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1. Self-supervised image quality assessment for X-ray tomographic images of Li-ion battery

Author

Kai Zhang, Tuan-Tu Nguyen, Zeliang Su, and Arnaud Demortière

Subjects
Materials of engineering and construction. Mechanics of materials, TA401-492, Computer software, QA76.75-76.765
Abstract

Abstract Image perception plays a fundamental role in the tomography-based approaches for microstructure characterization and has a deep impact on all subsequent stages of image processing, such as segmentation and 3D analysis. The enhancement of image perception, however, frequently involves observer-dependence, which reflects user-to-user dispersion and uncertainties in the calculated parameters. This work presents an objective quantitative method, which uses convolutional neural networks (CNN) for the quality assessment of the X-ray tomographic images. With only dozens of annotations, our method allows to evaluate directly and precisely the quality of tomographic images. Different metrics were employed to evaluate the correlation between our predicted scores and subjective human annotations. The evaluation results demonstrate that our method can be a direct tool to guide the enhancement process in order to produce reliable segmentation results. The processing of the tomographic image can thus evolve into a robust observer-independent procedure and advance towards the development of an efficient self-supervised approach.

Published
2022
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2. Artificial neural network approach for multiphase segmentation of battery electrode nano-CT images

Author

Zeliang Su, Etienne Decencière, Tuan-Tu Nguyen, Kaoutar El-Amiry, Vincent De Andrade, Alejandro A. Franco, and Arnaud Demortière

Subjects
Materials of engineering and construction. Mechanics of materials, TA401-492, Computer software, QA76.75-76.765
Abstract

Abstract The segmentation of tomographic images of the battery electrode is a crucial processing step, which will have an additional impact on the results of material characterization and electrochemical simulation. However, manually labeling X-ray CT images (XCT) is time-consuming, and these XCT images are generally difficult to segment with histographical methods. We propose a deep learning approach with an asymmetrical depth encode-decoder convolutional neural network (CNN) for real-world battery material datasets. This network achieves high accuracy while requiring small amounts of labeled data and predicts a volume of billions voxel within few minutes. While applying supervised machine learning for segmenting real-world data, the ground truth is often absent. The results of segmentation are usually qualitatively justified by visual judgement. We try to unravel this fuzzy definition of segmentation quality by identifying the uncertainty due to the human bias diluted in the training data. Further CNN trainings using synthetic data show quantitative impact of such uncertainty on the determination of material’s properties. Nano-XCT datasets of various battery materials have been successfully segmented by training this neural network from scratch. We will also show that applying the transfer learning, which consists of reusing a well-trained network, can improve the accuracy of a similar dataset.

Published
2022
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4. 3D Operando Monitoring of Lithiation Spatial Composition in NMC Cathode Electrode by X-ray Nano-CT & XANES Techniques

Author

Tuan-Tu Nguyen, Jiahui Xu, Zeliang Su, Vincent De Andrade, Bruno Delobel, Charles Delacourt, Arnaud Demortière, Réseau sur le stockage électrochimique de l'énergie (RS2E), Université de Nantes (UN)-Aix Marseille Université (AMU)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Collège de France (CdF (institution))-Université de Picardie Jules Verne (UPJV)-Ecole Nationale Supérieure de Chimie de Montpellier (ENSCM)-Ecole Nationale Supérieure de Chimie de Paris - Chimie ParisTech-PSL (ENSCP), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Pau et des Pays de l'Adour (UPPA)-Institut de Chimie du CNRS (INC)-Université de Montpellier (UM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Université Grenoble Alpes (UGA), Laboratoire réactivité et chimie des solides - UMR CNRS 7314 (LRCS), and Université de Picardie Jules Verne (UPJV)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[CHIM.GENI]Chemical Sciences/Chemical engineering, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, Instrumentation, [SPI.MAT]Engineering Sciences [physics]/Materials
Abstract

International audience

Published
2022
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5. Mathematical Modeling of Energy-dense NMC Electrodes: Part II. Data Analysis with Newman Model and with an Extended Model Accounting for Particle Agglomeration

