Week 18-24 March 2019
Welcome to this week's edition of Last Week In Batteries!
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This is a very basic review of last week's events relevant to the battery and fuel cell industries. We do not pretend to be experts in this space but as investors we find such an overview helpful. The digest is intentionally very brief and dry and is intended as a demonstration rather as an end product. We'd love to hear what tools/software/platforms you use to stay abreast of the events in your industries of interest: please answer our anonymous questionnaire.
WS2-graphite dual-ion battery
A team of researchers from Italy and Germany have developed "a novel WS2-graphite dual-ion battery (DIB)" that combines traditional graphite cathode with few-layer WS2 flakes anode. The new battery reportedly extends the operating voltage range of WS2-based Li-ion batteries.
Impact areas: Dual-ion batteries
Complex hydride lithium superionic conductor
Chemists from Tohoku University have developed a new material that promises to take all-solid-state batteries to a new level. The new material features "hydrogen clusters (complex anions), shows markedly high stability against lithium metal, which would make it the ultimate anode material for all-solid-state batteries."
Impact areas: All-solid-state battery capacity and performance
Salt water battery
Scientists from Imperial College London have built a prototype of a battery based on polymer and salt water. Although the battery has lower capacity than Li-ion batteries, it is non-toxic and can charge and discharge quickly, which makes it appropriate for applications where quick energy exchange is desired. The researchers quote automotive and utility applications as potential use cases.
Impact areas: Utility power management
Machine learning predicts voltage of electrode materials
Scientists from Central Michigan University have developed "a tool based on ML models to predict voltages of electrode materials for metal-ion batteries." This should help researchers identify promising materials before running expensive experiments.
Impact areas: Metal-ion battery research
Machine learning model for accurate estimates of charge level in Li-Ion batteries
Researchers from Data Science Institute at Columbia are developing a machine learning model for more accurate estimates of charge level in Li-ion batteries. They want to improve the error from 5% in existing models to 1%. Moe accurate battery management systems can help to optimize operating costs and to avoid disruptions due to inaccurate measurements.
Impact areas: Battery management and operating costs
In-situ electrolyte polymerization
Scientists from Cornell University have developed a method to produce solid-state batteries by polymerizing liquid electrolyte inside the battery. According to the researchers, the new method "shows promise for extending cycle life and recharging capabilities of high-energy-density rechargeable metal batteries."
Impact areas:Metal battery safety, durability, performance
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