepts are to be applied to real electrode microstructural data in a follow-up study exploring optimal electrode design.

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March 2020

We here report for the first time the characterization of Li−O2 battery cathodes by 3D tomography, allowing us to distinguish and localize Li2O2 from carbon material and pores. Such characterization, which coupled X-ray nano-CT with in- line Zernike phase contrast, was carried out on a few cathodes discharged independently until there are different depths of discharge. First and foremost, we demonstrated the feasibility of this technique theoretically onto the cathode of the Li−O2 system. In practice, at 8 keV this technique provides a high resolution (nm), contrast, and a large field of view (50 μm). Throughout the present paper, the X-ray Zernike phase contrast nanotomography for Li−O2 battery provides intuitive visualizations and valuable quantitative information. We successfully retrieved the pore size distribution and inter- connectivity in different cathode electrodes. The surface passivation has also been analyzed. In the studied material, the analysis shows a heterogeneous spatial distribution and inefficient occupation of Li2O2, probably due to the highly tortuous electrode and low diffusivity of oxygen. In addition, future work with this technique should be extended to other parts of the battery, such as a lithium anode, or a separator for investigating transport properties. Last but not least, the nondestructive property and nonvacuum requirement of this technique turn out to be robust for the development of the operando experiment. The investigation of the Li−O2 cathode kinetics should be carried out in future work.

Artificial Neural Network

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Our new software for multiphase segmentation

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