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CoFeO/ordered mesoporous carbon (OMC) nanocomposites were synthesized and tested as heterogeneous peroxymonosulfate (PMS) activator for the removal of rhodamine B. Characterization confirmed that CoFeO nanoparticles were tightly bonded to OMC, and the hybrid catalyst possessed high surface area, pore volume, and superparamagnetism. Oxidation experiments demonstrated that CoFeO/OMC nanocomposites displayed favorable catalytic activity in PMS solution and rhodamine B degradation could be well described by pseudo-first-order kinetic model. Sulfate radicals (SO·) were verified as the primary reactive species which was responsible for the decomposition of rhodamine B. The optimum loading ratio of CoFeO and OMC was determined to be 5:1. Under optimum operational condition (catalyst dosage 0.05 g/L, PMS concentration 1.5 mM, pH 7.0, and 25 °C), CoFeO/OMC-activated peroxymonosulfate system could achieve almost complete decolorization of 100 mg/L rhodamine B within 60 min. The enhanced catalytic activity of CoFeO/OMC nanocomposites compared to that of CoFeO nanoparticles could be attributable to the increased adsorption capacity and accelerated redox cycles between Co(III)/Co(II) and Fe(III)/Fe(II).

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The mildew phenomenon and mycotoxin pollution caused by the mass reproduction of grain fungi seriously endanger food security and people's health. The existing fungal growth models are difficult to accurately describe and predict the fungal growth trend, and can't optimally control the temperature and water content of grain storage environment. This paper constructs a fungal growth model with temperature and water content as the influencing factors to predict the fungal growth trend, and puts forward the optimal control method of temperature and water content during grain storage. Firstly, on the basis of the existing temperature-based microbial growth dynamics model, water influencing factors were added, and the transformation relationship model between colony diameter and fungal spore number was put forward by improving the two-dimensional normal distribution model. Then, the growth trend of fungi was predicted by discretizing and incrementing the model. On this basis, according to the characteristics of grain fungus pollution, the predictive control theory was improved, and the optimal control objective function with fungus growth as the predictive variable and temperature and water as the control variables was established. The fixed point method was used for iterative solution to achieve the global minimization of colony growth rate, temperature and water control cost. The experimental data of rice storage were used to verify the proposed method, and it was verified that the prediction accuracy of the established fungal growth model was high, and by optimizing the control of temperature and water content, the fungal growth could be significantly reduced.