Rare earth elements(REEs)are used for the developme nt of new energy materials owing to their intrinsic physicochemical property.However,excess REEs in water threaten the safety of animals,plants and humans.An efficie...Rare earth elements(REEs)are used for the developme nt of new energy materials owing to their intrinsic physicochemical property.However,excess REEs in water threaten the safety of animals,plants and humans.An efficient way to separate REEs from the water is therefore needed.In this study,a biosorbent consisting of iron oxide(Fe3 O4),persimmon tannin(PT),and graphene oxide(GO)as Fe3 O4/PT/GO was prepared,and the adsorption of trivalent erbium(Er3+)ions from aqueous solution was investigated.The adsorption process for Er3+ions conforms to pseudo-second order kinetic and the Langmuir isotherm model behavior.Thermodynamic studies indicate that the adsorption process is spontaneous and endothermic.Scanning electron microscopy(SEM),X-ray photoelectron spectroscopy(XPS),X-ray diffraction(XRD),thermogravimetric analysis(TGA).Fourier-transform infrared(FT-IR)spectroscopy;Brunauer-Emmett-Teller(BET)analysis,and vibrating sample magnetometer(VSM)were used to assess the adsorption mechanism of Er3+ions onto the Fe3 O4/PT/GO biosorbent.A combination of electrostatic interactions,redox reactivity and chelation are responsible for adsorption of Er3+ions on the Fe3 O4/PT/GO biosorbent,The magnetic Fe3 O4/PT/GO biosorbent can be easily separated under the magnetic field for effective recycle of Er3+ions from aqueous solution.Therefore,this new biomass composite holds great promise for wastewater treatment.展开更多
目的基于近红外光谱(near infrared spectroscopy,NIRS)技术快速检测大豆中水分和粗脂肪含量。方法首先采集350~2500 nm光谱范围的大豆近红外光谱,采用光谱-理化值共生距离(sample set partitioning based on joint x-y distance,SPXY)...目的基于近红外光谱(near infrared spectroscopy,NIRS)技术快速检测大豆中水分和粗脂肪含量。方法首先采集350~2500 nm光谱范围的大豆近红外光谱,采用光谱-理化值共生距离(sample set partitioning based on joint x-y distance,SPXY)算法将大豆样本划分为校正集样本与测试集样本,然后对原始光谱分别采用多元散射校正、标准正态变量交换、归一化等9种方法进行预处理,最后使用偏最小二乘回归分析方法建立模型对样本进行定量分析。结果原始光谱经过多元散射校正后建立的偏最小二乘回归模型对水分的预测精度最高,其校正集和测试集的相关系数分别为0.8964和0.9055,均方根误差分别为0.4211和0.5933;原始光谱经过归一化处理后建立的偏最小二乘回归模型对粗脂肪的预测精度最高,其校正集和测试集的相关系数分别为0.9084和0.9295,均方根误差分别为0.6897和0.6462。结论近红外光谱结合预处理及偏最小二乘回归法,可以快速、准确的检测大豆水分和粗脂肪含量。展开更多
基金Project supported by the National Natural Science Foundation of China(81760534,51961010)Guangxi Key Research and Development Program(Guike2018AB38016,GuikeAB16380278)+1 种基金the Natural Science Foundation of Guangxi Province(2016GXNSFGA380001)the Special Fund of Guangxi Distinguished Experts。
文摘Rare earth elements(REEs)are used for the developme nt of new energy materials owing to their intrinsic physicochemical property.However,excess REEs in water threaten the safety of animals,plants and humans.An efficient way to separate REEs from the water is therefore needed.In this study,a biosorbent consisting of iron oxide(Fe3 O4),persimmon tannin(PT),and graphene oxide(GO)as Fe3 O4/PT/GO was prepared,and the adsorption of trivalent erbium(Er3+)ions from aqueous solution was investigated.The adsorption process for Er3+ions conforms to pseudo-second order kinetic and the Langmuir isotherm model behavior.Thermodynamic studies indicate that the adsorption process is spontaneous and endothermic.Scanning electron microscopy(SEM),X-ray photoelectron spectroscopy(XPS),X-ray diffraction(XRD),thermogravimetric analysis(TGA).Fourier-transform infrared(FT-IR)spectroscopy;Brunauer-Emmett-Teller(BET)analysis,and vibrating sample magnetometer(VSM)were used to assess the adsorption mechanism of Er3+ions onto the Fe3 O4/PT/GO biosorbent.A combination of electrostatic interactions,redox reactivity and chelation are responsible for adsorption of Er3+ions on the Fe3 O4/PT/GO biosorbent,The magnetic Fe3 O4/PT/GO biosorbent can be easily separated under the magnetic field for effective recycle of Er3+ions from aqueous solution.Therefore,this new biomass composite holds great promise for wastewater treatment.
文摘目的基于近红外光谱(near infrared spectroscopy,NIRS)技术快速检测大豆中水分和粗脂肪含量。方法首先采集350~2500 nm光谱范围的大豆近红外光谱,采用光谱-理化值共生距离(sample set partitioning based on joint x-y distance,SPXY)算法将大豆样本划分为校正集样本与测试集样本,然后对原始光谱分别采用多元散射校正、标准正态变量交换、归一化等9种方法进行预处理,最后使用偏最小二乘回归分析方法建立模型对样本进行定量分析。结果原始光谱经过多元散射校正后建立的偏最小二乘回归模型对水分的预测精度最高,其校正集和测试集的相关系数分别为0.8964和0.9055,均方根误差分别为0.4211和0.5933;原始光谱经过归一化处理后建立的偏最小二乘回归模型对粗脂肪的预测精度最高,其校正集和测试集的相关系数分别为0.9084和0.9295,均方根误差分别为0.6897和0.6462。结论近红外光谱结合预处理及偏最小二乘回归法,可以快速、准确的检测大豆水分和粗脂肪含量。
