Dendrite growth represents one of the most significant challenges that impede the development of aqueous zinc-ion batteries.Herein,Gd^(3+)ions are introduced into conventional electrolytes as a microlevelling agent to...Dendrite growth represents one of the most significant challenges that impede the development of aqueous zinc-ion batteries.Herein,Gd^(3+)ions are introduced into conventional electrolytes as a microlevelling agent to achieve dendrite-free zinc electrodeposition.Simulation and experimental results demonstrate that these Gd^(3+)ions are preferentially adsorbed onto the zinc surface,which enables dendritefree zinc anodes by activating the microlevelling effect during electrodeposition.In addition,the Gd^(3+)additives effectively inhibit side reactions and facilitate the desolvation of[Zn(H_(2)O)_(6)]^(2+),leading to highly reversible zinc plating/stripping.Due to these improvements,the zinc anode demonstrates a significantly prolonged cycle life of 2100 h and achieves an exceptional average Coulombic efficiency of 99.72%over 1400 cycles.More importantly,the Zn//NH_(4)V_(4)O_(10)full cell shows a high capacity retention rate of 85.6%after 1000 cycles.This work not only broadens the application of metallic cations in battery electrolytes but also provides fundamental insights into their working mechanisms.展开更多
Residual magnetic error remains after standard levelling process.The weak non-geological effect,manifesting itself as streaky noise along flight lines,creates a challenge for airborne geophysical data processing and i...Residual magnetic error remains after standard levelling process.The weak non-geological effect,manifesting itself as streaky noise along flight lines,creates a challenge for airborne geophysical data processing and interpretation.Microleveling is the process to eliminate this residual noise and is now a standard areogeophysical data processing step.In this paper,we propose a two-step procedure for single aerogeophysical data microleveling:a deep convolutional network is first adopted as approximator to map the original data into a low-level part with nature geological structures and a corrugated residual which still contains high-level detail geological structures;second,the mixture of Gaussian robust principal component analysis(MoG-RPCA)is then used to separate the weak energy fine structures from the residual.The final microleveling result is the addition of low-level structures from deep convolutional network and fine structures from MoG-RPCA.The deep convolutional network does not need dataset for training and the handcrafted network serves as prior(deep image prior)to capture the low-level nature geological structures in the areogeophysical data.Experiments on synthetic data and field data demonstrate that the combination of deep convolutional network and MoG-RPCA is an effective framework for single areogeophysical data microleveling.展开更多
Shorea robusta Gaertn. f.（Sal）is one of the important tim-ber-yielding plants in India, which dominates the vegetation of Terai landscape of Uttar Pradesh state in India forming various communities based on its asso...Shorea robusta Gaertn. f.（Sal）is one of the important tim-ber-yielding plants in India, which dominates the vegetation of Terai landscape of Uttar Pradesh state in India forming various communities based on its associations. The present study deals with delineation, map-ping and characterization of various communities of Sal （Shorea robusta） forests in Terai landscape of Uttar Pradesh, India ranging across over 16 districts. Field survey and visual interpretation based forest vegetation type classification and mapping was carried out as part of the project entitled ‘Biodiversity characterization at landscape level using remote sensing and GIS’. Indian Remote Sensing-P6 （Resourcesat-1） Linear Imaging Self Scanner-III satellite data was used during the study. The total area covered by different Sal forests was found to be approximately 2256.77 km2. Sal communities were identified and characterized based on their spectral properties, physiognomy and phytosociological charac-teristics. Following nine Sal communities were identified, delineated and mapped with reasonable accuracyviz.,Chandar,Damar, dry plains, moist plains, western alluvium, western alluvium plains, mixed moist deciduous, mixed dry deciduous andSiwalik. It is evident from the area estimates that mixed moist deciduous Sal is the most dominant commu-nity in the region covering around （1613.90 km2）, other major communi-ties were found as western alluvium plains Sal （362.44 km2）, mixed dry deciduous Sal （362.44 km2） and dry plains Sal （107.71 km2）. The Terai landscape of Uttar Pradesh faces tremendous anthropogenic pressure leading to deterioration of the forests. Community level information could be used monitoring the status as well as for micro level conserva-tion and planning of the Sal forests in Terai Landscape of Uttar Pradesh.展开更多
基金supported by the Scientific Research and Technology Development Project of China National Petroleum Corporation(Grant Nos.2024ZG50,2022DQ03-03)the National Natural Science Foundation of China(Grant Nos.52372252)the Science and Technology Innovation Program of Hunan Province(Grant Nos.2024RC1022).
