Aluminum-ion batteries(AIBs)have been highlighted as a potential alternative to lithium-ion batteries for large-scale energy storage due to the abundant reserve,light weight,low cost,and good safety of Al.However,the ...Aluminum-ion batteries(AIBs)have been highlighted as a potential alternative to lithium-ion batteries for large-scale energy storage due to the abundant reserve,light weight,low cost,and good safety of Al.However,the development of AIBs faces challenges due to the usage of AlCl_(3)-based ionic liquid electrolytes,which are expensive,corrosive,and sensitive to humidity.Here,we develop a low-cost,non-corrosive,and air-stable hydrated eutectic electrolyte composed of aluminum perchlorate nonahydrate and methylurea(MU)ligand.Through optimizing the molar ratio to achieve the unique solvation structure,the formed Al(ClO_4)_(3)·9H_(2)O/MU hydrated deep eutectic electrolyte(AMHEE)with an average coordination number of 2.4 can facilely realize stable and reversible deposition/stripping of Al.When combining with vanadium oxide nanorods positive electrode,the Al-ion full battery delivers a high discharge capacity of 320 mAh g^(-1)with good capacity retention.The unique solvation structure with a low desolvation energy of the AMHEE enables Al^(3+)insertion/extraction during charge/discharge processes,which is evidenced by in situ synchrotron radiation X-ray diffraction.This work opens a new pathway of developing low-cost,safe,environmentally friendly and high-performance electrolytes for practical and sustainable AIBs.展开更多
Reliable and prompt information on forest above-ground biomass(AGB)and tree diameter at breast height(DBH)are crucial for sustainable forest management.Remote sensing technology,especially the Light Detection and Rang...Reliable and prompt information on forest above-ground biomass(AGB)and tree diameter at breast height(DBH)are crucial for sustainable forest management.Remote sensing technology,especially the Light Detection and Ranging(LiDAR)technology,has been proven to estimate important tree variables effectively.This study proposes predicting DBH and AGB from tree height and other LiDAR data extracted metrics.In the suggested DBH prediction,we developed a nonlinear estimation equation using the total tree height.As for the AGB prediction approach,we used regression methods such as multiple linear regression(MLR),random forest(RF)and support vector machine for regression(SVR).We conducted the study for the Gudao forest area dominated by Robinia Pseudoacacia trees,located in the Yellow River Delta(YRD),China.For our developed approaches,we used Unmanned Aerial Vehicle(UAV)and Backpack LiDAR point cloud datasets obtained in June 2017,and three field data measurements gathered in June 2017 and 2019 and October 2019,all from the same study area.The results demonstrate that:①The LiDAR data individual tree segmentation(ITS)from which we extracted individual tree information like tree location and tree height,was carried out with an overall accuracy F=0.91;②We used the ITS height data from the field stand in 2019 as a fit and developed a nonlinear DBH estimation equation with Root Mean Square Error(RMSE)=3.61 cm,later validated by the 2017 dataset;③Forest AGB at stand level was estimated with the MLR,RF and also SVR regression methods,and results show that the SVR method gave higher accuracy with R2=0.82 compared to the R2=0.72 of RF and the R2=0.70 of the MLR.Calculated AGB at plot level using the 2017 LiDAR data was used to validate both models’accuracy.Combining the UAV LiDAR data and the Backpack LiDAR significantly improved the overall ITS.The UAV LiDAR ability to provide high accuracy tree height abstraction,the DBH of the regression equation and other extracted LiDAR metrics showed high accuracy in estimating the forest AGB.This study shows that being cost-free is not the only advantage of free available software.In the performance of ITS and the LiDAR,metrics extraction proved to be as good as the commercially available software.展开更多
