Moist-electric generation,a green and environmentally friendly energy harvesting technology,is undoubt-edly one of the effective methods to alleviate energy shortages and environmental damage.However,the lack of fiber...Moist-electric generation,a green and environmentally friendly energy harvesting technology,is undoubt-edly one of the effective methods to alleviate energy shortages and environmental damage.However,the lack of fiber-like moist-electric generators(MEGs)that combine continuous power generation and high electrical output performance has constrained the development of moist-electric in the fields of flexi-ble wearable and self-power supplies.In this work,sodium alginate(SA)/multi-walled carbon nanotubes(MWCNT)fibers with axial heterogeneous(axi-he)of oxygen-containing functional groups(Ocfgs)are prepared through a mold forming method in assistance with the coagulation process.The interaction be-tween axi-he MEG and moisture is investigated by analyzing the electrical signal changes of dried MEG under moisture stimulation.The maximum output voltage and current of axi-he MEG can reach 0.35 V and 1.92μA under the stimulation of moisture.Based on the regulation of Ocfgs,axi-he MEG has a con-tinuous high moist-electric performance and environmental adaptability.The maximum output power density(Pmo)of axi-he MEG with a length of only 2 cm can reach 27.37μW cm-2 at RH=90%,which exceeds most of the MEGs reported in literature.Meanwhile,a continuous output voltage of 0.33-0.37 V for more than 15 h can be obtained from this axi-he MEG.Thus,the axi-he MEG from Ocfg distribution design and mold forming method provides a new way of clean energy generation using moisture from the ambient environment,exhibiting enormous potential in energy supply for Internet of Things(IoT)devices.展开更多
Currently,irrigation decisions in coffee cultivation primarily rely on empirical knowledge,resulting in inefficient practices.Combining real-time leaf water content(LWC)data can help improve the accuracy of the irriga...Currently,irrigation decisions in coffee cultivation primarily rely on empirical knowledge,resulting in inefficient practices.Combining real-time leaf water content(LWC)data can help improve the accuracy of the irrigation planning.Spectral remote sensing is a fast,reliable,and non-invasive method to detect vegetation moisture content.In this study,a model to estimate the LWC of Coffea arabica L.was built using hyperspectral reflectance of the canopy under various irrigation levels.For this purpose,common spectral indices,two-band spectral indices[ratio spectral index(RSI);difference spectral index(DSI);and normalized difference spectral index(NDSI)],and three-band spectral indices were constructed.Feature bands were extracted using the successive projections algorithm(SPA).Optimal spectral indices were extracted using the correlation coefficient method,and the feature wavebands and spectral indices were combined into five datasets.These datasets were split into modeling and validation datasets by sample set partitioning based on the joint x-y distance(SPXY)algorithm.A linear model[partial least squares regression(PLSR)]and three non-linear models[support vector machine(SVM);extreme learning machine(ELM);back propagation artificial neural network(BPANN)]were built to estimate LWC of Coffea arabica L.The results indicated that the non-linear models surpassed the linear model.The accuracy was the highest when the modeling was performed using the dataset combination 5.Among various modeling methods,the predictive performance of ELM was the best(modeling dataset:R^(2)=0.745,RMSE=2.241%,RRMSE=3.482%;validation dataset:R^(2)=0.721,RMSE=2.142%,RRMSE=3.364%).ELM outperformed PLSR,SVM,and BPANN in LWC retrieval.The obtained results indicated that the dataset built by the combined use of different methods was superior to those from a single data source in accuracy.This study provides a scientific basis for the quantitative diagnosis of coffee tree water status,with significant implications for optimizing field irrigation management.展开更多
基金supported by the National Natural Science Foundation of China(No.22073015)the National Key R&D Program of China(No.2022YFB3704600).
文摘Moist-electric generation,a green and environmentally friendly energy harvesting technology,is undoubt-edly one of the effective methods to alleviate energy shortages and environmental damage.However,the lack of fiber-like moist-electric generators(MEGs)that combine continuous power generation and high electrical output performance has constrained the development of moist-electric in the fields of flexi-ble wearable and self-power supplies.In this work,sodium alginate(SA)/multi-walled carbon nanotubes(MWCNT)fibers with axial heterogeneous(axi-he)of oxygen-containing functional groups(Ocfgs)are prepared through a mold forming method in assistance with the coagulation process.The interaction be-tween axi-he MEG and moisture is investigated by analyzing the electrical signal changes of dried MEG under moisture stimulation.The maximum output voltage and current of axi-he MEG can reach 0.35 V and 1.92μA under the stimulation of moisture.Based on the regulation of Ocfgs,axi-he MEG has a con-tinuous high moist-electric performance and environmental adaptability.The maximum output power density(Pmo)of axi-he MEG with a length of only 2 cm can reach 27.37μW cm-2 at RH=90%,which exceeds most of the MEGs reported in literature.Meanwhile,a continuous output voltage of 0.33-0.37 V for more than 15 h can be obtained from this axi-he MEG.Thus,the axi-he MEG from Ocfg distribution design and mold forming method provides a new way of clean energy generation using moisture from the ambient environment,exhibiting enormous potential in energy supply for Internet of Things(IoT)devices.
基金supported by the National Natural Science Foundation of China(Grant No.52379040)Yunnan Fundamental Research Projects(Grant No.202301AS070030 and 202101AU070039)+2 种基金Yunnan Major Science and Technology Special Plan(Grant No.202302AE090024)Yunnan Science and Technology Talent and Platform Program(Grant No.202305AM070006)the Yunnan Intelligent Water-Fertilizer-Pesticide Integration Technology and Equipment Innovation Team(No.202505AS350025).
文摘Currently,irrigation decisions in coffee cultivation primarily rely on empirical knowledge,resulting in inefficient practices.Combining real-time leaf water content(LWC)data can help improve the accuracy of the irrigation planning.Spectral remote sensing is a fast,reliable,and non-invasive method to detect vegetation moisture content.In this study,a model to estimate the LWC of Coffea arabica L.was built using hyperspectral reflectance of the canopy under various irrigation levels.For this purpose,common spectral indices,two-band spectral indices[ratio spectral index(RSI);difference spectral index(DSI);and normalized difference spectral index(NDSI)],and three-band spectral indices were constructed.Feature bands were extracted using the successive projections algorithm(SPA).Optimal spectral indices were extracted using the correlation coefficient method,and the feature wavebands and spectral indices were combined into five datasets.These datasets were split into modeling and validation datasets by sample set partitioning based on the joint x-y distance(SPXY)algorithm.A linear model[partial least squares regression(PLSR)]and three non-linear models[support vector machine(SVM);extreme learning machine(ELM);back propagation artificial neural network(BPANN)]were built to estimate LWC of Coffea arabica L.The results indicated that the non-linear models surpassed the linear model.The accuracy was the highest when the modeling was performed using the dataset combination 5.Among various modeling methods,the predictive performance of ELM was the best(modeling dataset:R^(2)=0.745,RMSE=2.241%,RRMSE=3.482%;validation dataset:R^(2)=0.721,RMSE=2.142%,RRMSE=3.364%).ELM outperformed PLSR,SVM,and BPANN in LWC retrieval.The obtained results indicated that the dataset built by the combined use of different methods was superior to those from a single data source in accuracy.This study provides a scientific basis for the quantitative diagnosis of coffee tree water status,with significant implications for optimizing field irrigation management.
