Deep learning prediction and experimental investigation of specific capacitance of nitrogen-doped porous biochar.

N-doped porous biochar is a promising carbon material for supercapacitor electrodes due to its developed pore structure and high chemical activity which greatly affect the capacitive performance. Predicting the capacitance and exploring the most influential factors are of great significance because it can not only avoid the trial-and-error experiments but also provide guidance for the synthesis of biochar with the aim of capacitance enhancement. In this study, a CNN model with ReLU activation function was established using DenseNet architecture for specific capacitance prediction. The importance and impacts of the physiochemical properties of N-doped porous biochar to the capacitance were revealed. With the guidance of the model, N-doped porous biochar samples with high capacitance were synthesized, the data of which were further used for model validation. This study provides not only a deep learning model which can be used in practice for capacitance prediction but also directions for the synthesis of N-doped porous biochar with high capacitive performance.

Determination of baicalin in Zhisou Kechuanning syrup by HPLC / 中华中医药杂志

Objective:To determine the content of baicalin in Zhisou Kechuanning syrup.Methods:.HPLC was used and di erent derivatization reaction conditions were investigated with the optimal conditions found to be the following:the chromatography column was Waters symmetry C 18(5?m;4.6?150mm),the mobile phase(solvent) was a mixture of methanol-0.1% Phosphoric acid(45:55)with a ow rate of 0.8 ml/min,and the detection wavelength was 280nm.Results:The linear range of Bergenin was 0.41-8.13?g with correlation factor r=0.9997,the average recovery was 98.36% and RSD=1.08%.Conclusion:This method is simple,reproducible,and can be used for quantitative determination of baicalin in Zhisou Kechuanning Syrup.