Author

Tuan-Tu Nguyen, Bruno Delobel, Arnaud Demortière, Charles Delacourt, Laboratoire réactivité et chimie des solides - UMR CNRS 7314 (LRCS), Université de Picardie Jules Verne (UPJV)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Technocentre Renault [Guyancourt], RENAULT, Réseau sur le stockage électrochimique de l'énergie (RS2E), Aix Marseille Université (AMU)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Collège de France (CdF (institution))-Université de Picardie Jules Verne (UPJV)-Ecole Nationale Supérieure de Chimie de Paris - Chimie ParisTech-PSL (ENSCP), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Pau et des Pays de l'Adour (UPPA)-Institut de Chimie du CNRS (INC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Université Grenoble Alpes (UGA)-Nantes Université (Nantes Univ)-Université de Montpellier (UM)-Ecole Nationale Supérieure de Chimie de Montpellier (ENSCM), Université de Montpellier (UM), Advanced Lithium Energy Storage Systems - ALISTORE-ERI (ALISTORE-ERI), Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Université de Toulouse (UT)-Université de Toulouse (UT)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Collège de France (CdF (institution))-Université de Picardie Jules Verne (UPJV)-Ecole Nationale Supérieure de Chimie de Paris - Chimie ParisTech-PSL (ENSCP), Université de Toulouse (UT)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université de Nantes (UN)-Aix Marseille Université (AMU)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Collège de France (CdF (institution))-Université de Picardie Jules Verne (UPJV)-Ecole Nationale Supérieure de Chimie de Montpellier (ENSCM)-Ecole Nationale Supérieure de Chimie de Paris - Chimie ParisTech-PSL (ENSCP), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Pau et des Pays de l'Adour (UPPA)-Institut de Chimie du CNRS (INC)-Université de Montpellier (UM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), and Université Grenoble Alpes (UGA)-Université Grenoble Alpes (UGA)

Subjects
[CHIM.GENI]Chemical Sciences/Chemical engineering, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, Renewable Energy, Sustainability and the Environment, Materials Chemistry, Electrochemistry, [CHIM.MATE]Chemical Sciences/Material chemistry, Condensed Matter Physics, [SPI.MAT]Engineering Sciences [physics]/Materials, Surfaces, Coatings and Films, Electronic, Optical and Magnetic Materials
Abstract

International audience; In this second part of this series of papers, the use of two physics-based models to analyze the discharge performance of a set of high-energy-density electrodes is discussed. The measured set of parameters from the first part is implemented into these models. First, the regular Newman pseudo-2D model shows a large discrepancy against the experimental values. Then, an extension of the Newman model considering the particle agglomeration due to the calendering effects is presented, allowing for the validation of discharge rate capabilities of all studied industry-grade electrodes with different electrolytes. At the agglomerate scale, the model accounts for both the ionic transport in sub-pores and the inter-particle solid diffusion. The simulation results from this work demonstrate that increasing the electrode loading and/or density leads to either a higher fraction of sub-pores (at the expense of that of macropores) or larger porous agglomerate size, resulting in a poor rate performance. The model analysis suggests that a substantial gain in performance at high C-rates is expected if agglomeration effects are mitigated in these high-energy electrodes.

Published
2022
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6. Mathematical Modeling of Energy-Dense NMC Electrodes: I. Determination of Input Parameters

Author

Tuan-Tu Nguyen, Bruno Delobel, Maxime Berthe, Benoît Fleutot, Arnaud Demortière, Charles Delacourt, Laboratoire réactivité et chimie des solides - UMR CNRS 7314 (LRCS), Université de Picardie Jules Verne (UPJV)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Technocentre Renault [Guyancourt], RENAULT, Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 (IEMN), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), Plateforme de Caractérisation Multi-Physiques - IEMN (PCMP - IEMN), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Université Polytechnique Hauts-de-France (UPHF)-JUNIA (JUNIA), Réseau sur le stockage électrochimique de l'énergie (RS2E), Aix Marseille Université (AMU)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Collège de France (CdF (institution))-Université de Picardie Jules Verne (UPJV)-Ecole Nationale Supérieure de Chimie de Paris - Chimie ParisTech-PSL (ENSCP), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Pau et des Pays de l'Adour (UPPA)-Institut de Chimie du CNRS (INC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Université Grenoble Alpes (UGA)-Nantes Université (Nantes Univ)-Université de Montpellier (UM)-Ecole Nationale Supérieure de Chimie de Montpellier (ENSCM), Université de Montpellier (UM), Advanced Lithium Energy Storage Systems - ALISTORE-ERI (ALISTORE-ERI), Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), The authors would like to acknowledge French National Association for Research and Technology (ANRT) for partially supporting the funding of this research work. T.T. Nguyen was supported by Renault Group for his PhD Project. This work has been supported by the IEMN-PCMP-PCP platform and by the RENATECH network., PCMP PCP, Renatech Network, Laboratoire réactivité et chimie des solides - UMR CNRS 7314 [LRCS], Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN], Université de Nantes (UN)-Aix Marseille Université (AMU)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Collège de France (CdF (institution))-Université de Picardie Jules Verne (UPJV)-Ecole Nationale Supérieure de Chimie de Montpellier (ENSCM)-Ecole Nationale Supérieure de Chimie de Paris - Chimie ParisTech-PSL (ENSCP), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Pau et des Pays de l'Adour (UPPA)-Institut de Chimie du CNRS (INC)-Université de Montpellier (UM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), and Université Grenoble Alpes (UGA)-Université Grenoble Alpes (UGA)

