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Commonly, in the artificial enzyme-involved signal amplification approach, the catalytic efficiency was limited by the relatively low binding affinity between artificial enzyme and substrate. In this work, substrate l-cysteine (l-Cys) and hemin were combined into one molecule to form l-Cys-hemin/G-quadruplex as an artificial self-catalytic complex for the improvement of the binding affinity between l-Cys-hemin/G-quadruplex and l-Cys. The apparent Michaelis-Menten constant ( K = 2.615 μM) on l-Cys-hemin/G-quadruplex for l-Cys was further investigated to assess the affinity, which was much lower than that of hemin/G-quadruplex ( K = 8.640 μM), confirming l-Cys-hemin/G-quadruplex possessed better affinity to l-Cys compared with that of hemin/G-quadruplex. Meanwhile, l-Cys bilayer could be further assembled onto the surface of l-Cys-hemin/G-quadruplex based on hydrogen-bond and electrostatic interaction to concentrate l-Cys around the active center, which was beneficial to the catalytic enhancement. Through this efficient electrochemical self-catalytic platform, a sensitive thrombin aptasensor was constructed. The results exhibited good sensitivity from 0.1 pM to 80 nM and the detection limit was calculated to be 0.032 pM. This self-catalytic strategy with improved binding affinity between l-Cys-hemin/G-quadruplex and l-Cys could provide an efficient approach to improve artificial enzymatic catalytic efficiency.

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A three-component metal catalyst was prepared and used in the process of catalytic wet peroxide oxidation (CWPO) for the degradation of unsymmetrical dimethylhydrazine (UDMH) in propellant wastewater with H2O2.

A novel magnetic nanocomposite is prepared using waste toner via calcination in ammonia, which exhibits excellent magnetic properties and high efficiency for the removal of Cr(vi) via pH regulation using H2SO4.

Volatility is integral for the financial market. As an emerging market, the Chinese stock market is acutely volatile. In this study, the data of the Shanghai Composite Index and Shenzhen Component Index returns were selected to conduct an empirical analysis based on the generalised autoregressive conditional heteroskedasticity (GARCH)-type model. We established the autoregressive moving average (ARMA)-GARCH model with t-distribution for the sample series to compare model effects under different distributions and orders. In contrast, we proposed threshold-GARCH (TGARCH) and exponential-GARCH (EGARCH) models to capture the features of the index. Additionally, the error degree and prediction results of different models were evaluated in terms of mean squared error (MSE), mean absolute error (MAE) and rootmean-squared error (RMSE). The results denote that the ARMA (4,4)-GARCH (1,1) model under Student's t-distribution outperforms other models when forecasting the Shanghai Composite Index return series. For the return series of the Shenzhen Component Index, ARMA(1,1)-TGARCH(1,1) display the best forecasting performance among all models. This study could provide an effective information reference for the macro decision-making of the government, the operation of listed companies and investors' investment decision-making.

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