Henan University of Technology

About: Henan University of Technology is a education organization based out in Zhengzhou, China. It is known for research contribution in the topics: Catalysis & Starch. The organization has 7648 authors who have published 6503 publications receiving 73067 citations. The organization is also known as: Hénán Gōngyè Dàxué.

Effect of ozone treatment on deoxynivalenol and quality evaluation of ozonised wheat.

Abstract: Deoxynivalenol (DON) is the secondary metabolite of Fusarium graminearum, which is always found in Fusarium head blight of wheat. In this study, gaseous ozone was used to treat both DON solution and scabbed wheat to investigate the effectiveness of ozone treatment on DON degradation and the effect of ozone on the quality parameters of wheat. It was found that gaseous ozone had a significant effect on DON reduction in solution, when 10 mg l(-1) gaseous ozone was used to treat a 1 μg ml(-1) of DON solution, the degradation rate of DON was 93.6% within 30 s. Lower initial concentrations of DON solution treated with higher concentrations of ozone, and longer times showed higher DON degradation rates. Gaseous ozone was effective against DON in scabbed wheat. The degradation rate of DON increased with ozone concentration and processing time. The correlation between the time and degradation rate was y = -1.1926x(2) + 11.427x - 8.7787. In the process of ozone oxidation, a higher moisture content of wheat was more sensitive than that of lower moisture content to ozone under the same conditions. All samples were treated with different concentrations of ozone for 4 h to investigate the effect of ozone on wheat quality. No significant detrimental changes in the starch pasting properties of wheat were observed after all the samples were treated with ozone within 4 h. On the other hand, there was a slight rise in the dough development time and stability time, which meant the quality of flour improved after ozone treatment.

Monolayer MoS2 quantum dots as catalysts for efficient hydrogen evolution

Abstract: Monolayer MoS2 quantum dots (QDs) with lateral size around 3 nm were prepared through an effective multi-exfoliation based on lithium (Li) intercalation. The effects of the number of exfoliation on the microstructures and electrocatalytic activities of hydrogen evolution reaction (HER) for MoS2 nanosheets were examined. The lateral size of the nanosheets decreases rapidly with increasing the number of exfoliation. The obtained monolayer MoS2 QDs exhibit improved HER catalytic activities with a low overpotential of approximately 120 mV and a relatively small Tafel slope of 69 mV dec−1. Both the ultrathin structure and the abundance of exposed active edge sites make monolayer MoS2 QDs a promising HER electrocatalyst for practical application.

Fabrication and characterization of Ag3PO4/TiO2 heterostructure with improved visible-light photocatalytic activity for the degradation of methyl orange and sterilization of E.coli

Abstract: Ag3PO4/TiO2 heterostructure photocatalysts were prepared by the hydrolysis of TiCl4 and NH3· H2O, microwave calcination, and chemical precipitation method. A series of techniques, such as XRD, SEM,...

Proteome and phosphoproteome analysis of honeybee (Apis mellifera) venom collected from electrical stimulation and manual extraction of the venom gland

Abstract: Honeybee venom is a complicated defensive toxin that has a wide range of pharmacologically active compounds. Some of these compounds are useful for human therapeutics. There are two major forms of honeybee venom used in pharmacological applications: manually (or reservoir disrupting) extracted glandular venom (GV), and venom extracted through the use of electrical stimulation (ESV). A proteome comparison of these two venom forms and an understanding of the phosphorylation status of ESV, are still very limited. Here, the proteomes of GV and ESV were compared using both gel-based and gel-free proteomics approaches and the phosphoproteome of ESV was determined through the use of TiO2 enrichment. Of the 43 proteins identified in GV, 60% of the proteins were non-toxic proteins resulting from contamination by gland tissue damage during extraction and bee death. Of the 17 proteins identified in ESV, 14 proteins (>80%) were venom toxic proteins and most of them were found in higher abundance than in GV. Moreover, two novel proteins (dehydrogenase/reductase SDR family member 11-like and histone H2B.3-like) and three novel phosphorylation sites (icarapin (S43), phospholipase A-2 (T145), and apamin (T23)) were identified. Our data demonstrate that venom extracted manually is different from venom extracted using ESV, and these differences may be important in their use as pharmacological agents. ESV may be more efficient than GV as a potential pharmacological source because of its higher venom protein content, production efficiency, and without the need to kill honeybee. The three newly identified phosphorylated venom proteins in ESV may elicit a different immune response through the specific recognition of antigenic determinants. The two novel venom proteins extend our proteome coverage of honeybee venom.

A novel generative adversarial network for estimation of trip travel time distribution with trajectory data

Abstract: Knowledge of trip travel times serves an important role in transportation management and control. Existing travel time estimation approaches generally cover empirical ones, statistical ones and hybrid ones. Despite strong tractability, the empirical approaches cannot sufficiently capture diverse travel time distributions (TTDs) and often encounter some issues (e.g., assumption of a predefined distribution, failure of significance tests). Statistical and hybrid methods possess better generalization in estimating heterogeneous TTDs, but fail to model the network-wide spatiotemporal correlations, which have been found useful in the TTD estimation. To address these drawbacks, this paper proposes a deep learning based Trip Information Maximizing Generative Adversarial Network (T-InfoGAN). In this method, the trip TTD is estimated by modeling the joint distribution of travel times of two successive links with the consideration of network-wide spatiotemporal correlations. Meanwhile, a dynamic clustering with Wasserstein distance (DCWD) algorithm is used to explore the traffic state transitions for link pairs and cluster the link pairs with similar TTDs into one group, which benefits the training and estimation processes of T-InfoGAN. Then, based on GPS trajectory data from Didi Chuxing in Chengdu city, China, numerical results show that the T-InfoGAN with DCWD can well estimate three mini trip TTDs with various features, and performs better than three other counterparts (i.e., Convolution method, MC-Grid method, and MC-GMMS method) in estimating the TTDs of two longer trips. In summary, this study is the first successful try to estimate trip TTDs within the framework of Generative Adversarial Networks (GANs), and the deep learning based T-InfoGAN is a promising approach to estimate heterogeneous trip TTDs with the better generalization and flexibility in the big data era.