Doping perylene diimide(PDI)into a polymer matrix is a simple strategy to prepare near-infrared(NIR)reflective materials,but the mechanical properties and NIR reflectance properties are significantly compromised due t...Doping perylene diimide(PDI)into a polymer matrix is a simple strategy to prepare near-infrared(NIR)reflective materials,but the mechanical properties and NIR reflectance properties are significantly compromised due to macro-phase separation.In this study,a novel polymer(denoted as PU-PDI)with intrinsic NIR reflective proper⁃ties was synthesized by covalent incorporation of PDI units into polyurethane chains.Its photophysical characteris⁃tics,mechanical property and NIR reflectance property are investigated in detail.The results show that covalent in⁃corporation reduces the severe aggregation of PDI units,thereby endows PU-PDI with excellent mechanical property.The elongation at break of PU-PDI can reach more than 700%,and the breaking strength is 34.11 MPa.Moreover,compared to the blending system,PU-PDI possesses enhanced NIR reflection ability due to the better dispersion of PDI units.展开更多
The aim of this study was to establish the applicability of near-infrared reflectance spectroscopy(NIRS)as a rapid method for the accurate estimation of nutrient components in agricultural soils.Focusing on the soil o...The aim of this study was to establish the applicability of near-infrared reflectance spectroscopy(NIRS)as a rapid method for the accurate estimation of nutrient components in agricultural soils.Focusing on the soil of the Sanjiang Plain,NIRS was used to predict soil organic matter(SOM),the total nitrogen(TN)and the total phosphorus(TP).A total of 540 samples were collected from the three different depths(180 samples from each depth:0-10,10-20 and 20-30 cm),from 2015 to 2017,from the Sanjiang Plain in Heilongjiang Province,China.From every depth,120 samples were used to construct the calibration set.Other 60 samples were used to check the efficiency of the model.Combining the first-order differentiation with the partial least square(PLS)method,a prediction model was obtained to measure SOM,TN and TP.The correlation coefficient of SOM from 0 to 10 cm was R2=0.9567,from 10 to 20 cm was R2=0.9416,and from 20 to 30 cm was R2=0.9402.The corresponding ratio(standard deviation[SD]/root mean square error of prediction[RMSEP])was>2.96.R2 of TN with the three depths was 0.9154,0.9028 and 0.9024,respectively,all with SD/RMSEP>2.89.Meanwhile,R2 of TP with the three depths was 0.8974,0.8624 and 0.7804,respectively,all with SD/RMSEP>2.50.These results demonstrated that NIRS based on the first-order differentiation and PLS could efficiently predict SOM,TN and TP from different soil depths.展开更多
利用野外实时快速获取的土壤光谱进行土壤有机质(SOM)预测与制图是精确农业与土壤遥感制图的必然需要,利用ASD FieldSpec Pro FR野外型光谱仪实时快速获取的光谱数据,去除噪声较大的边缘波段后,进行倒数的对数转换(Log(1/R))为吸收光谱...利用野外实时快速获取的土壤光谱进行土壤有机质(SOM)预测与制图是精确农业与土壤遥感制图的必然需要,利用ASD FieldSpec Pro FR野外型光谱仪实时快速获取的光谱数据,去除噪声较大的边缘波段后,进行倒数的对数转换(Log(1/R))为吸收光谱。在分析吸收光谱和光谱指数与SOM关系的基础上,采用偏最小二乘回归法进行SOM的建模预测并借助地统计学方法进行SOM空间变异制图研究。结果表明,建模效果好的指标分别为特征波段(R2=0.91,RPD=3.28),归一化光谱指数(R2=0.90,RPD=3.08),特征波段与3个光谱指数组合(R2=0.87,RPD=2.67),全波段(R2=0.95,RPD=4.36)。光谱指标的克里格制图与实测SOM制图表现出相同的空间变异趋势,不同的指标均达到了较好的预测效果。展开更多
文摘Doping perylene diimide(PDI)into a polymer matrix is a simple strategy to prepare near-infrared(NIR)reflective materials,but the mechanical properties and NIR reflectance properties are significantly compromised due to macro-phase separation.In this study,a novel polymer(denoted as PU-PDI)with intrinsic NIR reflective proper⁃ties was synthesized by covalent incorporation of PDI units into polyurethane chains.Its photophysical characteris⁃tics,mechanical property and NIR reflectance property are investigated in detail.The results show that covalent in⁃corporation reduces the severe aggregation of PDI units,thereby endows PU-PDI with excellent mechanical property.The elongation at break of PU-PDI can reach more than 700%,and the breaking strength is 34.11 MPa.Moreover,compared to the blending system,PU-PDI possesses enhanced NIR reflection ability due to the better dispersion of PDI units.
基金Supported by the National Natural Science Foundation(31802120)Research and Demonstration of Large-scale Artificial Grassland Combined Plant and Circular Mode(2017YFD0502106)Academic Backbone Fund Project of Northeast Agricultural University。
文摘The aim of this study was to establish the applicability of near-infrared reflectance spectroscopy(NIRS)as a rapid method for the accurate estimation of nutrient components in agricultural soils.Focusing on the soil of the Sanjiang Plain,NIRS was used to predict soil organic matter(SOM),the total nitrogen(TN)and the total phosphorus(TP).A total of 540 samples were collected from the three different depths(180 samples from each depth:0-10,10-20 and 20-30 cm),from 2015 to 2017,from the Sanjiang Plain in Heilongjiang Province,China.From every depth,120 samples were used to construct the calibration set.Other 60 samples were used to check the efficiency of the model.Combining the first-order differentiation with the partial least square(PLS)method,a prediction model was obtained to measure SOM,TN and TP.The correlation coefficient of SOM from 0 to 10 cm was R2=0.9567,from 10 to 20 cm was R2=0.9416,and from 20 to 30 cm was R2=0.9402.The corresponding ratio(standard deviation[SD]/root mean square error of prediction[RMSEP])was>2.96.R2 of TN with the three depths was 0.9154,0.9028 and 0.9024,respectively,all with SD/RMSEP>2.89.Meanwhile,R2 of TP with the three depths was 0.8974,0.8624 and 0.7804,respectively,all with SD/RMSEP>2.50.These results demonstrated that NIRS based on the first-order differentiation and PLS could efficiently predict SOM,TN and TP from different soil depths.
文摘利用野外实时快速获取的土壤光谱进行土壤有机质(SOM)预测与制图是精确农业与土壤遥感制图的必然需要,利用ASD FieldSpec Pro FR野外型光谱仪实时快速获取的光谱数据,去除噪声较大的边缘波段后,进行倒数的对数转换(Log(1/R))为吸收光谱。在分析吸收光谱和光谱指数与SOM关系的基础上,采用偏最小二乘回归法进行SOM的建模预测并借助地统计学方法进行SOM空间变异制图研究。结果表明,建模效果好的指标分别为特征波段(R2=0.91,RPD=3.28),归一化光谱指数(R2=0.90,RPD=3.08),特征波段与3个光谱指数组合(R2=0.87,RPD=2.67),全波段(R2=0.95,RPD=4.36)。光谱指标的克里格制图与实测SOM制图表现出相同的空间变异趋势,不同的指标均达到了较好的预测效果。
