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Chao Zhang

Researcher at Beihang University

Publications -  4347
Citations -  118320

Chao Zhang is an academic researcher from Beihang University. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 127, co-authored 3119 publications receiving 84711 citations. Previous affiliations of Chao Zhang include West Virginia University & University of Oklahoma.

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A virus-like particle-based tetravalent vaccine for hand, foot, and mouth disease elicits broad and balanced protective immunity

TL;DR: It is demonstrated that the tetravalent VLP vaccine represents a promising broad-spectrum HFMD vaccine candidate and passively transferred tetraavalent vaccine-immunized sera conferred efficient protection against single or mixed infections with EV71, CVA16, C VA10, and CVA6 viruses in mice, whereas the monovalent vaccines could only protect mice against homotypic virus infections but not heterotypic challenges.
Journal Article

Metal-organic framework UiO-66 for rapid dispersive solid phase extraction of neonicotinoid insecticides in water samples

TL;DR: In this paper, UIO-66 crystals were explored for the first time to adsorb neonicotinoid insecticides in environmental water samples, which demonstrated a uniform particle size, a large Brunauer-Emmett-Teller surface area and high thermostability.
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Stretchable and self-healing polyvinyl alcohol/cellulose nanofiber nanocomposite hydrogels for strain sensors with high sensitivity and linearity

TL;DR: In this paper, a simple yet efficient solution compounding method is proposed for fabricating a cellulose nanofiber reinforced borax/polyvinyl alcohol nanocomposite hydrogel (CBPH).
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Halloysite nanotube nanocomposite hydrogels with tunable mechanical properties and drug release behavior

TL;DR: The hydrogel showed comparable cytocompatibility with the widely recognized poly(ethylene glycol) hydrogels and was verified by the MTT assay.
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Accelerating Primal Solution Findings for Mixed Integer Programs Based on Solution Prediction

TL;DR: In this paper, a graph convolutional network (GCN) is constructed to predict solution values for binary variables, which are used to generate a local branching type cut which can be either treated as a global (invalid) inequality in the formulation resulting in a heuristic approach to solve the MIP, or as a root branching rule resulting in an exact approach.