Nanjing University of Science and Technology

About: Nanjing University of Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Control theory & Catalysis. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.

Ag/g-C3N4 catalyst with superior catalytic performance for the degradation of dyes: a borohydride-generated superoxide radical approach

Abstract: A straightforward approach is developed for fabrication of a visible-light-driven Ag/g-C3N4 catalyst. Morphological observation shows that the g-C3N4 sheets are decorated with highly dispersed Ag nanoparticles having an average size of 5.6 nm. The photocatalytic activity measurements demonstrate that the photocatalytic degradation rates of methyl orange (MO), methylene blue (MB), and neutral dark yellow GL (NDY-GL) over Ag/g-C3N4-4 can reach up to 98.2, 99.3 and 99.6% in the presence of borohydride ions (BH4−) only with 8, 45, and 16 min visible light irradiation, respectively. The significant enhancement in photoactivity of the catalyst is mainly attributed to the high dispersity and smaller size of Ag nanoparticles, the strong surface plasmon resonance (SPR) effect of metallic Ag nanoparticles, the efficient separation of photogenerated charge carriers, the additional superoxide radicals (O˙−2) generated from the reduction of dissolved oxygen in the presence of BH4− and the synergistic effect of Ag nanoparticles and g-C3N4.

Forecasting realized volatility in a changing world: A dynamic model averaging approach

Abstract: In this study, we forecast the realized volatility of the S&P 500 index using the heterogeneous autoregressive model for realized volatility (HAR-RV) and its various extensions. Our models take into account the time-varying property of the models’ parameters and the volatility of realized volatility. A dynamic model averaging (DMA) approach is used to combine the forecasts of the individual models. Our empirical results suggest that DMA can generate more accurate forecasts than individual model in both statistical and economic senses. Models that use time-varying parameters have greater forecasting accuracy than models that use the constant coefficients. The superiority of time-varying parameter models is also found in volatility density forecasting.