scispace - formally typeset
Z

Zhen Li

Researcher at Wuhan University

Publications -  3347
Citations -  95191

Zhen Li is an academic researcher from Wuhan University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 127, co-authored 1712 publications receiving 71351 citations. Previous affiliations of Zhen Li include Tsinghua University & Hong Kong University of Science and Technology.

Papers
More filters
Journal ArticleDOI

Hydrothermal route for cutting graphene sheets into blue-luminescent graphene quantum dots

TL;DR: This work reports on a novel and simple hydrothermal approach for the cutting of GSs into surface-functionalized GQDs, which were found to exhibit bright blue photoluminescence (PL), which has never been observed in GSs and GNRs owing to their large lateral sizes.
Journal ArticleDOI

Stabilizing Perovskite Structures by Tuning Tolerance Factor: Formation of Formamidinium and Cesium Lead Iodide Solid-State Alloys

TL;DR: In this article, the effect of alloying FA0.85Cs0.15PbI3 with CsPbIsI3 was investigated, and it was shown that the effective tolerance factor can be tuned and the stability of the photoactive α-phase of the mixed solid-state perovskite alloys FA1-xCsxPbisI3 is enhanced.
Journal ArticleDOI

Graphene‐On‐Silicon Schottky Junction Solar Cells

TL;DR: Graphene applications are just starting, and current investigations are on a number of areas such as composites, nanoelectronics, and transparent electrodes, where a continuous single-layer graphene fi lm could retain high conductivity at very low (atomic) thickness, and avoid contact resistance that occurs in a carbon nanotubes between interconnected nanotube bundles.
Journal ArticleDOI

Hollow Carbon Nanofibers Filled with MnO2 Nanosheets as Efficient Sulfur Hosts for Lithium-Sulfur Batteries.

TL;DR: A new physical and chemical entrapment strategy is based on a highly efficient sulfur host, namely hollow carbon nanofibers filled with MnO2 nanosheets, which efficiently prevents polysulfide dissolution in Lithium-sulfur batteries.
Journal ArticleDOI

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model.

TL;DR: A new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks that greatly outperforms existing methods and leads to much more accurate contact-assisted folding.