A robotics system called Clio is key, too.
Nature Communications
paper, explained that the problem with working with electrolyte ingredients is that you can combine them “in billions of ways.” Prior to now, most research relied on guesswork, intuition, and trial and error. By being both free from bias and rapidly able to cycle through experimental conditions, Clio and Dragonfly can test far more options than human researchers—whether they’re minor refinements or moonshot solutions—and aren’t hamstrung by their preconceived notions.
In this case, Clio and Dragonfly were optimizing for recharge speed, but similar “closed-loop” experiments could optimize for capacity, discharge time, voltage, and all the other factors that matter in commercial battery performance.
and complex variables that go into battery manufacturing, maintenance, and charging. Late last month, a team of government researchers at the Department of Energy-run Idaho National Laboratory announced that they had found a way to safely and reliably recharge electric vehicles. They used a machine learning algorithm to analyze between 20,000 and 30,000 data points from different kinds of lithium-ion batteries to find the most efficient and safest method of recharging.
And while liquid electrolytes are one frontier for battery research, another involves exploring ways to replace that liquid with
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