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Jinwei Liu

Researcher at Florida A&M University

Publications -  68
Citations -  2892

Jinwei Liu is an academic researcher from Florida A&M University. The author has contributed to research in topics: Hydrogen storage & Hydrogen. The author has an hindex of 24, co-authored 58 publications receiving 2322 citations. Previous affiliations of Jinwei Liu include South China University of Technology & Zunyi Medical College.

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Enhanced Hydrogen Storage Kinetics and Stability by Synergistic Effects of in Situ Formed CeH2.73 and Ni in CeH2.73-MgH2‑Ni Nanocomposites

TL;DR: In this paper, a method for in situ formation of cycle stable CeH2.73-MgH2-Ni nanocomposites, from the hydrogenation of as-melt Mg80Ce18Ni2 alloy, with excellent hydrogen storage performance.
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Enhanced dehydriding thermodynamics and kinetics in Mg(In)–MgF2 composite directly synthesized by plasma milling

TL;DR: In this article, a novel technique, plasma milling (P-milling), is proposed to solve the two problems simultaneously, namely the reversible formation of Mg(In) solid solution and the catalyzing effect of in situ synthesized MgF2.
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Dual-tuning effect of In on the thermodynamic and kinetic properties of Mg2Ni dehydrogenation

TL;DR: Mg2In0.1Ni solid solution with an Mg2Ni-type structure has been synthesized and its hydrogen storage properties have been investigated in this paper, where the authors showed that the introduction of In into Mg 2Ni not only significantly improved the dehydrogenation kinetics but also greatly lowered the thermodynamic stability.
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Enhanced high-rate discharge properties of La11.3Mg6.0Sm7.4Ni61.0Co7.2Al7.1 with added graphene synthesized by plasma milling

TL;DR: In this paper, the effects of plasma milling (PM) and graphene addition on their electrochemical properties and kinetics have been investigated, and it was found that the discharge capacity of AB 3.0 at a high discharge current density was significantly improved after the addition of graphene followed by PM for only 10 min.
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A Survey of Mobile Crowdsensing Techniques: A Critical Component for The Internet of Things

TL;DR: A survey of existing works for the mobile crowdsensing strategies with emphasis on reducing the resource cost and achieving high QoS, and provides the guidance on the future research direction of mobile crowdsense.