Hengyang Normal University

About: Hengyang Normal University is a education organization based out in Hengyang, China. It is known for research contribution in the topics: Graphene & Adsorption. The organization has 1087 authors who have published 1280 publications receiving 13850 citations. The organization is also known as: Hengyang Teachers' College & Héngyáng Shīfàn Xuéyuàn.

Highly efficient flexible quantum-dot light emitting diodes with an ITO/Ag/ITO cathode

Abstract: Flexible quantum dot light emitting diodes (QLEDs) are highly desired due to their advantages of foldability, lightweight, and potential applications in lighting and displays. In this report, we successfully fabricated high performance red (R), green (G), and blue (B) three primary color QLEDs based on a poly(ethylene-terephthalate)/ITO/Ag/ITO (PET–IAI) cathode. The multilayer flexible IAI electrode shows outstanding stability even after bending over 2000 times with a critical bending radius of 5 mm; the sheet resistance of the IAI film only increases from 12.7 to 14.8 Ω □−1. The maximum current efficiencies are 16.3, 86.5, and 16.1 cd A−1 for RGB QLEDs, respectively, which is the best device performance for flexible RGB QLEDs reported to date. Moreover, to the best of our knowledge, these are also record efficiencies for the green and blue devices in all the reported QLEDs. Furthermore, all the devices show saturated electroluminescence (EL) with the corresponding emission peaks at 606, 530, and 478 nm for three primary color QLEDs. The superior performance is a result of high transmittance and stability of the PET–IAI film. These results signify remarkable progress in flexible QLEDs and suggest that the PET–IAI based flexible QLEDs can offer a practicable platform for foldable applications.

A dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model

Abstract: Traditional dynamic multiobjective evolutionary algorithms usually imitate the evolution of nature, maintaining diversity of population through different strategies and making the population track the Pareto optimal solution set efficiently after the environmental change. However, these algorithms neglect the role of the dynamic environment in evolution, leading to the lacking of active guieded search. In this paper, a dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model is proposed (DEE-DMOEA). When the environment has not changed, this algorithm makes use of the evolutionary environment to record the knowledge and information generated in evolution, and in turn, the knowledge and information guide the search. When a change is detected, the algorithm helps the population adapt to the new environment through building a dynamic evolutionary environment model, which enhances the diversity of the population by the guided method, and makes the environment and population evolve simultaneously. In addition, an implementation of the algorithm about the dynamic evolutionary environment model is introduced in this paper. The environment area and the unit area are employed to express the evolutionary environment. Furthermore, the strategies of constraint, facilitation and guidance for the evolution are proposed. Compared with three other state-of-the-art strategies on a series of test problems with linear or nonlinear correlation between design variables, the algorithm has shown its effectiveness for dealing with the dynamic multiobjective problems.

Interaction of bZIP transcription factor TGA6 with salicylic acid signaling modulates artemisinin biosynthesis in Artemisia annua.

Abstract: Artemisinin is a sesquiterpene lactone produced by the Chinese traditional herb Artemisia annua and is used for the treatment of malaria. It is known that salicylic acid (SA) can enhance artemisinin content but the mechanism by which it does so is not known. In this study, we systematically investigated a basic leucine zipper family transcription factor, AaTGA6, involved in SA signaling to regulate artemisinin biosynthesis. We found specific in vivo and in vitro binding of the AaTGA6 protein to a 'TGACG' element in the AaERF1 promoter. Moreover, we demonstrated that AaNPR1 can interact with AaTGA6 and enhance its DNA-binding activity to its cognate promoter element 'TGACG' in the promoter of AaERF1, thus enhancing artemisinin biosynthesis. The artemisinin contents in AaTGA6-overexpressing and RNAi transgenic plants were increased by 90-120% and decreased by 20-60%, respectively, indicating that AaTGA6 plays a positive role in artemisinin biosynthesis. Importantly, heterodimerization with AaTGA3 significantly inhibits the DNA-binding activity of AaTGA6 and plays a negative role in target gene activation. In conclusion, we demonstrate that binding of AaTGA6 to the promoter of the artemisinin-regulatory gene AaERF1 is enhanced by AaNPR1 and inhibited by AaTGA3. Based on these findings, AaTGA6 has potential value in the genetic engineering of artemisinin production.