Southeast University

About: Southeast University is a education organization based out in Nanjing, China. It is known for research contribution in the topics: MIMO & Control theory. The organization has 66363 authors who have published 79434 publications receiving 1170576 citations. The organization is also known as: SEU.

Toward the Targeted Design of Molecular Ferroelectrics: Modifying Molecular Symmetries and Homochirality

Abstract: ConspectusAlthough the first ferroelectric discovered in 1920 is Rochelle salt, a typical molecular ferroelectric, the front-runners that have been extensively studied and widely used in diverse ap...

Low temperature casting of graphene with high compressive strength

Abstract: has attracted attention due to its fascinating properties such as high carrier mobility, [ 6–8 ] high thermal conductivity, [ 9 , 10 ] extraordinary elasticity and stiffness [ 11 ] and other properties. While mechanical exfoliation, [ 6 ] liquid exfoliation, [ 12 ] and epitaxial growth [ 13 ] can produce pristine graphene, graphene yields are currently too low for large-scale production of macrostructures. In contrast, chemical reduction of graphene oxide provides ‘graphene’ sheets in large scale for graphene macrostructures. [ 14–16 ] Graphene-based macrostructures prepared to date have been relatively weak mechanically, given their fl exible and often relatively porous or open structures, [ 17–26 ] particularly with respect to compressive strength when compared with commercial graphite products. [ 27–29 ] Achieving highly compacted and thus “fully dense” macrostructures based on graphene and measuring the physical properties of such material(s) is thus an important goal. Here, we report a pH-mediated hydrothermal reduction which is combined with moulding methods and allows controllable fabrication of compact high density graphene macrostructures with various shapes. The compact graphene (CG) product that is fabricated in this study shows great advantages over hitherto reported 3-D graphene products, [ 17–26 ] e.g. , a solid microstructure and a high density ( ∼ 1.6 g cm − 3 ) which is comparable to conventional graphite products [ 27–29 ] and an ultrahigh compressive strength ( ∼ 361 Mpa) which is 6 times higher than

A Sensitive Aptasensor Based on a Hemin/G-Quadruplex-Assisted Signal Amplification Strategy for Electrochemical Detection of Gastric Cancer Exosomes.

Abstract: Emerging evidence indicates that exosomes derived from gastric cancer cells enhance tumor migration and invasion through the modulation of the tumor microenvironment. However, it remains a major problem to detect cancer-specific exosomes due to technical and biological challenges. Most of the methods reported could not achieve efficient detection of tumor-derived exosomes in the background of normal exosomes. Herein, a label-free electrochemical aptasensor is presented for specific detection of gastric cancer exosomes. This platform contains an anti-CD63 antibody modified gold electrode and a gastric cancer exosome specific aptamer. The aptamer is linked to a primer sequence that is complementary to a G-quadruplex circular template. The presence of target exosomes could trigger rolling circle amplification and produce multiple G-quadruplex units. This horseradish peroxidase mimicking DNAzyme could catalyze the reduction of H2 O2 and generate electrochemical signals. This aptasensor exhibits high selectivity and sensitivity toward gastric cancer exosomes with a detection limit of 9.54 × 102 mL-1 and a linear response range from 4.8 × 103 to 4.8 × 106 exosomes per milliliter. Therefore, this electrochemical aptasensor is expected to become a useful tool for the early diagnosis of gastric cancer.

A One-Layer Projection Neural Network for Nonsmooth Optimization Subject to Linear Equalities and Bound Constraints

Abstract: This paper presents a one-layer projection neural network for solving nonsmooth optimization problems with generalized convex objective functions and subject to linear equalities and bound constraints. The proposed neural network is designed based on two projection operators: linear equality constraints, and bound constraints. The objective function in the optimization problem can be any nonsmooth function which is not restricted to be convex but is required to be convex (pseudoconvex) on a set defined by the constraints. Compared with existing recurrent neural networks for nonsmooth optimization, the proposed model does not have any design parameter, which is more convenient for design and implementation. It is proved that the output variables of the proposed neural network are globally convergent to the optimal solutions provided that the objective function is at least pseudoconvex. Simulation results of numerical examples are discussed to demonstrate the effectiveness and characteristics of the proposed neural network.