scispace - formally typeset
Y

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
More filters
Journal ArticleDOI

Robustness of amorphous silicon during the initial lithiation/delithiation cycle

TL;DR: In this article, the authors studied the fracture resistance of amorphous silicon micropillars (∼2.3mm tall) after electrochemical lithiation and delithiation.
Journal ArticleDOI

Nanoporous silicon networks as anodes for lithium ion batteries

TL;DR: Nanoporous silicon (Si) networks with controllable porosity and thickness are fabricated by a simple and scalable electrochemical process, and then released from Si wafers and transferred to flexible and conductive substrates to serve as high performance Li-ion battery electrodes.
Journal ArticleDOI

Functionalization of silicon nanowire surfaces with metal-organic frameworks

TL;DR: In this article, a polycrystalline metal-organic framework (MOF) was synthesized on surface modified silicon nanowires (SiNWs) by matching of the SiNW surface functional groups with the MOF organic linker coordinating groups.
Journal ArticleDOI

Microscopic model for fracture of crystalline Si nanopillars during lithiation

TL;DR: In this article, a microscopic model is presented to describe the size-dependent fracture of crystalline Si nanopillars (NPs) during lithiation, where the initial size and spacing of the nanovoids, together with the computed facture toughness, are chosen to conform to recent experiments which showed the critical diameter of Si NPs to be 300-400nm.
Journal ArticleDOI

Patch Ordering-Based SAR Image Despeckling Via Transform-Domain Filtering

TL;DR: Experimental results with both simulated images and real SAR images demonstrate that the proposed method achieves state-of-the-art despeckling performance in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM) index, equivalent number of looks (ENLs), and ratio image.