Rechargeable aluminum-sulfur(Al-S)batteries have been considered as a highly potential energy storage system owing to the high theoretical capacity,good safety,abundant natural reserves,and low cost of Al and S.Howeve...Rechargeable aluminum-sulfur(Al-S)batteries have been considered as a highly potential energy storage system owing to the high theoretical capacity,good safety,abundant natural reserves,and low cost of Al and S.However,the research progress of Al-S batteries is limited by the slow kinetics and shuttle effect of soluble polysulfides intermediates.Herein,an interconnected free-standing interlayer of iron sin-gle atoms supported on porous nitrogen-doped carbon nanofibers(FeSAs-NCF)on the separator is developed and used as both catalyst and chemical barrier for Al-S batteries.The atomically dispersed iron active sites(Fe-N_(4))are clearly identified by aberration-corrected high-angle annular dark-field scanning transmission electron microscopy and X-ray absorption near-edge structure.The Al-S battery with the FeSAs-NCF shows an improved specific capacity of 780 mAh g^(−1)and enhanced cycle stability.As evidenced by experimental and theoretical results,the atomically dispersed iron active centers on the separator can chemically adsorb the polysulfides and accelerate reaction kinetics to inhibit the shuttle effect and promote the reversible conversion between aluminum polysulfides,thus improving the electrochemical performance of the Al-S battery.This work provides a new way that can not only promote the conversion of aluminum sulfides but also suppress the shuttle effect in Al-S batteries.展开更多
The Yellow River Delta(YRD)has China's largest artificial Robinia pseudoacacia forest,which was planted in the late 1970s and suffered extensive dieback in the 1990s.The health grade of the R.pseudoacacia forest(n...The Yellow River Delta(YRD)has China's largest artificial Robinia pseudoacacia forest,which was planted in the late 1970s and suffered extensive dieback in the 1990s.The health grade of the R.pseudoacacia forest(named canopy vigor grade,CVG)could be achieved by using high-resolution images and canopy vigor indicators(CVIs).However,a previous study showed that there was no significant correlation between CVG and the field-estimated aboveground biomass(AGB)of R.pseudoacacia forest.Therefore,this study aims to construct forest health indicators(FHIs)based on canopy spatial structure parameters extracted from LiDAR.The FHIs included Weibull_α(the scale parameter of the Weibull density function that reflects the shape of the tree canopy),VCI(vertical complexity index),sdCC(the standard deviation of canopy cover),H99(the 99th percentile height)and cvLAD(the coefficient of variation of leaf area density),and could significantly distinguish three forest health grades(FHG)(p<0.05).The FHG was positively correlated with forest AGB(rs=0.51,p=0.004),and the similarity value with CVG was 63.33%.The results of this study confirmed that the FHIs can reflect both canopy vigor and tree productivity,and distinguish forest health status without prior classification information.展开更多
基金supported by the National Natural Science Foundation of China(52274302)。
文摘Aluminum-ion batteries(AIBs)have been highlighted as a potential alternative to lithium-ion batteries for large-scale energy storage due to the abundant reserve,light weight,low cost,and good safety of Al.However,the development of AIBs faces challenges due to the usage of AlCl_(3)-based ionic liquid electrolytes,which are expensive,corrosive,and sensitive to humidity.Here,we develop a low-cost,non-corrosive,and air-stable hydrated eutectic electrolyte composed of aluminum perchlorate nonahydrate and methylurea(MU)ligand.Through optimizing the molar ratio to achieve the unique solvation structure,the formed Al(ClO_4)_(3)·9H_(2)O/MU hydrated deep eutectic electrolyte(AMHEE)with an average coordination number of 2.4 can facilely realize stable and reversible deposition/stripping of Al.When combining with vanadium oxide nanorods positive electrode,the Al-ion full battery delivers a high discharge capacity of 320 mAh g^(-1)with good capacity retention.The unique solvation structure with a low desolvation energy of the AMHEE enables Al^(3+)insertion/extraction during charge/discharge processes,which is evidenced by in situ synchrotron radiation X-ray diffraction.This work opens a new pathway of developing low-cost,safe,environmentally friendly and high-performance electrolytes for practical and sustainable AIBs.