Subjects
[SPI]Engineering Sciences [physics], [CHIM.GENI]Chemical Sciences/Chemical engineering, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, Renewable Energy, Sustainability and the Environment, Physics::Plasma Physics, Materials Chemistry, Electrochemistry, Condensed Matter Physics, [SPI.MAT]Engineering Sciences [physics]/Materials, Surfaces, Coatings and Films, Electronic, Optical and Magnetic Materials
Abstract

Physics-based models of the Li-ion battery are promising to decipher and quantify the electrode limitations, thereby providing valuable insights for choosing the optimal electrode design for a specific application. However, to obtain relevant results from the models, a reliable set of input parameters is required. This work presents a combined experimental/modeling approach relying on the Newman pseudo-2D model for a complete characterization of a set of LiNi0.5Mn0.3Co0.2O2 electrodes. Intrinsic properties of the active materials are determined and validated using low-loading electrodes having negligible porous-electrode limitations. Then, high-energy-density electrode properties are characterized using appropriate experimental methods, which are widely reported in the literature. In the second part of this series of papers, parameters obtained from this part serve as input parameters in the Newman pseudo-2D model as well as in its extension in order to simulate the rate capability during discharge of the aforementioned set of high-energy-density electrodes. List of symbols a i m i 2 / m PE 3 interfacial surface area of phase i c s , surf mol m − 3 concentration at the surface of the AM particle c s , max mol m − 3 maximum concentration of intercalated Li in AM particle c s mol m − 3 solid-phase Li concentration within the AM particle c ¯ s mol m − 3 local volume-averaged solid Li concentration of AM phase within the PA c mol m − 3 salt concentration in a binary electrolyte d 50 μ m median diameter of AM particles D m 2 s − 1 bulk diffusion coefficient of the liquid phase D s m 2 s − 1 diffusion coefficient of Li in the AM particles F C mol − 1 Faraday’s constant i coexisting phase presented in the PE i n 0 A m − 2 exchange current density i Li 0 A m − 2 exchange current density at the Li foil I app A / m CC 2 discharge current density j n mol / m AM 2 · s pore-wall flux across the sandwich k 0 mol m 2 · s · mol m − 3 1.5 − 1 reaction rate constant of the AM k 0 , Li mol m 2 · s · mol m − 3 0.5 − 1 reaction rate constant of Li foil L el μ m PE thickness L sep μ m separator thickness Q th Ah kg − 1 electrode theoretical capacity R J mol · K − 1 ideal gas constant r μ m radial dimension along the AM particle T K absolute temperature t s time t + 0 transference number of Li+ in the electrolyte with respect to the solvent velocity U V equilibrium potential of the AM Δ V V voltage drop between the two inner contacts in the μ4-probe experiment x μ m dimension across the sandwich x 0 initial stoichiometry Greek Symbols α thermodynamic factor β charge transfer coefficient ε m elyte 3 / m PE 3 PE porosity ε sep m elyte 3 / m sep 3 separator porosity κ eff S m − 1 effective ionic conductivity of the liquid phase ρ el g cm − 3 electrode density σ eff S m − 1 effective electronic conductivity of the solid phase of the electrode τ Br tortuosity factor by Bruggeman τ e electrode tortuosity factor τ sep tortuosity factor of the separator Φ 1 , Li V electric potential at Li foil Φ i V electric potential of phase i

Published
2022
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7. Towards a Local In situ X‐ray Nano Computed Tomography under Realistic Cycling Conditions for Battery Research

