Electron Backscatter Diffraction for Investigating Lithium-Ion Electrode Particle Architectures

Quinn, Alexander; Moutinho, H. R.; Usseglio-Viretta, François; Verma, Ankit; Smith, Kandler; Keyser, Matthew; Finegan, Donal P.

doi:10.1016/j.xcrp.2020.100137

Cited by 38 publications

(59 citation statements)

References 72 publications

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“…6 ). Moreover, we also performed electron backscattered diffraction (EBSD) experiments on the LMO-TB sample since EBSD is a complementary technique when exploring defects and surveying the orientation distribution of target materials 34 . Given that the twinning structure can be clearly observed from the cross-section of the LMO-TB particles via EBSD, as illustrated in Supplementary Figs.…”

Section: Resultsmentioning

confidence: 99%

Twin boundary defect engineering improves lithium-ion diffusion for fast-charging spinel cathode materials

et al. 2021

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Defect engineering on electrode materials is considered an effective approach to improve the electrochemical performance of batteries since the presence of a variety of defects with different dimensions may promote ion diffusion and provide extra storage sites. However, manipulating defects and obtaining an in-depth understanding of their role in electrode materials remain challenging. Here, we deliberately introduce a considerable number of twin boundaries into spinel cathodes by adjusting the synthesis conditions. Through high-resolution scanning transmission electron microscopy and neutron diffraction, the detailed structures of the twin boundary defects are clarified, and the formation of twin boundary defects is attributed to agminated lithium atoms occupying the Mn sites around the twin boundary. In combination with electrochemical experiments and first-principles calculations, we demonstrate that the presence of twin boundaries in the spinel cathode enables fast lithium-ion diffusion, leading to excellent fast charging performance, namely, 75% and 58% capacity retention at 5 C and 10 C, respectively. These findings demonstrate a simple and effective approach for fabricating fast-charging cathodes through the use of defect engineering.

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Section: Resultsmentioning

confidence: 99%

Twin boundary defect engineering improves lithium-ion diffusion for fast-charging spinel cathode materials

et al. 2021

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“…The particle size distribution of ZSO:Eu 3+ analyzed by SEM was determined to be approximately 300–400 nm. The crystallite size was also estimated from the XRD (410) peak using the Scherrer formula [ 50 , 51 ]. The calculated crystallite size of ZSO:Eu 3+ was 430–437 nm, which was comparable to the result of SEM analysis.…”

Section: Resultsmentioning

confidence: 99%

Red Zn2SiO4:Eu3+ and Mg2TiO4:Mn4+ nanophosphors for on-site rapid optical detections: Synthesis and characterization

Yang¹,

Zhang²,

Huang

et al. 2021

Appl. Phys. A

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This study reports the synthesis and characterization of the red nanophosphors Zn 2 SiO 4 :Eu 3+ (ZSO:Eu 3+ ) and Mg 2 TiO 4 :Mn 4+ (MTO:Mn 4+ ). The use of phosphors as a fluorescence label for lateral flow immunochromatographic assay (LFIA) has also been described. The optimal photoluminescence (PL) for ZSO:Eu 3+ was obtained when it was synthesized with 7 mol% of Eu 3+ and annealed at 1100 °C for 1 h. Long fluorescence lifetime (1.01 ms), high activation energy E a (0.28 eV), and low PL degeneration (10% at 110 °C) are the characteristics of ZSO:Eu 3+ . MTO:Mn 4+ also exhibited high PL intensity along with a high E a of 0.32 eV. The emission wavelengths of phosphors are biocompatible with the optical bio-window of tissues. When human immunoglobulin G (human IgG) at a constant concentration of 100 μg/mL was used for detection, the PL ratios of the test line to the control line were 2.15 and 2.28 for the ZSO:Eu 3+ -and MTO:Mn 4+ -labeled LFIA, respectively. Thus, the ZSO:Eu 3+ and MTO:Mn 4+ nanophosphors are capable of human IgG recognition and are the promising candidates as fluorescent labels for on-site rapid optical biodetection.

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“…The resolution of EBSD is dependent on the spot size of the electron beam as well as the size of the steps between each measurement, and since images are reconstructed from a series of point measurements the field of view is only limited by the time that the user is willing to commit to the measurement and perhaps their data storage capabilities. Quinn et al [37] applied EBSD to NMC particles with a resolution of about 50 nm (Figure 2a) and quantified the orientation of grains within NMC particles while also showing through basic modeling that the orientation of grains within particles will greatly affect the transport dynamics of lithium during operation. While this work presented opportunities for EBSD in 2D, gathering the grain information in 3D is essential to accurately model transport within full particles.…”

Section: Laboratory-based Techniquesmentioning

confidence: 99%

“…Correlative FIB-SEM, EDS, and TOF-SIMS was demonstrated by Sui et al, [40] and showed the power of these techniques for identifying lithium trapping sites within particles and heterogeneous elemental distributions within particles that would affect lithium transport and rate of degradation of cells. [37] Copyright 2020, Elsevier. b) 3D image of the grain structure within an NMC particle quantified using FIB-EBSD.…”

Section: Laboratory-based Techniquesmentioning

confidence: 99%

Guiding the Design of Heterogeneous Electrode Microstructures for Li‐Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning

Zhu

Finegan

et al. 2021

Advanced Energy Materials

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Electrochemical and mechanical properties of lithium‐ion battery materials are heavily dependent on their 3D microstructure characteristics. A quantitative understanding of the role played by stochastic microstructures is critical for the prediction of material properties and for guiding synthesis processes. Furthermore, tailoring microstructure morphology is also a viable way of achieving optimal electrochemical and mechanical performances of lithium‐ion cells. To facilitate the establishment of microstructure‐resolved modeling and design methods, a review covering spatially and temporally resolved imaging of microstructure and electrochemical phenomena, microstructure statistical characterization and stochastic reconstruction, microstructure‐resolved modeling for property prediction, and machine learning for microstructure design is presented here. The perspectives on the unresolved challenges and opportunities in applying experimental data, modeling, and machine learning to improve the understanding of materials and identify paths toward enhanced performance of lithium‐ion cells are presented.

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Electron Backscatter Diffraction for Investigating Lithium-Ion Electrode Particle Architectures

Cited by 38 publications

References 72 publications

Twin boundary defect engineering improves lithium-ion diffusion for fast-charging spinel cathode materials

Twin boundary defect engineering improves lithium-ion diffusion for fast-charging spinel cathode materials

Red Zn2SiO4:Eu3+ and Mg2TiO4:Mn4+ nanophosphors for on-site rapid optical detections: Synthesis and characterization

Guiding the Design of Heterogeneous Electrode Microstructures for Li‐Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning

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