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Jing Yang

Researcher at Harbin Engineering University

Publications -  6
Citations -  860

Jing Yang is an academic researcher from Harbin Engineering University. The author has contributed to research in topics: Catalysis & Computer science. The author has an hindex of 3, co-authored 3 publications receiving 593 citations.

Papers
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Journal ArticleDOI

Sulfate radicals induced from peroxymonosulfate by magnetic ferrospinel MFe2O4 (M = Co, Cu, Mn, and Zn) as heterogeneous catalysts in the water

TL;DR: In this paper, the mechanism of different catalysts in the catalytic peroxymonosulfate (PMS) solution was illustrated, and the results showed that the incorporation of CoFe 2 O 4 had the highest catalytic performance in PMS oxidation for DBP degradation.
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The selective binding character of a molecular imprinted particle for Bisphenol A from water.

TL;DR: It was found that the molecular geometry and the hydrogen bonding interactions between the hydroxyl and carboxyl played an important role in recognizing the target molecular in the binding process.
Patent

Catalyst for oxidation and decomposition of organic pollutants in water with ozone and method thereof for catalyzing sewage treatment with ozone

TL;DR: In this article, a method for catalyzing the sewage treatment with ozone comprises the following steps of adjusting pH and temperature of sewage, then introducing ozone, further adding the catalyst and treating for 1 minute-120 minutes, then using a magnetic field to recover the catalyst, and finally absorbing tail gas of the ozone.
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A comparative study of the degradation of refractory organic matter in MBR effluent from landfill leachate treatment by the microwave-enhanced iron-activated hydrogen peroxide and peroxydisulfate processes

TL;DR: In this article , two advanced oxidation processes dominated by different reactive oxygen species (ROS), microwave-iron-hydrogen peroxide (Microwave/ZVI/H2O2) and microwave-irony peroxydisulfate (MW Microwave, ZVI/PDS), were applied to the degradation of refractory organic matter in the membrane bioreactor (MBR) effluent from landfill leachate treatment.
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Span-based joint entity and relation extraction augmented with sequence tagging mechanism

TL;DR: The core architecture enables the model to learn token-level label information via the sequence tagging mechanism and then uses the information in the span-based joint extraction; on the other hand, it establishes a bi-directional information interaction between NER and RE.