为了提高Sn基材料表面CO_(2)电化学还原为甲酸盐的法拉第效率,利用一步溶剂热法合成了具有丰富硫空位的Cu掺杂SnS_(2)纳米花(Cu-SnS_(2-x))催化剂,在较宽的电位窗口实现了CO_(2)电化学还原制备甲酸盐。结果表明,通过调控催化剂制备过程...为了提高Sn基材料表面CO_(2)电化学还原为甲酸盐的法拉第效率,利用一步溶剂热法合成了具有丰富硫空位的Cu掺杂SnS_(2)纳米花(Cu-SnS_(2-x))催化剂,在较宽的电位窗口实现了CO_(2)电化学还原制备甲酸盐。结果表明,通过调控催化剂制备过程中Cu和Sn的摩尔比,在-1.1 V vs.RHE电位条件下得到了72.64%的FE_(formate),电流密度J_(formate)达到-14.38 mA/cm^(2)。二维纳米片阵列增加了催化活性位点,Cu掺杂所产生的硫空位能够协同提高催化活性、促进电子转移,从而提高甲酸盐的选择性。展开更多
The application of carbon dioxide(CO_(2)) in enhanced oil recovery(EOR) has increased significantly, in which CO_(2) solubility in oil is a key parameter in predicting CO_(2) flooding performance. Hydrocarbons are the...The application of carbon dioxide(CO_(2)) in enhanced oil recovery(EOR) has increased significantly, in which CO_(2) solubility in oil is a key parameter in predicting CO_(2) flooding performance. Hydrocarbons are the major constituents of oil, thus the focus of this work lies in investigating the solubility of CO_(2) in hydrocarbons. However, current experimental measurements are time-consuming, and equations of state can be computationally complex. To address these challenges, we developed an artificial intelligence-based model to predict the solubility of CO_(2) in hydrocarbons under varying conditions of temperature, pressure, molecular weight, and density. Using experimental data from previous studies,we trained and predicted the solubility using four machine learning models: support vector regression(SVR), extreme gradient boosting(XGBoost), random forest(RF), and multilayer perceptron(MLP).Among four models, the XGBoost model has the best predictive performance, with an R^(2) of 0.9838.Additionally, sensitivity analysis and evaluation of the relative impacts of each input parameter indicate that the prediction of CO_(2) solubility in hydrocarbons is most sensitive to pressure. Furthermore, our trained model was compared with existing models, demonstrating higher accuracy and applicability of our model. The developed machine learning-based model provides a more efficient and accurate approach for predicting CO_(2) solubility in hydrocarbons, which may contribute to the advancement of CO_(2)-related applications in the petroleum industry.展开更多
A series of multi-hydroxyl bis-(quaternary ammonium)ionic liquids(Ils1‒7)was prepared as bifunctional catalysts for the chemical fixation of CO_(2).All these ionic liquid compounds were efficient for the catalytic syn...A series of multi-hydroxyl bis-(quaternary ammonium)ionic liquids(Ils1‒7)was prepared as bifunctional catalysts for the chemical fixation of CO_(2).All these ionic liquid compounds were efficient for the catalytic synthesis of cyclic carbonates and oxazolidinones via the cycloaddition reactions between CO_(2) and epoxides or aziridines with excellent yield and high selectivity in the absence of co-catalyst,metal and solvent.Due to the synergistic effects of hydroxyl groups and halogen anion,the cycloaddition reactions proceeded smoothly either at atmospheric pressure or room temperature.The selectivity for substituted oxazolidinones at 5-and 4-positions can be tuned via changing the reaction conditions.Finally,possible mechanisms including the activation of both CO_(2) and epoxide or aziridines were proposed based on the literatures and experimental results.展开更多
文摘为了提高Sn基材料表面CO_(2)电化学还原为甲酸盐的法拉第效率,利用一步溶剂热法合成了具有丰富硫空位的Cu掺杂SnS_(2)纳米花(Cu-SnS_(2-x))催化剂,在较宽的电位窗口实现了CO_(2)电化学还原制备甲酸盐。结果表明,通过调控催化剂制备过程中Cu和Sn的摩尔比,在-1.1 V vs.RHE电位条件下得到了72.64%的FE_(formate),电流密度J_(formate)达到-14.38 mA/cm^(2)。二维纳米片阵列增加了催化活性位点,Cu掺杂所产生的硫空位能够协同提高催化活性、促进电子转移,从而提高甲酸盐的选择性。
基金supported by the Fundamental Research Funds for the National Major Science and Technology Projects of China (No. 2017ZX05009-005)。
文摘The application of carbon dioxide(CO_(2)) in enhanced oil recovery(EOR) has increased significantly, in which CO_(2) solubility in oil is a key parameter in predicting CO_(2) flooding performance. Hydrocarbons are the major constituents of oil, thus the focus of this work lies in investigating the solubility of CO_(2) in hydrocarbons. However, current experimental measurements are time-consuming, and equations of state can be computationally complex. To address these challenges, we developed an artificial intelligence-based model to predict the solubility of CO_(2) in hydrocarbons under varying conditions of temperature, pressure, molecular weight, and density. Using experimental data from previous studies,we trained and predicted the solubility using four machine learning models: support vector regression(SVR), extreme gradient boosting(XGBoost), random forest(RF), and multilayer perceptron(MLP).Among four models, the XGBoost model has the best predictive performance, with an R^(2) of 0.9838.Additionally, sensitivity analysis and evaluation of the relative impacts of each input parameter indicate that the prediction of CO_(2) solubility in hydrocarbons is most sensitive to pressure. Furthermore, our trained model was compared with existing models, demonstrating higher accuracy and applicability of our model. The developed machine learning-based model provides a more efficient and accurate approach for predicting CO_(2) solubility in hydrocarbons, which may contribute to the advancement of CO_(2)-related applications in the petroleum industry.
文摘A series of multi-hydroxyl bis-(quaternary ammonium)ionic liquids(Ils1‒7)was prepared as bifunctional catalysts for the chemical fixation of CO_(2).All these ionic liquid compounds were efficient for the catalytic synthesis of cyclic carbonates and oxazolidinones via the cycloaddition reactions between CO_(2) and epoxides or aziridines with excellent yield and high selectivity in the absence of co-catalyst,metal and solvent.Due to the synergistic effects of hydroxyl groups and halogen anion,the cycloaddition reactions proceeded smoothly either at atmospheric pressure or room temperature.The selectivity for substituted oxazolidinones at 5-and 4-positions can be tuned via changing the reaction conditions.Finally,possible mechanisms including the activation of both CO_(2) and epoxide or aziridines were proposed based on the literatures and experimental results.
