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Article
Engineering, Multidisciplinary
Jinlong Fu et al.
Summary: This paper presents an efficient method that combines machine learning-based characterization of 2D images with statistical reconstruction of 3D microstructures. By fitting supervised machine learning models, the latent stochasticity of 2D images is mastered, and a morphology integration scheme is developed to project the 2D morphological statistics into the 3D space. Experimental results confirm that the proposed method can economically and accurately reproduce 3D microstructures that preserve the statistical characteristics, geometrical irregularities, long-distance connectivity, and anisotropy present in 2D cross-sectional images.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
Article
Engineering, Chemical
Takao Ueda
Summary: A modified 2D-3D conversion method was developed in this study to estimate the 3D size and shape characteristics of particles from measurable 2D characteristics. Experimental validation showed the accuracy of this method and successful reproduction of the repose angle in the estimated 3D particle shapes.
Article
Chemistry, Physical
Tanvir R. Tanim et al.
Summary: This study investigates the degradation mechanisms of NMC811 under extreme fast charging conditions and compares its performance with NMC532. The results show that NMC811 experiences more severe subsurface crystallographic degradation compared to NMC532, but still maintains superior performance due to its radially oriented grains and improved transport properties.
ADVANCED ENERGY MATERIALS
(2022)
Article
Chemistry, Physical
Orkun Furat et al.
Summary: To better understand the functional behavior of energy materials, it is necessary to investigate their microstructure using imaging techniques like scanning electron microscopy (SEM). However, the heterogeneous nature of active materials often requires quantification of features over large volumes, which can compromise resolution. In this study, generative adversarial networks (GANs) were used to super-resolve SEM images of cracked cathode materials, providing representative quantification of fine features.
NPJ COMPUTATIONAL MATERIALS
(2022)
Article
Chemistry, Physical
Sangwook Kim et al.
Summary: This study presents a synthetic-data-based deep learning modeling framework for rapid classification and quantification of battery aging modes, with experimental validation of the technique.
ENERGY STORAGE MATERIALS
(2022)
Article
Chemistry, Physical
Feng Tian et al.
Summary: X-ray nano-computed tomography and deep learning were used to study the structural evolution of LiNi0.8Co0.1Mn0.1O2 during cycling at 55 degrees C. Two types of intergranular cracks were observed, with the open cracks showing a significant increase in volume and a decrease in capacity retention. Further analysis revealed that migration of transition metal ions and reduction of these ions predominantly occurred in the open crack regions.
Article
Electrochemistry
Orkun Furat et al.
Summary: This study demonstrates a high-throughput approach to quantify crack evolution in lithium-ion positive electrodes using super-resolution scanning electron microscopy. The results show that while crack evolution strongly correlates with capacity fade in the first 25 cycles, it does not correlate well for the following cycles, indicating that cracking may not be the dominant cause of capacity fade throughout the cycle-life of cells.
JOURNAL OF THE ELECTROCHEMICAL SOCIETY
(2022)
Article
Energy & Fuels
David C. Robertson et al.
Summary: This study investigates the effect of electrode porosity on lithium plating by testing single-layer pouch cells. Higher temperatures are found to reduce lithium plating and improve fast-charge capacity, but also alter degradation mechanisms. Kinetic rate laws can be used to fit capacity loss and resistance increase data, with A and B constants changing with temperature and porosity.
Article
Chemistry, Physical
Orkun Furat et al.
Summary: Accurately quantifying the 3D architecture of lithium ion electrode particles is essential for understanding sub-particle lithium transport and degradation mechanisms in lithium ion batteries. This study utilized focused ion beam slicing and electron backscatter diffraction to accurately quantify intra-particle grain morphologies, identifying them using convolution neural network segmentation and developing bivariate probability density maps to show correlative relationships between morphological grain descriptors, and discussed the implications of morphological features on cell performance.
JOURNAL OF POWER SOURCES
(2021)
Article
Materials Science, Multidisciplinary
Benedikt Prifling et al.
Summary: This paper investigates the influence of 3D microstructure on the effective properties of functional materials, establishing relationships between microstructure and mass transport properties. Analytical prediction formulas, artificial neural networks, and convolutional neural networks are used for the first time to compare these three statistical learning approaches on the same dataset. The diversity and size of the dataset are crucial for determining the generality of the relationships and for robust training of convolutional neural networks.
