An improved method for trace level quantification of dicyandiamide in stream water has been developed. This method includes sample pretreatment using solid phase extraction.The extraction procedure（including loading,...An improved method for trace level quantification of dicyandiamide in stream water has been developed. This method includes sample pretreatment using solid phase extraction.The extraction procedure（including loading, washing, and eluting） used a flow rate of1.0 m L/min, and dicyandiamide was eluted with 20 m L of a methanol/acetonitrile mixture（V/V = 2：3）, followed by pre-concentration using nitrogen evaporation and analysis with high performance liquid chromatography–ultraviolet spectroscopy（HPLC–UV）. Sample extraction was carried out using a Waters Sep-Pak AC-2 Cartridge（with activated carbon）.Separation was achieved on a ZIC-Hydrophilic Interaction Liquid Chromatography（ZIC-HILIC）（50 mm × 2.1 mm, 3.5 μm） chromatography column and quantification was accomplished based on UV absorbance. A reliable linear relationship was obtained for the calibration curve using standard solutions（R^2〉 0.999）. Recoveries for dicyandiamide ranged from 84.6% to 96.8%, and the relative standard deviations（RSDs, n = 3） were below 6.1% with a detection limit of 5.0 ng/m L for stream water samples.展开更多
Landslide susceptibility mapping is a crucial tool for analyzing geohazards in a region.Recent publications have popularized data-driven models,particularly machine learning-based methods,owing to their strong capabil...Landslide susceptibility mapping is a crucial tool for analyzing geohazards in a region.Recent publications have popularized data-driven models,particularly machine learning-based methods,owing to their strong capability in dealing with complex nonlinear problems.However,a significant proportion of these models have neglected qualitative aspects during analysis,resulting in a lack of interpretability throughout the process and causing inaccuracies in the negative sample extraction.In this study,Scoops 3D was employed as a physics-informed tool to qualitatively assess slope stability in the study area(the Hubei Province section of the Three Gorges Reservoir Area).The non-landslide samples were extracted based on the calculated factor of safety(FS).Subsequently,the random forest algorithm was employed for data-driven landslide susceptibility analysis,with the area under the receiver operating characteristic curve(AUC)serving as the model evaluation index.Compared to the benchmark model(i.e.,the standard method of utilizing the pure random forest algorithm),the proposed method’s AUC value improved by 20.1%,validating the effectiveness of the dual-driven method(physics-informed data-driven).展开更多
基金Department of Chemistry at Mississippi State University for financial support for this project
文摘An improved method for trace level quantification of dicyandiamide in stream water has been developed. This method includes sample pretreatment using solid phase extraction.The extraction procedure（including loading, washing, and eluting） used a flow rate of1.0 m L/min, and dicyandiamide was eluted with 20 m L of a methanol/acetonitrile mixture（V/V = 2：3）, followed by pre-concentration using nitrogen evaporation and analysis with high performance liquid chromatography–ultraviolet spectroscopy（HPLC–UV）. Sample extraction was carried out using a Waters Sep-Pak AC-2 Cartridge（with activated carbon）.Separation was achieved on a ZIC-Hydrophilic Interaction Liquid Chromatography（ZIC-HILIC）（50 mm × 2.1 mm, 3.5 μm） chromatography column and quantification was accomplished based on UV absorbance. A reliable linear relationship was obtained for the calibration curve using standard solutions（R^2〉 0.999）. Recoveries for dicyandiamide ranged from 84.6% to 96.8%, and the relative standard deviations（RSDs, n = 3） were below 6.1% with a detection limit of 5.0 ng/m L for stream water samples.
基金funded by the National Key R&D Program of China(Project No.2019YFC1509605)High-end Foreign Expert Introduction program(No.G20200022005 and DL2021165001L)Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.HZ2021001)。
文摘Landslide susceptibility mapping is a crucial tool for analyzing geohazards in a region.Recent publications have popularized data-driven models,particularly machine learning-based methods,owing to their strong capability in dealing with complex nonlinear problems.However,a significant proportion of these models have neglected qualitative aspects during analysis,resulting in a lack of interpretability throughout the process and causing inaccuracies in the negative sample extraction.In this study,Scoops 3D was employed as a physics-informed tool to qualitatively assess slope stability in the study area(the Hubei Province section of the Three Gorges Reservoir Area).The non-landslide samples were extracted based on the calculated factor of safety(FS).Subsequently,the random forest algorithm was employed for data-driven landslide susceptibility analysis,with the area under the receiver operating characteristic curve(AUC)serving as the model evaluation index.Compared to the benchmark model(i.e.,the standard method of utilizing the pure random forest algorithm),the proposed method’s AUC value improved by 20.1%,validating the effectiveness of the dual-driven method(physics-informed data-driven).
