Silicon-based anode materials experience significant volume changes and low conductivity during the lithiation process, which severely hinders their successful application in lithium-ion batteries. Reducing the size of silicon particles and effectively combining them with carbon-based materials are considered the main strategies to enhance the lithium-ion storage performance of silicon-based anodes. In this study, we employed a "bottom-up" strategy to synthesize Si@C anode materials by cross-linking octa-aminopropyl polyhedral oligomeric silsesquioxane (NH-POSS) with terephthalaldehyde and subsequent high-temperature treatment and low-temperature liquid reduction. The obtained nanospheres consist of ultra-thin silicon stripes embedded in a continuous carbon framework, forming a carbon-protected silicon-based anode material suitable for lithium-ion batteries. The Si@C nanospheres exhibit excellent lithium-ion storage performance. After 1000 cycles at a current density of 0.5 A g, it retains an impressive capacity of 1363 mA h g, which is more than three times the theoretical capacity of graphite and 182% of the first cycle capacity after activation (750 mA h g). This work not only provides new possibilities for the application of POSS but also broadens the design and application of advanced silicon-based anode materials in the energy storage field.
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http://dx.doi.org/10.1039/d4dt03185g | DOI Listing |
RSC Adv
January 2025
Key Laboratory of Engineering Dielectric and Applications (Ministry of Education), School of Materials Science and Engineering, Harbin University of Science and Technology Harbin 150080 P. R. China
[This corrects the article DOI: 10.1039/C9RA10485B.].
View Article and Find Full Text PDFNanoscale Horiz
January 2025
Laboratory of Advanced Materials, Shanghai Key Lab of Molecular Catalysis and Innovative Materials, Academy for Engineering & Technology, Advanced Coatings Research Center of Ministry of Education of China, Fudan University, Shanghai 200438, P. R. China.
A porous hedgehog-like CoO/NiO/graphene oxide (denoted as PHCNO/GO) microsphere was prepared by a facile solvothermal method, followed by an annealing treatment under argon atmosphere. Benefiting from the thin CoO/NiO nanosheets with a large specific surface area, abundant pores distributed between the CoO/NiO nanosheets, and GO firmly wrapped around the surface of PHCNO microspheres, the PHCNO/GO microspheres showed excellent lithium storage performance. The CoO/NiO nanosheets provided numerous active sites, achieving a high reversible specific capacity.
View Article and Find Full Text PDFAdv Mater
January 2025
Key Laboratory of Power Station Energy Transfer Conversion and System of Ministry of Education and School of Energy Power and Mechanical Engineering, and Beijing Laboratory of New Energy Storage Technology, North China Electric Power University, Beijing, 102206, China.
Co-free high-Ni layered cathode materials LiNiMeO (Me = Mn, Mg, Al, etc.) are a key part of the next-generation high-energy lithium-ion batteries (LIBs) due to their high specific capacity and low cost. However, the hindered Li kinetics and the high reactivity of Ni result in poor rate performance and unsatisfied cycling stability.
View Article and Find Full Text PDFSmall
January 2025
Department of Chemistry, Zhejiang University, Hangzhou, 310027, PR China.
Unstable solid-electrolyte interphase (SEI) film resulting from chemically active surface state and huge volume fluctuation limits the development of Si-based anode materials in lithium-ion batteries. Herein, a photo-initiated polypyrrole (PPy) coating is manufactured on Si nanoparticles to guide the in situ generation of PPy-integrated hybrid SEI film (hSEI). The hSEI film shows excellent structure stability and optimized component composition for lithium storage.
View Article and Find Full Text PDFSci Rep
January 2025
CALCE University of Maryland, College Park, MD, 20742, USA.
Remaining useful life (RUL) prediction is a crucial aspect of the prognostics health management of lithium-ion batteries (LIBs). Owing to the influence of resampling technology, particle degradation is often observed in the particle filter-based RUL prediction of LIBs, resulting in a low prediction accuracy and large uncertainty. In this paper, a novel particle flow filter with the grey model method (GM-PFF) is proposed to forecast the RUL and state of health of batteries.
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