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Yi Cui

Researcher at Stanford University

Publications -  1109
Citations -  245406

Yi Cui is an academic researcher from Stanford University. The author has contributed to research in topics: Anode & Lithium. The author has an hindex of 220, co-authored 1015 publications receiving 199725 citations. Previous affiliations of Yi Cui include KAIST & University of California, Berkeley.

Papers
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Journal ArticleDOI

A novel battery scheme: Coupling nanostructured phosphorus anodes with lithium sulfide cathodes

TL;DR: In this article, the authors used a P/C nanocomposite anode and paired it with a Li2S coated carbon nanofiber cathode to achieve remarkable specific capacity, rate and cycling performances.
Patent

Conducting formation cycles

TL;DR: In this article, the authors proposed methods of preparing a lithium ion cell including forming the cell by charging the battery to at least about 5% of the theoretical capacity of the negative electrode, holding the battery in a partially charged state for at least 0.5 hours, and discharging the battery.
Patent

Water sterilization devices and uses thereof

TL;DR: In this article, a water sterilization device is configured to provide passage of a fluid stream through the first porous electrode and the second porous electrode, and an inactivation efficiency of pathogens in the fluid stream is at least about 99.9% or at least approximately 99.95%.
Journal ArticleDOI

Graphene coating on silicon anodes enabled by thermal surface modification for high-energy lithium-ion batteries

TL;DR: In this paper , the effect of silicon surface properties on carbon coating morphology and the consequent silicon cycling stability have not been clearly elucidated, but it is shown that thermal oxidation of the silicon anodes followed by chemical vapor deposition of carbonaceous precursors leads to a well-ordered graphene coating, whereas disordered graphite coating is formed on the native silicon surface.
Journal ArticleDOI

Automatic multiorgan segmentation in CT images of the male pelvis using region-specific hierarchical appearance cluster models.

TL;DR: The authors have developed an efficient and reliable method for automatic segmentation of multiple organs in the male pelvis that should be useful for treatment planning and adaptive replanning for prostate cancer radiotherapy.