ABSTRACT
Aqueous zinc (Zn) ion batteries have received broad attention recently. However, their practical application is limited by severe Zn dendrite growth and the hydrogen evolution reaction. In this study, three alkali metal ions (Li+, Na+, and K+) are added in ZnSO4 electrolytes, which are subjected to electrochemical measurements and molecular dynamics simulations. The studies show that since K+ has the highest mobility and self-diffusion coefficient among the four ions (Li+, Na+, K+, and Zn2+), it enables K+ to preferentially approach a zinc dendrite at an earlier time, driven by a negative electric field during a cathodic process. The electric double layer, with K+ around the negatively charged Zn dendrite, inhibits dendrite growth and mitigates the hydrogen evolution reaction on the Zn anode. Under this kinetic effect, the Zn-Zn symmetric cell with K+ exhibits a long cycling stability of 1000 h at 1 mA·cm-2 of 1 mAh·cm-2 and 190 h at 30 mA·cm-2 of 2 mAh·cm-2. Such a kinetic effect is also observed with additives Na+ and Li+, though less profound than that of K+.
ABSTRACT
Converting more CO2 absorbed by plant photosynthesis into biomass-activated carbon effectively reduces carbon emissions. In this study, we used a one-step preparation of biomass-activated carbon loaded with MgO nanoparticles to investigate the effect of Mg loading on the catalytic pyrolysis process. The influences of magnesium loading on biochar yield and fixed carbon production were assessed. The addition of 1% Mg weakened the carbonyl C=O, inhibited the dehydroxylation reaction, enhanced the C-H signal strength, and the formation of MgO inhibited the weaker- bound substituent breakage. Additionally, the addition of magnesium altered the morphological features and chemical composition of the biochar material. It also increased the activated carbon mesoporosity by 3.94%, biochar yield by 5.55%, and fixed carbon yield by 12.14%. The addition of 1% Mg increased the adsorption capacity of the activated carbon to potassium dichromate, acid magenta, methylene blue, and tetracycline effluents by 8.71 mg, 37.15 mg, 117.68 mg, and 3.53 mg, respectively. The results showed that MgCl2 played a significant role in promoting the thermal degradation of biomass and improving the solid yield and adsorption performance of activated carbon.
ABSTRACT
In this paper, we propose an adaptive path tracking algorithm based on the BP (back propagation) neural network to increase the performance of vehicle path tracking in different paths. Specifically, based on the kinematic model of the vehicle, the front wheel steering angle of the vehicle was derived with the PP (Pure Pursuit) algorithm, and related parameters affecting path tracking accuracy were analyzed. In the next step, BP neural networks were introduced and vehicle speed, radius of path curvature, and lateral error were used as inputs to train models. The output of the model was used as the control coefficient of the PP algorithm to improve the accuracy of the calculation of the front wheel steering angle, which is referred to as the BP-PP algorithm in this paper. As a final step, simulation experiments and real vehicle experiments are performed to verify the algorithm's performance. Simulation experiments show that compared with the traditional path tracking algorithm, the average tracking error of BP-PP algorithm is reduced by 0.025 m when traveling at a speed of 3 m/s on a straight path, and the average tracking error is reduced by 0.27 m, 0.42 m, and 0.67 m, respectively, at a speed of 1.5 m/s with a curvature radius of 6.8 m, 5.5 m, and 4.5 m, respectively. In the real vehicle experiment, an electric patrol vehicle with an autonomous tracking function was used as the experimental platform. The average tracking error was reduced by 0.1 m and 0.086 m on a rectangular road and a large curvature road, respectively. Experimental results show that the proposed algorithm performs well in both simulation and actual scenarios, improves the accuracy of path tracking, and enhances the robustness of the system. Moreover, facing paths with changes in road curvature, the BP-PP algorithm achieved significant improvement and demonstrated great robustness. In conclusion, the proposed BP-PP algorithm reduced the interference of nonlinear factors on the system and did not require complex calculations. Furthermore, the proposed algorithm has been applied to the autonomous driving patrol vehicle in the park and achieved good results.
