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Fei Zhou

Researcher at Harbin Institute of Technology

Publications -  47
Citations -  556

Fei Zhou is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Chemistry & Graphene. The author has an hindex of 8, co-authored 26 publications receiving 262 citations. Previous affiliations of Fei Zhou include University of California, Berkeley.

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Metallic FePSe3 nanoparticles anchored on N-doped carbon framework for All-pH hydrogen evolution reaction

TL;DR: In this article, a high performance electrcatalyst based on ternary iron phosphoselenide (FePSe3) nanoparticles anchored on N-doped carbon framework for hydrogen evolution reaction (HER) in acidic, neutral and basic media is reported.
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Metallic and superhydrophilic nickel cobalt diselenide nanosheets electrodeposited on carbon cloth as a bifunctional electrocatalyst

TL;DR: In this paper, the authors reported the construction of intrinsically metallic NiCoSe2 on carbon cloth with a crimped nanosheet configuration and highly open structure, which exhibited superior electrocatalytic performance and robust durability toward overall water splitting, especially toward the oxygen evolution reaction.
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Ternary SnS(2-x)Se(x) Alloys Nanosheets and Nanosheet Assemblies with Tunable Chemical Compositions and Band Gaps for Photodetector Applications.

TL;DR: The photoelectrochemical measurements indicate that the performance of ternary SnS2−xSex alloys depends on their band structures and morphology characteristics, which renders them promising candidates for a variety of optoelectronic applications.
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Modeling of alloying effect on elastic properties in BCC Nb-Ti-V-Zr solid solution: From unary to quaternary

TL;DR: In this article, the single-crystal elastic constant model of the quaternary BCC Nb-Ti-V-Zr system across full composition space was established using first principles combined with the CALPHAD model.
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Ultra-high temperature ceramics melting temperature prediction via machine learning

TL;DR: The given case of melting temperature prediction of Hf-C-N ceramics proves the generality of the artificial neural network (ANN), and an inter-validation of meltingTemperature prediction using the network with materials thermodynamics and density functional theory has been demonstrated, indicating that the network is of powerful prediction ability.