Oct. 30 (UPI) -- New research has offered an explanation for why olivine lithium iron phosphate, a material used to make battery cathodes, often exceeds performance expectations. The material's secret lies in its defects.
"We know this material works very well but there's still much debate about why," Rice materials scientist Ming Tang said in a news release. "In many aspects, this material isn't supposed to be so good, but somehow it exceeds people's expectations."
During the process of fabricating olivine lithium iron phosphate, some of the atoms in the crystal lattice are misplaced. The new research suggests these point defects, called antisite defects, may explain the material's impressive performance.
The defects allow the cathode material to release and take up lithium ions across a surprisingly large surface area.
Until now, scientists assumed lithium ions could only move in a single direction, limiting the amount of the material's surface of area that can actively release or take up lithium ions.
Microscopic imaging and computer models allowed scientists to observe the movement of ions during battery charging. Their analysis -- detailed this week in the journal Nature Communications -- showed point defects allow ions to move in new directions.
The defects effectively expand the active surface area of the olivine lithium iron phosphate nanorods, enabling more efficient transfer of lithium ions between the cathode and electrolyte.
"What we learned changes the thinking on how the shape of lithium iron phosphate particles should be optimized," Tang said.
Most cathodes are built in the shape of thin disks to accommodate the one-directional movement of lithium ions.
"But as we now know that lithium can move in multiple directions, thanks to defects, the design criteria to maximize performance will certainly look quite different," Tang said.
Even experts in battery science don't always understand the electrochemical properties and processes that underly a lithium ion battery and its components. But the more scientists can image and analyze these processes and properties, the more they can fine-tune them to perform as efficiently as possible.