Here we report a novel twin polarization angle （TPA） approach in the quantitative chirality detection with the surface sum-frequency generation vibrational spectroscopy （SFG-VS）. Generally, the achiral contributio...Here we report a novel twin polarization angle （TPA） approach in the quantitative chirality detection with the surface sum-frequency generation vibrational spectroscopy （SFG-VS）. Generally, the achiral contribution dominates the surface SFG-VS signal, and the pure chiral signal is usually two or three orders of magnitude smaller. Therefore, it has been difficult to make quantitative detection and analysis of the chiral contributions to the surface SFG- VS signal. In the TPA method, by varying together the polarization angles of the incoming visible light and the sum frequency signal at fixed s or p polarization of the incoming infrared beam, the polarization dependent SFG signal can give not only direct signature of the chiral contribution in the total SFG-VS signal, but also the accurate measurement of the chiral and achiral components in the surface SFG signal. The general description of the TPA method is presented and the experiment test of the TPA approach is also presented for the SFG-VS from the S- and R-limonene chiral liquid surfaces. The most accurate degree of chiral excess values thus obtained for the 2878 cm^-1 spectral peak of the S- and R-limonene liquid surfaces are （23.7±0.4）% and （-25.4±1.3）%, respectively.展开更多
Soil salinization is a land degradation process that leads to reduced agricultural yields. This study investigated the method that can best predict electrical conductivity (EC) in dry soils using individual bands, a n...Soil salinization is a land degradation process that leads to reduced agricultural yields. This study investigated the method that can best predict electrical conductivity (EC) in dry soils using individual bands, a normalized difference salinity index (NDSI), partial least squares regression (PLSR), and bagging PLSR. Soil spectral reflectance of dried, ground, and sieved soil samples containing varying amounts of EC was measured using an ASD FieldSpec spectrometer in a darkroom. Predictive models were computed using a training dataset. An independent validation dataset was used to validate the models. The results showed that good predictions could be made based on bagging PLSR using first derivative reflectance (validation R2 = 0.85), PLSR using untransformed reflectance (validation R2 = 0.70), NDSI (validation R2 = 0.65), and the untransformed individual band at 2257 nm (validation R2 = 0.60) predictive models. These suggested the potential of mapping soil salinity using airborne and/or satellite hyperspectral data during dry seasons.展开更多
文摘Here we report a novel twin polarization angle （TPA） approach in the quantitative chirality detection with the surface sum-frequency generation vibrational spectroscopy （SFG-VS）. Generally, the achiral contribution dominates the surface SFG-VS signal, and the pure chiral signal is usually two or three orders of magnitude smaller. Therefore, it has been difficult to make quantitative detection and analysis of the chiral contributions to the surface SFG- VS signal. In the TPA method, by varying together the polarization angles of the incoming visible light and the sum frequency signal at fixed s or p polarization of the incoming infrared beam, the polarization dependent SFG signal can give not only direct signature of the chiral contribution in the total SFG-VS signal, but also the accurate measurement of the chiral and achiral components in the surface SFG signal. The general description of the TPA method is presented and the experiment test of the TPA approach is also presented for the SFG-VS from the S- and R-limonene chiral liquid surfaces. The most accurate degree of chiral excess values thus obtained for the 2878 cm^-1 spectral peak of the S- and R-limonene liquid surfaces are （23.7±0.4）% and （-25.4±1.3）%, respectively.
基金Project supported by the Agricultural Research Council-Institute for Soil, Climate and Water (ARC-ISCW) of South Africa (No.GW51/072)the National Research Foundation (NRF) of South Africa (No.GW 51/083/01)the Water Research Commission (WRC)of South Africa (No.K5/1849)
文摘Soil salinization is a land degradation process that leads to reduced agricultural yields. This study investigated the method that can best predict electrical conductivity (EC) in dry soils using individual bands, a normalized difference salinity index (NDSI), partial least squares regression (PLSR), and bagging PLSR. Soil spectral reflectance of dried, ground, and sieved soil samples containing varying amounts of EC was measured using an ASD FieldSpec spectrometer in a darkroom. Predictive models were computed using a training dataset. An independent validation dataset was used to validate the models. The results showed that good predictions could be made based on bagging PLSR using first derivative reflectance (validation R2 = 0.85), PLSR using untransformed reflectance (validation R2 = 0.70), NDSI (validation R2 = 0.65), and the untransformed individual band at 2257 nm (validation R2 = 0.60) predictive models. These suggested the potential of mapping soil salinity using airborne and/or satellite hyperspectral data during dry seasons.
