建立了一种在温和条件下,用可见光催化合成一系列3,4-二氢异喹啉-1(2H)-酮及其衍生物的方法。该方法在室温条件下,以2-烯丙基-N-甲氧基苯甲酰胺为模板底物,以碘化钾作为光催化剂,25 W 460 nm的蓝色LED灯照射下,合成一系列3,4-二氢异喹啉...建立了一种在温和条件下,用可见光催化合成一系列3,4-二氢异喹啉-1(2H)-酮及其衍生物的方法。该方法在室温条件下,以2-烯丙基-N-甲氧基苯甲酰胺为模板底物,以碘化钾作为光催化剂,25 W 460 nm的蓝色LED灯照射下,合成一系列3,4-二氢异喹啉-1(2H)-酮衍生物,最高产率可达到83%。该合成路径具有底物适用范围广、经济实用等特点,为3,4-二氢异喹啉-1(2H)-酮衍生物合成提供了一种经济简便的方法。展开更多
In order to further improve the utility of unmanned aerial vehicle(UAV)remote-sensing for quickly and accurately monitoring the growth of winter wheat under film mulching, this study examined the treatments of ridge m...In order to further improve the utility of unmanned aerial vehicle(UAV)remote-sensing for quickly and accurately monitoring the growth of winter wheat under film mulching, this study examined the treatments of ridge mulching,ridge–furrow full mulching, and flat cropping full mulching in winter wheat.Based on the fuzzy comprehensive evaluation (FCE) method, four agronomic parameters (leaf area index, above-ground biomass, plant height, and leaf chlorophyll content) were used to calculate the comprehensive growth evaluation index (CGEI) of the winter wheat, and 14 visible and near-infrared spectral indices were calculated using spectral purification technology to process the remote-sensing image data of winter wheat obtained by multispectral UAV.Four machine learning algorithms, partial least squares, support vector machines, random forests, and artificial neural network networks(ANN), were used to build the winter wheat growth monitoring model under film mulching, and accuracy evaluation and mapping of the spatial and temporal distribution of winter wheat growth status were carried out.The results showed that the CGEI of winter wheat under film mulching constructed using the FCE method could objectively and comprehensively evaluate the crop growth status.The accuracy of remote-sensing inversion of the CGEI based on the ANN model was higher than for the individual agronomic parameters, with a coefficient of determination of 0.75,a root mean square error of 8.40, and a mean absolute value error of 6.53.Spectral purification could eliminate the interference of background effects caused by mulching and soil, effectively improving the accuracy of the remotesensing inversion of winter wheat under film mulching, with the best inversion effect achieved on the ridge–furrow full mulching area after spectral purification.The results of this study provide a theoretical reference for the use of UAV remote-sensing to monitor the growth status of winter wheat with film mulching.展开更多
文摘建立了一种在温和条件下,用可见光催化合成一系列3,4-二氢异喹啉-1(2H)-酮及其衍生物的方法。该方法在室温条件下,以2-烯丙基-N-甲氧基苯甲酰胺为模板底物,以碘化钾作为光催化剂,25 W 460 nm的蓝色LED灯照射下,合成一系列3,4-二氢异喹啉-1(2H)-酮衍生物,最高产率可达到83%。该合成路径具有底物适用范围广、经济实用等特点,为3,4-二氢异喹啉-1(2H)-酮衍生物合成提供了一种经济简便的方法。
基金This study was funded by the National Key R&D Program of China(2021YFD1900700)the National Natural Science Foundation of China(51909221)the China Postdoctoral Science Foundation(2020T130541 and 2019M650277).
文摘In order to further improve the utility of unmanned aerial vehicle(UAV)remote-sensing for quickly and accurately monitoring the growth of winter wheat under film mulching, this study examined the treatments of ridge mulching,ridge–furrow full mulching, and flat cropping full mulching in winter wheat.Based on the fuzzy comprehensive evaluation (FCE) method, four agronomic parameters (leaf area index, above-ground biomass, plant height, and leaf chlorophyll content) were used to calculate the comprehensive growth evaluation index (CGEI) of the winter wheat, and 14 visible and near-infrared spectral indices were calculated using spectral purification technology to process the remote-sensing image data of winter wheat obtained by multispectral UAV.Four machine learning algorithms, partial least squares, support vector machines, random forests, and artificial neural network networks(ANN), were used to build the winter wheat growth monitoring model under film mulching, and accuracy evaluation and mapping of the spatial and temporal distribution of winter wheat growth status were carried out.The results showed that the CGEI of winter wheat under film mulching constructed using the FCE method could objectively and comprehensively evaluate the crop growth status.The accuracy of remote-sensing inversion of the CGEI based on the ANN model was higher than for the individual agronomic parameters, with a coefficient of determination of 0.75,a root mean square error of 8.40, and a mean absolute value error of 6.53.Spectral purification could eliminate the interference of background effects caused by mulching and soil, effectively improving the accuracy of the remotesensing inversion of winter wheat under film mulching, with the best inversion effect achieved on the ridge–furrow full mulching area after spectral purification.The results of this study provide a theoretical reference for the use of UAV remote-sensing to monitor the growth status of winter wheat with film mulching.
