Scientists Finally Find Superconductivity In Place They Have Been Looking For Decades
Arthur T Knackerbracket has found the following story:
Researchers at Stanford University and the Department of Energy's SLAC National Accelerator Laboratory say they have found the first, long-sought proof that a decades-old scientific model of material behavior can be used to simulate and understand high-temperature superconductivity - an important step toward producing and controlling this puzzling phenomenon at will.
The simulations they ran, published in Science today, suggest that researchers might be able to toggle superconductivity on and off in copper-based materials called cuprates by tweaking their chemistry so electrons hop from atom to atom in a particular pattern -- as if hopping to the atom diagonally across the street rather than to the one next door.
"The big thing you want to know is how to make superconductors operate at higher temperatures and how to make superconductivity more robust," said study co-author Thomas Devereaux, director of the Stanford Institute for Materials and Energy Sciences (SIMES) at SLAC. "It's about finding the knobs you can turn to tip the balance in your favor."
The biggest obstacle to doing that, he said, has been the lack of a model -- a mathematical representation of how a system behaves -- that describes this type of superconductivity, whose discovery in 1986 raised hopes that electricity might someday be transmitted with no loss for perfectly efficient power lines and maglev trains.
While scientists thought the Hubbard model, used for decades to represent electron behavior in numerous materials, might apply to cuprate high-temperature superconductors, until now they had no proof, said Hong-Chen Jiang, a SIMES staff scientist and co-author of the report.
"This has been a major unsolved problem in the field -- does the Hubbard model describe high-temperature superconductivity in the cuprates, or is it missing some key ingredient?" he said. "Because there are a number of competing states in these materials, we have to rely on unbiased simulations to answer these questions, but the computational problems are very difficult, and so progress has been slow."
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