FRONTIERS IN MATERIALS
(2021)
Article
Materials Science, Multidisciplinary
Joao P. C. Bertoldo et al.
Summary: X-Ray Computed Tomography (XCT) techniques have advanced to acquire high-resolution data quickly, requiring automated data pipelines for processing 3D images; deep learning shows potential as an alternative for segmentation pipelines, but the increasing number of available architectures may hinder wide adoption; a modular interpretation of U-Net with a parametrized architecture has shown promising results in automated data processing without human intervention in XCT.
FRONTIERS IN MATERIALS
(2021)
Article
Chemistry, Physical
Tanvir R. Tanim et al.
Summary: The study found that while cathode issues are minimal in early cycling of extreme fast charging, they begin to accelerate in later life with distinct cracking identified as a fatigue mechanism. The bulk structure of cathodes remains intact, but there is particle surface reconstruction observed, which has a less pronounced effect on cathode aging compared to cracking.
ENERGY STORAGE MATERIALS
(2021)
Article
Chemistry, Physical
Jeffery M. Allen et al.
Summary: The study develops a damage model based on NMC 532 secondary cathode particles to explore the influence of particle sizes on damage and determine charging profiles that reduce cathode fracture. It is found that small secondary particles with large grains experience significantly less damage compared to larger particles with small grains, and that most of the damage accumulates in the initial cycles.
JOURNAL OF POWER SOURCES
(2021)
Article
Chemistry, Physical
Orkun Furat et al.
Summary: This study focuses on accurately capturing the architecture of single lithium-ion electrode particles using multimodal microscopy techniques to generate virtual electrode particles with full-grain detail. The research demonstrates the possibility of creating representative single electrode particle architectures for modeling and characterization to guide synthesis approaches for particle architectures with enhanced performance.
NPJ COMPUTATIONAL MATERIALS
(2021)
Article
Computer Science, Artificial Intelligence
Steve Kench et al.
Summary: SliceGAN is a generative approach that constructs complex 3D images from 2D image examples, making it particularly useful for studying microstructured materials. It leverages high-quality 2D imaging techniques to create 3D datasets, eliminating the need for challenging 3D training data. This approach demonstrates promising potential for design optimization, with the ability to generate high-fidelity 3D datasets using a single representative 2D image.
NATURE MACHINE INTELLIGENCE
(2021)
Article
Electrochemistry
H. Michael et al.
Summary: Graphite is a commonly used anode material in lithium-ion batteries, and understanding the dimensional changes during cycling can help predict electrochemical performance loss. This study combined microscopy tools and electrochemical dilatometry to analyze the changes in thickness of graphite electrodes during galvanostatic cycling. X-ray CT imaging revealed that irreversible dilation of the electrode was mainly caused by delamination, while electrode porosity remained relatively unchanged.
JOURNAL OF THE ELECTROCHEMICAL SOCIETY
(2021)
Review
Chemistry, Physical
Ying Zhao et al.
JOURNAL OF POWER SOURCES
(2019)
Article
Materials Science, Multidisciplinary
Orkun Furat et al.
MICROSCOPY AND MICROANALYSIS
(2019)
Article
Materials Science, Multidisciplinary
Matthias Neumann et al.
MATERIALS CHARACTERIZATION
(2019)
Article
Materials Science, Multidisciplinary
Orkun Furat et al.
MICROSCOPY AND MICROANALYSIS
(2018)
Article
Engineering, Chemical
Takao Ueda et al.
ADVANCED POWDER TECHNOLOGY
(2016)
Article
Microscopy
Oluwadamilola O. Taiwo et al.
JOURNAL OF MICROSCOPY
(2016)
Article
Statistics & Probability
Johanna F. Ziegel et al.
SCANDINAVIAN JOURNAL OF STATISTICS
(2015)
Article
Multidisciplinary Sciences
Stefan van der Walt et al.
Article
Electrochemistry
J Christensen et al.
JOURNAL OF THE ELECTROCHEMICAL SOCIETY
(2006)
Article
Mathematical & Computational Biology
A Hobolth