探索有机化学中的镜像世界:课程思政视角下“对映异构体”的教学设计

课程思政是高校培根铸魂、落实立德树人根本任务的重要战略举措。本文以对映异构体为例,从文学作品中的镜像关系、对映异构体的生物活性、我国科学家在不对称合成上的研究等角度,将专业知识与文学经典、职业道德、探索精神、创新研究与家国情怀等思政元素相融合,构建“智育与德育”协同的有机化学课程思政体系。这种体系旨在推动专业教育与思想政治教育相辅相成,既培养学生的专业技能,也提高他们的思想道德素质,以造就德才兼备的化学专业人才。

Effects of Soil Hydraulic Properties on Soil Moisture Estimation

Accurate quantification of soil moisture is essential to understand the land surface processes.Soil hydraulic properties influence water transport in soil and thus affect the estimation of soil moisture.However,some soil hydraulic properties are only observable at a few field sites.In this study,the effects of soil hydraulic properties on soil moisture estimation are investigated by using the one-dimensional(1-D)Richards equation at ELBARA,which is part of the Maqu monitoring network over the Tibetan Plateau(TP),China.Soil moisture assimilation experiments are then conducted with the unscented weighted ensemble Kalman filter(UWEnKF).The results show that the soil hydraulic properties significantly affect soil moisture simulation.Saturated soil hydraulic conductivity(Ksat)is optimized based on its observations in each soil layer with a genetic algorithm(GA,a widely used optimization method in hydrology),and the 1-D Richards equation performs well using the optimized values.If the range of Ksat for a complete soil profile is known for a particular soil texture(rather than for arbitrary layers within the horizon),optimized Ksat for each soil layer can be obtained by increasing the number of generations in GA,although this increases the computational cost of optimization.UWEnKF performs well with optimized Ksat,and improves the accuracy of soil moisture simulation more than that with calculated Ksat.Sometimes,better soil moisture estimation can be obtained by using optimized saturated volumetric soil moisture content Ksat.In summary,an accurate soil profile can be obtained by using soil moisture assimilation with optimized soil hydraulic properties.

Hierarchical Octahedral NazMnFe（CN）6 and Na2MnFe（CN）6@Ppy as Cathode Materials for Sodium-Ion Batteries

The Na2MnFe（CN）6 is successfully synthesized by a simple co-precipitation method, and then coated with con- ducting polymers （PPy） on the surface of Na2MnFe（CN）6 particle. Interestingly, Na2MnFe（CN）6 and NazMnFe（CN）6@PPy both show hollow octahedral hierarchical structures with nanometer-sized subunits. Na2MnFe（CN）6@PPy exhibits a discharge capacity of 107 mAh·g^-1 after 150 cycles at a current density of 10 mA·g^-1. It can still remain 65 mAh,g I even the current density is increased to 200 mA·g^-1. This superior electro- chemical property could be ascribed to unique hierarchical architecture as well as improved electronic conductivity.

Evaluating Soil Moisture Predictions Based on Ensemble Kalman Filter and SiB2 Model

Soil moisture is an important variable in the fields of hydrology, meteorology, and agriculture, and has been used for numerous applications and forecasts. Accurate soil moisture predictions on both a large scale and local scale for different soil depths are needed. In this study, a soil moisture assimilation and prediction based on the Ensemble Kalman Filter(EnKF) and Simple Biosphere Model(SiB2) have been performed in Meilin watershed, eastern China, to evaluate the initial state values with different assimilation frequencies and precipitation influences on soil moisture predictions. The assimilated results at the end of the assimilation period with different assimilation frequencies were set to be the initial values for the prediction period. The measured precipitation, randomly generated precipitation,and zero precipitation were used to force the land surface model in the prediction period. Ten cases were considered based on the initial value and precipitation. The results indicate that, for the summer prediction period with the deeper water table depth, the assimilation results with different assimilation frequencies influence soil moisture predictions significantly. The higher assimilation frequency gives better soil moisture predictions for a long lead-time. The soil moisture predictions are affected by precipitation within the prediction period. For a short lead-time, the soil moisture predictions are better for the case with precipitation, but for a long lead-time, they are better without precipitation. For the winter prediction period with a lower water table depth, there are better soil moisture predictions for the whole prediction period. Unlike the summer prediction period, the soil moisture predictions of winter prediction period are not significantly influenced by precipitation. Overall, it is shown that soil moisture assimilations improve its predictions.