Results 171 to 180 of about 21,156 (298)

Decoding Gas Evolution Pathways and Interfacial Chemistry in Layered Oxide Cathodes for Safer Sodium‐Ion Batteries

open access: yesAdvanced Energy Materials, EarlyView.
Gas evolution behaviors of sodium layered oxide cathodes with varying compositions, cutoff voltages, dopants, and particle sizes/morphologies have been systematically investigated by online electrochemical mass spectrometry. The fundamental outgassing mechanisms of sodium‐based cathodes compared to lithium‐based cathodes have been elucidated.
Chen Liu, Zehao Cui, Arumugam Manthiram
wiley   +1 more source

Detecting faulty lithium-ion cells in large-scale parallel battery packs using current distributions. [PDF]

open access: yesCommun Eng
Lambert P   +5 more
europepmc   +1 more source

Safety of Sodium‐Ion Batteries: Evaluation and Perspective from Component Materials to Cells, Modules, and Packs

open access: yesAdvanced Energy Materials, EarlyView.
This review provides a bottom‐up evaluation of sodium‐ion battery safety, linking material degradation mechanisms, cell engineering parameters, and module/pack assembly. It emphasizes that understanding intrinsic material stability and establishing coordinated engineering control across hierarchical levels are vital for preventing degradation coupling ...
Won‐Gwang Lim   +5 more
wiley   +1 more source

Time series analysis of high energy density lithium-ion batteries for electric vehicles applications [PDF]

open access: yes, 2019
Alkali, Babakalli   +3 more
core  

Sulfide‐Based Electrolytes for All‐Solid‐State Sodium Batteries

open access: yesAdvanced Energy Materials, EarlyView.
This review covers the structural features and synthesis strategies of sulfide‐based solid electrolytes, as well as critical challenges related to conductivity, interfacial and moisture stability, and scaling‐up for practical application in Sodium‐based All Solid‐State Batteries.
Han Yang   +6 more
wiley   +1 more source

Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics

open access: yesAdvanced Energy Materials, EarlyView.
In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu   +6 more
wiley   +1 more source

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