Collapse of iron-based superconductor suggests super-elasticity

A collaboration between scientists at the US Department of Energy's Ames Laboratory and the Institute for Theoretical Physics at Goethe University Frankfurt am Main in Germany has computationally predicted a number of unique properties in a group of iron-based superconductors, including room-temperature super-elasticity.

The scientists at Ames Laboratory produced samples of an iron arsenide material with calcium and potassium – CaKFe₄As₄ – and experimentally discovered that the structure of the material collapsed when placed under pressure.

"It's a large change in dimension for a non-rubber-like material, and we wanted to know how exactly that collapsed state was occurring," said Paul Canfield, a senior scientist at Ames Laboratory and a professor of physics and astronomy at Iowa State University.

"Not only does this study have implications for properties of magnetism and superconductivity, it may have much wider application in room-temperature elasticity."Paul Canfield, Ames Laboratory

Through computational pressure simulations, the scientists learned that the material collapses in stages – termed ‘half-collapsed tetragonal phases’. The atomic structure near the calcium layers in the material collapses first, followed by the potassium layer, which collapses at higher pressures. The simulations also predicted these behaviors could be found in similar materials that are as-yet untested experimentally. The scientists report their findings in a paper in Physical Review B.

"Not only does this study have implications for properties of magnetism and superconductivity, it may have much wider application in room-temperature elasticity," said Canfield.

Canfield collaborated with Roser Valenti at the Institute for Theoretical Physics at Goethe University Frankfurt am Main, who served as the host faculty member for Canfield's Humboldt Award in 2014.

"It has been a delight as an experimentalist to be able to access this theoretical group's ever-increasing computational skills to model and predict properties," said Canfield.

This story is adapted from material from Ames Laboratory, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier. Link to original source.