Author

Zeliang Su, Tuan‐Tu Nguyen, Christophe Le Bourlot, François Cadiou, Arash Jamali, Vincent De Andrade, Alejandro A. Franco, Arnaud Demortière, Laboratoire réactivité et chimie des solides - UMR CNRS 7314 (LRCS), Université de Picardie Jules Verne (UPJV)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Réseau sur le stockage électrochimique de l'énergie (RS2E), Aix Marseille Université (AMU)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Collège de France (CdF (institution))-Université de Picardie Jules Verne (UPJV)-Ecole Nationale Supérieure de Chimie de Paris - Chimie ParisTech-PSL (ENSCP), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Pau et des Pays de l'Adour (UPPA)-Institut de Chimie du CNRS (INC)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Université Grenoble Alpes (UGA)-Nantes Université (Nantes Univ)-Université de Montpellier (UM)-Ecole Nationale Supérieure de Chimie de Montpellier (ENSCM), Université de Montpellier (UM), Matériaux, ingénierie et science [Villeurbanne] (MATEIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), and Argonne National Laboratory [Lemont] (ANL)

Subjects
General Medicine, [CHIM.MATE]Chemical Sciences/Material chemistry, [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det]
Abstract

International audience; Performing high resolution 3D imaging with transmission X-ray microscopy (TXM) in an in situ battery system is extremely challenging. We show herein a method to conduct an in situ nano-X ray Computed Tomography (nano-XCT) experiment assisted by the in-line phase contrast technique for low Z elements in batteries. A carbon-based O2-cathode of Li-O2 battery was used for this investigation and the time-resolved 3D volumes were obtained and successfully reconstructed. Our approach is to perform a nano-XCT by zooming directly into a laser-shaped area not larger than 50 μm in an in-house in situ cell. The rotation axis of the tomography of this cell being parallel to the plan of the electrode, a specific method of preparing the electrode by laser is required. This method is essential in this approach, which allows to minimize the dead angles, align the beam and adjust the focus plan. We have demonstrated in situ observation of the difficult light species, lithium peroxide, in a lithium-oxygen battery. The time-resolved 3D volumes obtained showed a force pushing the analyzed area out of the field of view. This is due to the thickening of the lithium foil during the electrochemical process. Although X-ray Computed-Tomography (XCT) is often considered non-invasive and beneficial for the development of an in situ experiment for battery research, here we discuss the effect of the focused beam at the nanoscale with a focusing X-ray beam. The current contribution opens possibilities of characterization of battery materials by local in situ nano-XCT under realistic cycling conditions.

Published
2022
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8. Adorym: A multi-platform generic x-ray image reconstruction framework based on automatic differentiation

Author

Rémi Tucoulou, Qiaoling Jin, Ming Du, Junjing Deng, Arnaud Demortière, Tuan Tu Nguyen, Vincent De Andrade, Saugat Kandel, Chris Jacobsen, Xiaojing Huang, and Argonne National Laboratory [Lemont] (ANL)

Subjects
Automatic differentiation, Computer science, Image quality, Holography, FOS: Physical sciences, 78-04, 02 engineering and technology, Iterative reconstruction, 01 natural sciences, Article, law.invention, 010309 optics, Optics, law, 0103 physical sciences, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, [CHIM]Chemical Sciences, Computer vision, Mathematics - Numerical Analysis, ComputingMilieux_MISCELLANEOUS, [PHYS]Physics [physics], business.industry, Image and Video Processing (eess.IV), Ranging, Numerical Analysis (math.NA), Computational Physics (physics.comp-ph), Electrical Engineering and Systems Science - Image and Video Processing, 021001 nanoscience & nanotechnology, Object (computer science), Atomic and Molecular Physics, and Optics, Range (mathematics), Parallel processing (DSP implementation), Artificial intelligence, 0210 nano-technology, business, Physics - Computational Physics
Abstract

We describe and demonstrate an optimization-based X-ray image reconstruction framework called Adorym. Our framework provides a generic forward model, allowing one code framework to be used for a wide range of imaging methods ranging from near-field holography to fly-scan ptychographic tomography. By using automatic differentiation for optimization, Adorym has the flexibility to refine experimental parameters including probe positions, multiple hologram alignment, and object tilts. It is written with strong support for parallel processing, allowing large datasets to be processed on high-performance computing systems. We demonstrate its use on several experimental datasets to show improved image quality through parameter refinement. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Published
2020
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9. Resolving the Discrepancy in Tortuosity Determination for Battery Porous Electrodes Via a Numerical Approach

