In this study, a novel copper nanozyme (CNZ) was synthesized by a mild way and characterized by scanning electron microscopy and Fourier transform infrared spectroscopy (FTIR). The as-fabricated CNZ exhibited typical peroxidase activity toward 2, 2′-azinodi-(3-ethylbenzthiazoline)-6-sulfonate. We successfully applied CNZ for the degradation of methyl orange pollutants. Under the optimum conditions (pH, 3.0; T, 60 °C; H₂O₂ concentration, 200 mM; dosage of CNZ, 8 mg), 93% of the degradation rate could be obtained in less than 10 min. Furthermore, the nanozyme exhibited excellent reusability and storage stability. All these experimental results suggested that CNZ is a powerful catalyst for industrial wastewater treatment.

The corn-to-sugar process is difficult to control automatically because of the complex physical and chemical phenomena involved. Because the RNN-LSTN model has been shown to handle long-term time dependencies well, this article focused on the design of a model predictive control system based on this machine learning model. Based on the historical data, we first reduced the input variable dimension through data preprocessing, data dimension reduction, sensitivity analysis, etc., and then the RNN-LSTM model, with these identified key sites as inputs, and the dextrose equivalent value as the output, was constructed. Then, through model predictive control using the locally linearized RNN-LSTM as the predictive model, the objective value of the dextrose equivalent was successfully controlled at the target value by our simulation study, in different situations of setpoint changes and disturbances. This showed the potential of applying RNN-LSTM-Based model predictive control in a corn-to-sugar process.

The escalating demand for processed foods has led to the widespread industrial use of glucose isomerase (GI) for high-fructose corn syrup (HFCS) production. This reliance on GIs necessitates continual Co²⁺ supplementation to sustain high catalytic activity across multiple reaction cycles. In this study, Serratia marcescens GI (SmGI) was immobilized onto surfaces of the metal-organic framework (MOF) material MOF (Co)-525 to generate MOF (Co)-525-GI for use in catalyzing glucose isomerization to generate fructose. Examination of MOF (Co)-525-GI structural features using scanning electron microscopy-energy dispersive spectroscopy, Fourier-transform infrared spectroscopy, and ultraviolet spectroscopy revealed no structural changes after SmGI immobilization and the addition of Co²⁺. Notably, MOF (Co)-525-GI exhibited optimal catalytic activity at pH 7.5 and 70 °C, with a maximum reaction rate (V_max) of 37.24 ± 1.91 μM/min and Km value of 46.25 ± 3.03 mM observed. Remarkably, immobilized SmGI exhibited sustained high catalytic activity over multiple cycles without continuous Co²⁺ infusion, retaining its molecular structure and 96.38% of its initial activity after six reaction cycles. These results underscore the potential of MOF (Co)-525-GI to serve as a safer and more efficient immobilized enzyme technology compared to traditional GI-based food-processing technologies.