Although graphite has been used as anodes of lithium-ion batteries (LiBs) for 30 years, its unsatisfactory energy density makes it insufficient toward some new electronic products such as unmanned aerial vehicles. Herein, in situ synthesis of nano-GeP confined in nitrogen-doped carbon (GeP@NC) fibers was designed and performed via coaxial electrospinning followed by a phosphating process. This way ensured the paper-like GeP@NC- electrode with high conductivity, high flexibility, and lightweight properties, which simultaneously solved the key scientific problems of difficulty in structural design and severe volume expansion of GeP. The inner diameter and wall thickness of the nanofibers can be effectively controlled by adjusting the size of electrospinning needles. It was suggested that the fibers not only effectively inhibited the growth of GeP, resulting in the synthesis of nano-GeP with size less than 50 nm, but also alleviated the volume expansion/agglomeration and improved the diffusion kinetics of Li in nano-GeP during cycling. The Li diffusion coefficient can be improved by reducing the inner diameter and wall thickness of the fibers. As a model system, the paper-like electrode (GeP@NC-2) with a fiber diameter of 280 nm and a wall thickness of 110 nm exhibited the best electrochemical performance. When applied as anodes in LiBs, it displayed a reversible capacity of 612 mAh g at the 600th cycle at 1 A g, while GeP@NC-0 with a solid structure only delivered 239 mAh g. Furthermore, the GeP@NC-2 also exhibited good long-term cycling stability at 5 A g, and the capacity displayed a slight difference of 221.2 and 209.0 mAh g in a voltage range of 0∼3 V and 0∼1.5 V, respectively. The well-defined synthetic approach combined with unique nanostructural design provided a meaningful reference for the rational design and development of next-generation flexible and high-performance LiB anodes.
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http://dx.doi.org/10.1021/acsami.1c07387 | DOI Listing |
J Clin Med
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Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072 Pieve Emanuele, Italy.
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Institut Universitaire de Cardiologie et de Pneumologie de Québec, Université Laval, Québec, QC G1V 4G5, Canada.
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Biofluids Laboratory, Perm National Research Polytechnic University, 614990 Perm, Russia.
Simulating the cardiac valves is one of the most complex tasks in cardiovascular modeling. As fluid-structure interaction simulations are highly computationally demanding, machine-learning techniques can be considered a good alternative. Nevertheless, it is necessary to design many aortic valve geometries to generate a training set.
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Engineering Research Center of Chinese Ministry of Education for Edible and Medicinal Fungi, Jilin Agricultural University, Changchun 130118, China.
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