Author

Tuan-Tu Nguyen, Arnaud Demortière, Charles Delacourt, Bruno Delobel, Benoit Fleutot, Samuel J. Cooper, Laboratoire réactivité et chimie des solides - UMR CNRS 7314 (LRCS), Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC), Réseau sur le stockage électrochimique de l'énergie (RS2E), Université de Picardie Jules Verne (UPJV)-Institut de Chimie du CNRS (INC)-Aix Marseille Université (AMU)-Université de Pau et des Pays de l'Adour (UPPA)-Université de Nantes (UN)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Ecole Nationale Supérieure de Chimie de Paris - Chimie ParisTech-PSL (ENSCP), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Collège de France (CdF (institution))-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Université Grenoble Alpes (UGA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Supérieure de Chimie de Montpellier (ENSCM), Université de Nantes (UN)-Aix Marseille Université (AMU)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Collège de France (CdF (institution))-Université de Picardie Jules Verne (UPJV)-Ecole Nationale Supérieure de Chimie de Montpellier (ENSCM)-Ecole Nationale Supérieure de Chimie de Paris - Chimie ParisTech-PSL (ENSCP), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Pau et des Pays de l'Adour (UPPA)-Institut de Chimie du CNRS (INC)-Université de Montpellier (UM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Université Grenoble Alpes (UGA), and Université de Picardie Jules Verne (UPJV)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Battery (electricity), [PHYS]Physics [physics], Tomographic reconstruction, Materials science, Diffusion equation, Mathematical analysis, Tortuosity, [SPI.MAT]Engineering Sciences [physics]/Materials, [CHIM.GENI]Chemical Sciences/Chemical engineering, Macroscopic scale, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, [CHIM]Chemical Sciences, Diffusion (business), Porous medium, Microscale chemistry, ComputingMilieux_MISCELLANEOUS
Abstract

The tortuosity factor of porous electrode microstructure is a crucial input parameter for numerical models of batteries as it strongly influences the electrode performance. As such, it is very important to have a method to determine this parameter accurately, based on a definition that reflects the design of the cell. Various experimental methods have been developed for either directly measuring or indirectly inferring the tortuosity factor; however, numerical approaches, based on 3D image data, are now gaining interest in the battery community, due to the advances in nanoscale tomographic imaging methods. The standard definition of the tortuosity factor solves the Fick diffusion equation at steady-state, i.e., between two parallel constant-value boundaries. Although this approach has been widely used for porous materials, including both electronic insulators (e.g., a battery separator), and electronic conductors (e.g., battery porous electrodes), it may be the case that the definition needs to be adjusted depending on the scenario being observed. In this study, we intend to give an insight into the appropriate way to determine the tortuosity factor of battery porous electrodes and the impact of various tortuosity determination methods is investigated. An additional module that relies on the symmetric cell method [1] [2] was implemented in the TauFactor software package [3] to compare with the already-implemented diffusion-based method [4]. This symmetric cell method refers to the measurement of the ionic current distribution inside the pores using AC impedance based on a symmetric cell setup. Figure 1 shows the workflow for tortuosity determination applied in this study. The integration of this module in TauFactor might be interesting for tortuosity determination at the microscale since it is the same method as at macroscopic scale observed in various experimental approaches. Figure 1 . Illustration of the workflow for tortuosity determination applied in this work. The module recently implemented in TauFactor allows calculation based directly on tomographic data in symmetric cell configuration, and generates a simulated impedance spectrum. A macroscopic model such as TLM or Newman’s model is used to extract the tortuosity value of the electrode. References: [1] Landesfeind, J. et al.; J. Electrochem. Soc. 2016, 163 (7), A1373–A1387 [2] Malifarge, S. et al.; J. Electrochem. Soc. 2017, 164 (11), E3329–E3334 [3] Cooper, S. J. et al.; SoftwareX 2016, 5, 203–210 [4] Cooper, S. J. et al.; Electrochimica Acta 251 (2017) 681–689 [5] Pouraghajan, F. et al.; J. Electrochem. Soc. 2018, 165 Figure 1

Published
2020
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10. 3D Quantification of Microstructural Properties of LiNi 0.5 Mn 0.3 Co 0.2 O 2 High‐Energy Density Electrodes by X‐Ray Holographic Nano‐Tomography

Author

Julie Villanova, Arnaud Demortière, Bruno Delobel, Zeliang Su, Benoit Fleutot, Charles Delacourt, Tuan-Tu Nguyen, and Rémi Tucoulou

Subjects
Materials science, Renewable Energy, Sustainability and the Environment, 020209 energy, Holography, Analytical chemistry, X-ray, 02 engineering and technology, 021001 nanoscience & nanotechnology, law.invention, law, Electrode, Nano, 0202 electrical engineering, electronic engineering, information engineering, Energy density, Electrode microstructure, General Materials Science, Tomography, 0210 nano-technology
Published
2021
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