FORTH finds electron transfer, not layer spacing, controls superconductivity

Photo: Katerina Koti · iesl.forth.gr

Synchrotron X-rays at the European Synchrotron Radiation Facility in France were instrumental in revealing how electron transfer, not layer spacing, controls superconductivity in iron-based materials. Research led by Dr. PhD candidate Myrsini Kaitatzi, first author of the resulting publication in Solids 220, 114128 (2027), details findings that provide important experimental evidence that microscopic electronic structure, not structural expansion alone, is central to understanding unconventional superconductivity.

Electron Transfer, Not Layer Spacing, Governs Superconductivity

Molecular insertion alters electron transfer within iron-based materials, a new study reveals, rather than simply increasing the distance between conducting layers. Researchers demonstrated this by modifying electron distribution, offering a potential pathway to tune superconductivity, the ability of a material to conduct electricity with zero resistance. This finding challenges the long-held assumption that expanding the space between layers was the primary control mechanism for achieving superconductivity at higher temperatures.

Synchrotron Mössbauer spectroscopy, combined with high-resolution X-ray diffraction, provided critical insight into these microscopic changes, probing both the atomic structure and the local electronic environment of iron. These results suggest that manipulating electron behavior, rather than physical spacing, is key to unlocking more effective superconducting materials.

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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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