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Su Y, Lei X, Han Z, Liu H, Xiao J, Su Y, Ren S, Lin Y, Hu Q, Yang R, Zhou G, Su D, Zhang Y. Structural Reversibility of Nanoscaled Sn Anodes. NANO LETTERS 2024; 24:5332-5341. [PMID: 38634554 DOI: 10.1021/acs.nanolett.4c01183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
Alloying-type anode materials provide high capacity for lithium-ion batteries; however, they suffer pulverization problems resulting from the volume change during cycling. Realizing the cycling reversibility of these anodes is therefore critical for sustaining their electrochemical performance. Here, we investigate the structural reversibility of Sn NPs during cycling at atomic-level resolution utilizing in situ high-resolution TEM. We observed a surprisingly near-perfect structural reversibility after a complete cycle. A three-step phase transition happens during lithiation, accompanied by the generation of a significant number of defects, grain boundaries, and up to 202% volume expansion. In subsequent delithiation, the volume, morphology, and crystallinity of the Sn NPs were restored to their initial state. Theoretical calculations show that compressive stress drives the removal of vacancies generated within the NPs during delithiation, therefore maintaining their intact morphology. This work demonstrates that removing vacancies during cycling can efficiently improve the structural reversibility of high-capacity anode materials.
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Affiliation(s)
- Yi Su
- State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics, Tsinghua University, Beijing 100084, China
| | - Xincheng Lei
- National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China
| | - Zhen Han
- National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China
| | - Haowen Liu
- State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics, Tsinghua University, Beijing 100084, China
| | - Jianhua Xiao
- State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics, Tsinghua University, Beijing 100084, China
| | - Yipeng Su
- State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics, Tsinghua University, Beijing 100084, China
| | - Shuaiyang Ren
- State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics, Tsinghua University, Beijing 100084, China
| | - Yitao Lin
- State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics, Tsinghua University, Beijing 100084, China
| | - Qingmiao Hu
- Shi-Changxu Innovation Center for Advanced Materials, Institute of Metal Research, Chinese Academy of Sciences, Shenyang 110016, China
| | - Rui Yang
- Shi-Changxu Innovation Center for Advanced Materials, Institute of Metal Research, Chinese Academy of Sciences, Shenyang 110016, China
| | - Gang Zhou
- Shi-Changxu Innovation Center for Advanced Materials, Institute of Metal Research, Chinese Academy of Sciences, Shenyang 110016, China
| | - Dong Su
- National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China
| | - Yuegang Zhang
- State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics, Tsinghua University, Beijing 100084, China
- Frontier Science Center for Quantum Information, Beijing 100084, China
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Xue Y, Li Y, Zhang K, Yang F. A Physics-inspired Neural Network to Solve Partial Differential Equations – Application in Diffusion-induced Stress. Phys Chem Chem Phys 2022; 24:7937-7949. [DOI: 10.1039/d1cp04893g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Analyzing and predicting diffusion-induced stress is of paramount importance in understanding structural durability of lithium- and sodium-ion batteries, which generally requires to solve initial-boundary value problems, involving the partial differential...
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Long-Term Failure Mechanisms of Thermal Barrier Coatings in Heavy-Duty Gas Turbines. COATINGS 2020. [DOI: 10.3390/coatings10111022] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Thermal barrier coatings serve as thermal insulation and antioxidants on the surfaces of hot components. Different from the frequent thermal cycles of aero-engines, a heavy-duty gas turbine experiences few thermal cycles and continuously operates with high-temperature gas over 8000 h. Correspondingly, their failure mechanisms are different. The long-term failure mechanisms of the thermal barrier coatings in heavy-duty gas turbines are much more important. In this work, two long-term failure mechanisms are reviewed, i.e., oxidation and diffusion. It is illustrated that the growth of a uniform mixed oxide layer and element diffusion in thermal barrier coatings are responsible for the changes in mechanical performance and failures. Moreover, the oxidation of bond coat and the interdiffusion of alloy elements can affect the distribution of elements in thermal barrier coatings and then change the phase component. In addition, according to the results, it is suggested that suppressing the growth rate of uniform mixed oxide and oxygen diffusion can further prolong the service life of thermal barrier coatings.
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