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In this paper, the optimization of sodium vanadate (NVO) as cathode material for zinc-ion batteries (ZIBs) and the improvement of the material's synthesis are reported. Following the convincing capacities obtained for ZIBs using vanadium pentoxide (V₂O₅) as a cathode material, it is tried to further enhance the latter’s electrochemical performances through the insertion of sodium ions into the crystallographic structure of V₂O₅. Acting as stabilizing pillars, the added sodium ions allow NVO cathode material to reach extremely high cycling numbers without a considerable loss of capacity. Special attention is paid to the ecological aspect of the synthesizing method, as ZIBs are considered as being a sustainable and eco-friendly alternative to LIBs.
Zinc-ion battery --- Sodium vanadate --- ZIB --- NVO --- Na0.33V2O5 --- Na1.1V3O7.9 --- triflate --- aqueous electrolyte --- liquid pathway --- stirring --- hydrothermal --- calcination --- thermal treatment --- XRD --- SEM --- specific capacity --- cycle number --- stability --- pillars --- sodium ions --- Physique, chimie, mathématiques & sciences de la terre > Chimie
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This book is based on Special Issue "Artificial Intelligence Methods Applied to Urban Remote Sensing and GIS" from early 2020 to 2021. This book includes seven papers related to the application of artificial intelligence, machine learning and deep learning algorithms using remote sensing and GIS techniques in urban areas.
Environmental science, engineering & technology --- groundwater potential --- specific capacity --- machine learning --- boosted tree --- ensemble models --- prototype selection --- river pollution --- supervised classification --- WSN --- probabilistic method --- Monte Carlo simulation --- physical slope model --- Mt. Umyeon landslides --- Seoul --- synthetic aperture radar --- land subsidence --- GIS --- time-series --- Jakarta --- land subsidence susceptibility mapping --- time-series InSAR --- StaMPS processing --- seismic vulnerability map --- DPM method --- Sentinel-1 --- seismic literacy --- neural networks --- urban vegetation --- urban open spaces --- Monterrey Metropolitan Area --- sustainable development --- deep learning --- transfer learning --- artificial intelligence --- remote sensing --- earth observation --- DInSAR --- change detection --- space data science
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This book is based on Special Issue "Artificial Intelligence Methods Applied to Urban Remote Sensing and GIS" from early 2020 to 2021. This book includes seven papers related to the application of artificial intelligence, machine learning and deep learning algorithms using remote sensing and GIS techniques in urban areas.
groundwater potential --- specific capacity --- machine learning --- boosted tree --- ensemble models --- prototype selection --- river pollution --- supervised classification --- WSN --- probabilistic method --- Monte Carlo simulation --- physical slope model --- Mt. Umyeon landslides --- Seoul --- synthetic aperture radar --- land subsidence --- GIS --- time-series --- Jakarta --- land subsidence susceptibility mapping --- time-series InSAR --- StaMPS processing --- seismic vulnerability map --- DPM method --- Sentinel-1 --- seismic literacy --- neural networks --- urban vegetation --- urban open spaces --- Monterrey Metropolitan Area --- sustainable development --- deep learning --- transfer learning --- artificial intelligence --- remote sensing --- earth observation --- DInSAR --- change detection --- space data science
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This book is based on Special Issue "Artificial Intelligence Methods Applied to Urban Remote Sensing and GIS" from early 2020 to 2021. This book includes seven papers related to the application of artificial intelligence, machine learning and deep learning algorithms using remote sensing and GIS techniques in urban areas.
Environmental science, engineering & technology --- groundwater potential --- specific capacity --- machine learning --- boosted tree --- ensemble models --- prototype selection --- river pollution --- supervised classification --- WSN --- probabilistic method --- Monte Carlo simulation --- physical slope model --- Mt. Umyeon landslides --- Seoul --- synthetic aperture radar --- land subsidence --- GIS --- time-series --- Jakarta --- land subsidence susceptibility mapping --- time-series InSAR --- StaMPS processing --- seismic vulnerability map --- DPM method --- Sentinel-1 --- seismic literacy --- neural networks --- urban vegetation --- urban open spaces --- Monterrey Metropolitan Area --- sustainable development --- deep learning --- transfer learning --- artificial intelligence --- remote sensing --- earth observation --- DInSAR --- change detection --- space data science --- groundwater potential --- specific capacity --- machine learning --- boosted tree --- ensemble models --- prototype selection --- river pollution --- supervised classification --- WSN --- probabilistic method --- Monte Carlo simulation --- physical slope model --- Mt. Umyeon landslides --- Seoul --- synthetic aperture radar --- land subsidence --- GIS --- time-series --- Jakarta --- land subsidence susceptibility mapping --- time-series InSAR --- StaMPS processing --- seismic vulnerability map --- DPM method --- Sentinel-1 --- seismic literacy --- neural networks --- urban vegetation --- urban open spaces --- Monterrey Metropolitan Area --- sustainable development --- deep learning --- transfer learning --- artificial intelligence --- remote sensing --- earth observation --- DInSAR --- change detection --- space data science
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Electrochemical energy storage is becoming essential for portable electronics, electrified transportation, integration of intermittent renewable energy into grids, and many other energy and power applications. The electrode materials and their structures, in addition to the electrolytes, play key roles in supporting a multitude of coupled physicochemical processes that include electronic, ionic, and diffusive transport in electrode and electrolyte phases, electrochemical reactions and material phase changes, as well as mechanical and thermal stresses, thus determining the storage energy density and power density, conversion efficiency, performance lifetime, and system cost and safety. Different material chemistries and multiscale porous structures are being investigated for high performance and low cost. The aim of this Special Issue is to report the recent advances in materials used in electrochemical energy storage that encompass supercapacitors and rechargeable batteries.
lithium ion batteries --- microstructure --- zinc sulfide --- material index --- solid-state complexation method --- submicron powder --- X-ray diffraction --- vertical graphene --- garnet --- electrochemical energy storage --- biotemplate --- nanotubes --- cathode material --- Cr3+/Cr6+ redox pairs --- mechanical stability --- cathode materials --- supercapacitors --- electrochemical properties --- Co-doping --- elasto-plastic stress --- inductively-coupled plasma --- water --- voltage decay --- Mn3O4 --- thermal annealing --- parametric analysis --- solid-state batteries --- pulse power storage --- cycling performance --- energy storage and conversion --- anode material --- carbon nanostructures --- Li ion battery --- electrode materials --- Li2MoO3 --- lithium-ion conductivity --- lithium-ion batteries --- voltage attenuation --- methanol --- specific capacity --- lithium-ion battery --- sulfidation --- solid-state electrolyte --- lithium-rich layered oxide --- Li-rich layered oxide --- carbon microfibers --- specific capacitance --- nanostructure --- green synthesis route --- 0.5Li2MnO3·0.5LiMn0.8Ni0.1Co0.1O2 --- ZIF-67 --- co-precipitation method --- high-rate supercapacitor --- LiFePO4/C composite --- AC filtering --- sol–gel method --- electrochemical performance --- cross-linked carbon nanofiber
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