文摘Dendrite growth represents one of the most significant challenges that impede the development of aqueous zinc-ion batteries.Herein,Gd^(3+)ions are introduced into conventional electrolytes as a microlevelling agent to achieve dendrite-free zinc electrodeposition.Simulation and experimental results demonstrate that these Gd^(3+)ions are preferentially adsorbed onto the zinc surface,which enables dendritefree zinc anodes by activating the microlevelling effect during electrodeposition.In addition,the Gd^(3+)additives effectively inhibit side reactions and facilitate the desolvation of[Zn(H_(2)O)_(6)]^(2+),leading to highly reversible zinc plating/stripping.Due to these improvements,the zinc anode demonstrates a significantly prolonged cycle life of 2100 h and achieves an exceptional average Coulombic efficiency of 99.72%over 1400 cycles.More importantly,the Zn//NH_(4)V_(4)O_(10)full cell shows a high capacity retention rate of 85.6%after 1000 cycles.This work not only broadens the application of metallic cations in battery electrolytes but also provides fundamental insights into their working mechanisms.
文摘Residual magnetic error remains after standard levelling process.The weak non-geological effect,manifesting itself as streaky noise along flight lines,creates a challenge for airborne geophysical data processing and interpretation.Microleveling is the process to eliminate this residual noise and is now a standard areogeophysical data processing step.In this paper,we propose a two-step procedure for single aerogeophysical data microleveling:a deep convolutional network is first adopted as approximator to map the original data into a low-level part with nature geological structures and a corrugated residual which still contains high-level detail geological structures;second,the mixture of Gaussian robust principal component analysis(MoG-RPCA)is then used to separate the weak energy fine structures from the residual.The final microleveling result is the addition of low-level structures from deep convolutional network and fine structures from MoG-RPCA.The deep convolutional network does not need dataset for training and the handcrafted network serves as prior(deep image prior)to capture the low-level nature geological structures in the areogeophysical data.Experiments on synthetic data and field data demonstrate that the combination of deep convolutional network and MoG-RPCA is an effective framework for single areogeophysical data microleveling.
基金part of the Department of Space/Department of Biotechnology sponsored project entitled "Biodiversity Characterization at Landscape level using Remote Sensing and GIS for Uttar Pradesh state except Vindhyan Hills"
文摘Shorea robusta Gaertn. f.（Sal）is one of the important tim-ber-yielding plants in India, which dominates the vegetation of Terai landscape of Uttar Pradesh state in India forming various communities based on its associations. The present study deals with delineation, map-ping and characterization of various communities of Sal （Shorea robusta） forests in Terai landscape of Uttar Pradesh, India ranging across over 16 districts. Field survey and visual interpretation based forest vegetation type classification and mapping was carried out as part of the project entitled ‘Biodiversity characterization at landscape level using remote sensing and GIS’. Indian Remote Sensing-P6 （Resourcesat-1） Linear Imaging Self Scanner-III satellite data was used during the study. The total area covered by different Sal forests was found to be approximately 2256.77 km2. Sal communities were identified and characterized based on their spectral properties, physiognomy and phytosociological charac-teristics. Following nine Sal communities were identified, delineated and mapped with reasonable accuracyviz.,Chandar,Damar, dry plains, moist plains, western alluvium, western alluvium plains, mixed moist deciduous, mixed dry deciduous andSiwalik. It is evident from the area estimates that mixed moist deciduous Sal is the most dominant commu-nity in the region covering around （1613.90 km2）, other major communi-ties were found as western alluvium plains Sal （362.44 km2）, mixed dry deciduous Sal （362.44 km2） and dry plains Sal （107.71 km2）. The Terai landscape of Uttar Pradesh faces tremendous anthropogenic pressure leading to deterioration of the forests. Community level information could be used monitoring the status as well as for micro level conserva-tion and planning of the Sal forests in Terai Landscape of Uttar Pradesh.
