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Abstract Achieving durable lithium (Li) metal anodes in liquid electrolytes remains challenging, primarily due to the instability of the formed solid‐electrolyte interphases (SEIs). Modulating the Li‐ion solvation structures is pivotal in forming a stable SEI for stabilizing Li metal anodes. Here a strategy is developed to fine‐tune the Li‐ion solvation structures through enhanced dipole–dipole interactions between the Li‐ion‐coordinated solvent and the non‐Li‐ion‐coordinating diluent, for creating a stable SEI in the developed binary salt electrolyte. The enhanced dipole–dipole interactions weaken the coordination between Li‐ions and the solvents while strengthening the interaction between Li‐ions and dual anions, thereby facilitating the Li‐ion transport and a robust anion‐derived SEI with a distinct bilayer structure. Consequently, the developed electrolyte exhibited exceptional electrochemical performance in high energy‐density Li||LiNi0.8Mn0.1Co0.1O2 (NMC811) cells, with long calendar life, stable cyclability at 1 C, and reliable operation between 25 and −20 °C, and it also demonstrat remarkable cycling stability for a Li||NMC811 pouch cell with projected energy density of 402 Wh kg−1, maintaining 80% capacity retention over 606 cycles under practical conditions.
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Deep Learning Analysis of Solid‐Electrolyte Interphase Microstructures in Lithium‐Ion Batteries
Abstract A comprehensive understanding of the solid‐electrolyte interphase (SEI) in lithium‐ion batteries is crucial for improving energy efficiency, battery performance, and safety. In this study, a transformer‐based instance segmentation framework, integrating deep convolutional neural networks is introduced with a feature pyramid network (FPN), to quantitatively analyze High‐Resolution Transmission Electron Microscopy (HRTEM) images and explain the complex microstructural features of the SEI. The model is trained on a dataset of simulated HRTEM images generated using Density Functional Theory (DFT)‐optimized grain boundary (GB) structures and calibrated with experimental microscope parameters. The model achieves robust segmentation performance, with training and validation mean intersection over union (mIOU) values of 0.98 and 0.96, respectively. On unseen test data, the model attains mean area match (AM) scores of 91.4% for GBs, 92.3% for Li2CO3, 91.7% for LiF, 88.7% for LiOH, and 88.6% for Li2O. These quantitative results highlight the model's high fidelity and its ability to capture subtle variations in crystallographic orientations and material contrasts. By enabling detailed, statistically grounded segmentation of SEI components, the approach offers valuable insights into ion transport and degradation mechanisms, paving the way for more resilient and efficient energy storage solutions.
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- Award ID(s):
- 2311104
- PAR ID:
- 10640194
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Materials Interfaces
- Volume:
- 12
- Issue:
- 21
- ISSN:
- 2196-7350
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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