With the vigorous development of the electronics industry,the consumption of lithium continues to increase,and more lithium needs to be mined to meet the development of the industry.The content of lithium in the solut...With the vigorous development of the electronics industry,the consumption of lithium continues to increase,and more lithium needs to be mined to meet the development of the industry.The content of lithium in the solution is much higher than that of minerals,but the interference of impurity ions increases the difficulty of extracting lithium ions.Therefore,we prepared an imidazole-based ionic liquid(1-butyl-3-methylImidazolium bis(trifluoromethyl sulfonyl)imide)(IL)for efficient lithium extraction from aqueous solutions by solvent extraction.Using an extraction consisting of 10%IL,85% tributyl phosphate(TBP),and 5% dichloroethane and an organic to aqueous phase ratio(O/A)of 2/1,over 64.23% of Li were extracted,and the extraction rate after five-stage extraction could reach more than 96%.The addition of ammonium ions to the solution inhibited the extraction of Ni,and the separation coefficient between lithium and nickel approached infinity,showing a very perfect separation effect.Fouriertransform infrared spectroscopy and slope methods were used to analyze the changes that occurred during extraction,revealing possible extraction mechanisms.In addition,the LiCl solution generated during the preparation of ionic liquids was mixed with the stripping solution,and the battery-grade lithium carbonate was prepared by Na_(2)CO_(3) precipitation,with a purity of 99.74%.This study provides an efficient and sustainable strategy for recovering lithium from the solution.展开更多
To remove vegetation bias(VB)from the global DEMs(GDEMs),an artificial neural network(ANN)-based method with the consideration of elevation spatial autocorrelation is developed in this paper.Three study sites with dif...To remove vegetation bias(VB)from the global DEMs(GDEMs),an artificial neural network(ANN)-based method with the consideration of elevation spatial autocorrelation is developed in this paper.Three study sites with different forest types(evergreen,mixed evergreen-deciduous,and deciduous)are employed to evaluate the performance of the proposed model on three popular 30-m GDEMs,including SRTM1,AW3D30,and COPDEM30.Taking LiDAR DTM as the ground truth,the accuracy of the GDEMs before and after VB correction is assessed,as well as two existing GDEMs including MERIT and FABDEM.Results show that all the original GDEMs significantly overestimate the LiDAR DTM in the three forest types,with the largest biases of 21.5 m for SRTM1,26.3 m for AW3D30,and 27.18 m for COPDEM30.Taking data randomly sampled from the corrected area as the training points,the proposed model reduces the mean errors(root mean square errors)of the three GDEMs by 98.8%-99.9%(55.1%-75.8%)in the three forests.When training data have the same forest type as the corrected GDEM but under different local situations,the proposed model lowers the GDEM errors by at least 76.9%(44.1%).Furthermore,our corrected GDEMs consistently outperform the existing GDEMs for the two cases.展开更多
基金This work was supported by the National Natural Science Foundation of China(No.52206222,No.22227901)State Key Laboratory of Laser Interaction with Matter Foundation(SKLLIM2009).
基金supported by the National Natural Science Foundation of China(22008161)Sichuan Science and Technology Program(2022YFQ0037)。
文摘With the vigorous development of the electronics industry,the consumption of lithium continues to increase,and more lithium needs to be mined to meet the development of the industry.The content of lithium in the solution is much higher than that of minerals,but the interference of impurity ions increases the difficulty of extracting lithium ions.Therefore,we prepared an imidazole-based ionic liquid(1-butyl-3-methylImidazolium bis(trifluoromethyl sulfonyl)imide)(IL)for efficient lithium extraction from aqueous solutions by solvent extraction.Using an extraction consisting of 10%IL,85% tributyl phosphate(TBP),and 5% dichloroethane and an organic to aqueous phase ratio(O/A)of 2/1,over 64.23% of Li were extracted,and the extraction rate after five-stage extraction could reach more than 96%.The addition of ammonium ions to the solution inhibited the extraction of Ni,and the separation coefficient between lithium and nickel approached infinity,showing a very perfect separation effect.Fouriertransform infrared spectroscopy and slope methods were used to analyze the changes that occurred during extraction,revealing possible extraction mechanisms.In addition,the LiCl solution generated during the preparation of ionic liquids was mixed with the stripping solution,and the battery-grade lithium carbonate was prepared by Na_(2)CO_(3) precipitation,with a purity of 99.74%.This study provides an efficient and sustainable strategy for recovering lithium from the solution.
基金supported by the National Natural Science Foundation of China(grant number 42271438)the Shan-dong Provincial Natural Science Foundation of China(grant no.ZR2020YQ26)a project of the Shandong Province Higher Educational Youth Innovation Science and Technology Program(grant number 2019KJH007).
文摘To remove vegetation bias(VB)from the global DEMs(GDEMs),an artificial neural network(ANN)-based method with the consideration of elevation spatial autocorrelation is developed in this paper.Three study sites with different forest types(evergreen,mixed evergreen-deciduous,and deciduous)are employed to evaluate the performance of the proposed model on three popular 30-m GDEMs,including SRTM1,AW3D30,and COPDEM30.Taking LiDAR DTM as the ground truth,the accuracy of the GDEMs before and after VB correction is assessed,as well as two existing GDEMs including MERIT and FABDEM.Results show that all the original GDEMs significantly overestimate the LiDAR DTM in the three forest types,with the largest biases of 21.5 m for SRTM1,26.3 m for AW3D30,and 27.18 m for COPDEM30.Taking data randomly sampled from the corrected area as the training points,the proposed model reduces the mean errors(root mean square errors)of the three GDEMs by 98.8%-99.9%(55.1%-75.8%)in the three forests.When training data have the same forest type as the corrected GDEM but under different local situations,the proposed model lowers the GDEM errors by at least 76.9%(44.1%).Furthermore,our corrected GDEMs consistently outperform the existing GDEMs for the two cases.
