Results 111 to 120 of about 11,640 (313)
This special issue of Electrochemistry features selected papers from the 65th Battery Symposium in Japan, held in November 2024, which marked a full return to in-person meetings after the COVID-19 pandemic.
Nobuyuki IMANISHI +6 more
doaj +1 more source
Navigating Ternary Doping in Li‐ion Cathodes With Closed‐Loop Multi‐Objective Bayesian Optimization
The search for advanced battery materials is pushing us into highly complex composition spaces. Here, a space with about 14 million unique combinations is efficiently explored using high‐throughput experimentation guided by Bayesian optimization with a deep kernel trained on both the Materials Project database and our data.
Nooshin Zeinali Galabi +6 more
wiley +1 more source
Decoding NASICON and Its Metal Interface for Solid‐State Batteries
This review paper focuses on lithium‐ and sodium‐based NASICON solid electrolytes and their interfaces with metal anodes using a top‐down framework. It begins with NASICON fundamentals, elucidates anode‐induced failure mechanisms, surveys advanced characterization strategies to understand them, summarizes mitigation approaches, and concludes with a ...
Jiaqi Xu +8 more
wiley +1 more source
As the demand for battery technology with enhanced safety and high energy density increases, solid-state batteries are currently attracting attention as a solution to problems such as fire and explosion risks associated with lithium-ion batteries.
Taehong Park, Sunho Lee, Dong-Min Kim
doaj +1 more source
Analyzing the Effect of Nano-Sized Conductive Additive Content on Cathode Electrode Performance in Sulfide All-Solid-State Lithium-Ion Batteries [PDF]
Jae Hong Choi +12 more
openalex +1 more source
This review highlights the integration of metal‐organic frameworks (MOFs) and two‐dimensional (2D) materials through dimensional interface engineering. By addressing intrinsic limitations like poor conductivity and agglomeration, these hybrid architectures optimize interfacial charge and mass transport.
Prashant Dubey +7 more
wiley +1 more source
A machine learning model that can predict the ionic conductivity of lithium-containing oxides using chemical composition and ionic conductivity data was previously developed.
Yudai IWAMIZU +7 more
doaj +1 more source
Microscopic photoelectron analysis of single crystalline LiCoO2 particles during the charge-discharge in an all solid-state lithium ion battery. [PDF]
Akada K +8 more
europepmc +1 more source

