Copper-doped titanium dioxide nanotubes were prepared by a one-step solvothermal method and exhibited high photocatalytic activity and stability.

In this paper, the transient MRF approach coupled with the standard k-ε and SST k-ω turbulence models was employed to study the effect of bottom shape, impeller diameter (D _J) and bottom height (H ₂) on critical impeller off-bottom clearance (C). It was found the bottom shape and bottom height (H ₂) have obvious influence on the flow pattern transition from double-loop to single-loop of RT impeller. The flow pattern transition mechanism was inferred to relate to the relationship between the space required by the lower circulation zone and the actual space. The boundary conditions of critical C were further concluded to help distinguish the flow pattern and receive the expected one in the stirred vessel design.

Safety design is a critical concern for energetic materials, with sensitivities and performance parameters becoming increasingly important as energy density rises. However, obtaining experimental data for these properties is costly and risky. Although linear methods and neural networks have been applied to predict these properties, the limited sample size of experimental data has led to models with limitations, including inadequate accuracy, lack of effective predictive models, and inevitable reliance on experimental properties. To address these challenges, this study utilizes kernel methods and heuristic algorithms, including Genetic Algorithm and Particle Swarm Optimization, to develop effective models for predicting the impact sensitivity, electric spark sensitivity, and detonation velocity of energetic materials. After optimizing the modeling process with Particle Swarm Optimization, the models achieved R² values of 0.871, 0.898, and 0.942 on the test sets, respectively, surpassing those of neural network models, with R² values of 0.827, 0.826, and 0.909, and support vector regression models, with R² values of 0.822, 0.862, and 0.894. The proposed models significantly improve the accuracy of impact sensitivity predictions and, for the first time, offer an effective model for predicting electric spark sensitivity. By being entirely based on computational descriptors, these models expand the application range compared to previous empirical formulas. These results demonstrate the high effectiveness and accuracy of this methodology in predicting the hazardous properties of chemicals with limited experimental data.