G
Guozhen Shen
Researcher at Chinese Academy of Sciences
Publications - 445
Citations - 30312
Guozhen Shen is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Nanowire & Transmission electron microscopy. The author has an hindex of 84, co-authored 422 publications receiving 23992 citations. Previous affiliations of Guozhen Shen include University of Southern California & Chinese PLA General Hospital.
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Recent Advances in Perovskite Photodetectors for Image Sensing
TL;DR: A comprehensive overview of the recent advances in perovskite photodetectors for image sensing is provided in this paper, where the key performance parameters and the basic device types of photoderectors are briefly introduced.
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Nanowire-assembled Co3O4@NiCo2O4 architectures for high performance all-solid-state asymmetric supercapacitors
TL;DR: In this article, a rational design was proposed to fabricate two-dimensional (2D) Co3O4@NiCo2O4 architectures composed of nanowires on a Ni foam substrate via two steps of hydrothermal processing.
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Single‐Crystalline p‐Type Zn3As2 Nanowires for Field‐Effect Transistors and Visible‐Light Photodetectors on Rigid and Flexible Substrates
Gui Chen,Zhe Liu,Bo Liang,Gang Yu,Zhong Xie,Hongtao Huang,Bin Liu,Xianfu Wang,Di Chen,Ming-Qiang Zhu,Guozhen Shen +10 more
TL;DR: In this article, the synthesis of single-crystalline Zn3As2 nanowires (NWs) via a simple chemical vapor deposition method was reported, and highperformance single Zn 3As2 NW field effect transistors (FETs) on rigid SiO2/Si substrates and visible-light photodetectors on rigid and flexible substrates were fabricated and studied.
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Hydrothermally Grown ZnO Micro/Nanotube Arrays and Their Properties.
TL;DR: The optical and wettability properties of aligned zinc oxide micro/nanotube arrays, which were synthesized on zinc foil via a simple hydrothermal method, show typical n-type semiconducting behavior.
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PRINTR: Prediction of RNA binding sites in proteins using SVM and profiles
TL;DR: A novel method for the prediction of protein residues that interact with RNA using support vector machine (SVM) and position-specific scoring matrices (PSSMs) and yields a Matthews correlation coefficient (MCC) of 0.432 by a 7-fold cross-validation, which is the best among all previous reported RNA-binding sites prediction methods.