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 & Chemistry. The organization has 7648 authors who have published 6503 publications receiving 73067 citations. The organization is also known as: Hénán Gōngyè Dàxué.

A “signal-on” voltammetric aptasensor fabricated by hcPt@AuNFs/PEI-rGO and Fe3O4NRs/rGO for the detection of zearalenone

Abstract: A “signal-on” electrochemical aptasensor was proposed by employing hollow cubic platinum@gold nanoframes functionalized polyethyleneimine-reduced graphene oxide (hcPt@AuNFs/PEI-rGO) as the label material and Fe3O4 nanorods/reduced graphene oxide (Fe3O4NRs/rGO) for quantitative detection of zearalenone (ZEN). In this work, the hcPt@AuNFs/PEI-rGO with excellent conductivity and large surface area was synthesized as the label material, which can increase the loading of Thi and DNA S1. The Fe3O4NRs/rGO as the platform was decorated on gold electrodes, which can not only effectively improve the load capacity of the gold nanoparticles (AuNPs) and the DNA S2 but also catalyze Thi, thereby improving analytical performance of the aptasensor. Upon the specific recognition of aptamers to ZEN, the remained DNA S2 captured the hcPt@AuNFs/PEI-rGO immobilized with DNA S1 by hybridization reaction. With the aid of signal amplification system (hcPt@AuNFs/PEI-rGO and Fe3O4NRs/rGO), the aptasensor exhibited a linear relationship related to the logarithmic values of ZEN concentration from 0.5 pg/mL to 50 ng/mL with a low detection limit of 0.105 pg/mL under optimal conditions. Meanwhile, the aptasensor exhibited satisfactory specificity, good stability, excellent reproducibility and provided a potential application in maize samples.

A Novel Spatial Clustering with Obstacles Constraints Based on Genetic Algorithms and K-Medoids

Abstract: Spatial clustering is an important research topic in spatial data mining (SDM). Many methods have been proposed in the literature, but few of them have taken into account constraints that may be present in the data or constraints on the clustering. These constraints have significant influence on the results of the clustering process of large spatial data. In this paper, we discuss the problem of spatial clustering with obstacles constraints and propose a novel spatial clustering method based on genetic algorithms (GAs) and K-Medoids, called GKSCOC, which aims to cluster spatial data with obstacles constraints. It can not only give attention to higher local constringency speed and stronger global optimum search, but also get down to the obstacles constraints and practicalities of spatial clustering. The results on real datasets show that it is better than standard GAs and K-Medoids