文摘Reliable and prompt information on forest above-ground biomass(AGB)and tree diameter at breast height(DBH)are crucial for sustainable forest management.Remote sensing technology,especially the Light Detection and Ranging(LiDAR)technology,has been proven to estimate important tree variables effectively.This study proposes predicting DBH and AGB from tree height and other LiDAR data extracted metrics.In the suggested DBH prediction,we developed a nonlinear estimation equation using the total tree height.As for the AGB prediction approach,we used regression methods such as multiple linear regression(MLR),random forest(RF)and support vector machine for regression(SVR).We conducted the study for the Gudao forest area dominated by Robinia Pseudoacacia trees,located in the Yellow River Delta(YRD),China.For our developed approaches,we used Unmanned Aerial Vehicle(UAV)and Backpack LiDAR point cloud datasets obtained in June 2017,and three field data measurements gathered in June 2017 and 2019 and October 2019,all from the same study area.The results demonstrate that:①The LiDAR data individual tree segmentation(ITS)from which we extracted individual tree information like tree location and tree height,was carried out with an overall accuracy F=0.91;②We used the ITS height data from the field stand in 2019 as a fit and developed a nonlinear DBH estimation equation with Root Mean Square Error(RMSE)=3.61 cm,later validated by the 2017 dataset;③Forest AGB at stand level was estimated with the MLR,RF and also SVR regression methods,and results show that the SVR method gave higher accuracy with R2=0.82 compared to the R2=0.72 of RF and the R2=0.70 of the MLR.Calculated AGB at plot level using the 2017 LiDAR data was used to validate both models’accuracy.Combining the UAV LiDAR data and the Backpack LiDAR significantly improved the overall ITS.The UAV LiDAR ability to provide high accuracy tree height abstraction,the DBH of the regression equation and other extracted LiDAR metrics showed high accuracy in estimating the forest AGB.This study shows that being cost-free is not the only advantage of free available software.In the performance of ITS and the LiDAR,metrics extraction proved to be as good as the commercially available software.
基金financially supported by the National Natural Science Foundation of China (No.51874197)Natural Science Foundation of Shanghai (Nos.21ZR1429400,22ZR1429700)
文摘Rechargeable aluminum-sulfur(Al-S)batteries have been considered as a highly potential energy storage system owing to the high theoretical capacity,good safety,abundant natural reserves,and low cost of Al and S.However,the research progress of Al-S batteries is limited by the slow kinetics and shuttle effect of soluble polysulfides intermediates.Herein,an interconnected free-standing interlayer of iron sin-gle atoms supported on porous nitrogen-doped carbon nanofibers(FeSAs-NCF)on the separator is developed and used as both catalyst and chemical barrier for Al-S batteries.The atomically dispersed iron active sites(Fe-N_(4))are clearly identified by aberration-corrected high-angle annular dark-field scanning transmission electron microscopy and X-ray absorption near-edge structure.The Al-S battery with the FeSAs-NCF shows an improved specific capacity of 780 mAh g^(−1)and enhanced cycle stability.As evidenced by experimental and theoretical results,the atomically dispersed iron active centers on the separator can chemically adsorb the polysulfides and accelerate reaction kinetics to inhibit the shuttle effect and promote the reversible conversion between aluminum polysulfides,thus improving the electrochemical performance of the Al-S battery.This work provides a new way that can not only promote the conversion of aluminum sulfides but also suppress the shuttle effect in Al-S batteries.
基金supported by National Natural Science Foundation of China:[Grant Number No.41471419 and No.31971579].
文摘The Yellow River Delta(YRD)has China's largest artificial Robinia pseudoacacia forest,which was planted in the late 1970s and suffered extensive dieback in the 1990s.The health grade of the R.pseudoacacia forest(named canopy vigor grade,CVG)could be achieved by using high-resolution images and canopy vigor indicators(CVIs).However,a previous study showed that there was no significant correlation between CVG and the field-estimated aboveground biomass(AGB)of R.pseudoacacia forest.Therefore,this study aims to construct forest health indicators(FHIs)based on canopy spatial structure parameters extracted from LiDAR.The FHIs included Weibull_α(the scale parameter of the Weibull density function that reflects the shape of the tree canopy),VCI(vertical complexity index),sdCC(the standard deviation of canopy cover),H99(the 99th percentile height)and cvLAD(the coefficient of variation of leaf area density),and could significantly distinguish three forest health grades(FHG)(p<0.05).The FHG was positively correlated with forest AGB(rs=0.51,p=0.004),and the similarity value with CVG was 63.33%.The results of this study confirmed that the FHIs can reflect both canopy vigor and tree productivity,and distinguish forest health status without prior classification information.
