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.

An o-phthalimide-based multistimuli-responsive aggregation-induced emission (AIE) system

Abstract: Aggregation-induced emission luminogens (AIEgens) have been widely used in the development of functional materials due to their excellent luminescence properties in the solid state. The development of new AIEgens is of great significance. In this work, a novel o-phthalimide-based AIEgen of 2,3-diphenylquinoxaline-6,7-dicarboimide (2) is reported, which exhibited multifunctional sensing applications. Interestingly, compound 2 exhibited ultrafast photo-response properties upon UV light irradiation in both the dissolved state and solid state, which was attributed to a unique photo-induced rearrangement reaction. This property of 2 makes it suitable for UV sensing and photo-patterning materials with a significant fluorescence change. Meanwhile, based on the highly specific interaction between Hg(II) and the o-phthalimide moiety of 2, a complex of 2-Hg-2 formed in the presence of 0.5 equiv. Hg(II), resulting in a strong fluorescence change of 2. The active hydrogen in the o-phthalimide moiety also makes the fluorescence of 2 sensitive to the pH of aqueous solutions. Due to these characteristics, 2 was successfully used in the detection of UV light, Hg(II) and the pH of aqueous solutions, in which 2 exhibited satisfactory detection performance.

Dissipativity Analysis for Discrete Time-Delay Fuzzy Neural Networks With Markovian Jumps

Abstract: This paper is concerned with the dissipativity analysis and design of discrete Markovian jumping neural networks with sector-bounded nonlinear activation functions and time-varying delays represented by Takagi–Sugeno fuzzy model. The augmented fuzzy neural networks with Markovian jumps are first constructed based on estimator of Luenberger observer type. Then, applying piecewise Lyapunov–Krasovskii functional approach and stochastic analysis technique, a sufficient condition is provided to guarantee that the augmented fuzzy jump neural networks are stochastically dissipative. Moreover, a less conservative criterion is established to solve the dissipative state estimation problem by using matrix decomposition approach. Furthermore, to reduce the computational complexity of the algorithm, a dissipative estimator is designed to ensure stochastic dissipativity of the error fuzzy jump neural networks. As a special case, we have also considered the mixed $H_{\infty }$ and passive analysis of fuzzy jump neural networks. All criteria can be formulated in terms of linear matrix inequalities. Finally, two examples are given to show the effectiveness and potential of the new design techniques.