Physics, Quantum Physics

Superconductivity in FeSe resists disruption from zinc impurities

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November 12, 2026 by Ivy Delaney

The superconducting transition temperature of iron selenide (FeSe) exhibited surprising behavior when researchers at the Hefei Institutes of Physical Science of the Chinese Academy of Sciences introduced zinc into its crystal structure. Rather than a simple decline, the temperature initially decreased, then increased, and finally decreased again with rising zinc concentration.

This non-monotonic response indicates that impurities and the material’s electronic structure compete in ways that defy expectations of weakened superconductivity. According to the researchers, these findings show that FeSe retains its two-gap superconducting state despite zinc doping, offering new insights into superconductivity in iron-based materials.

Zinc Doping Induces Non-Monotonic Superconducting Transition Temperatures in FeSe

The team’s work, published in Physical Review B, used the Steady High Magnetic Field Facility to probe the material’s properties. The introduction of zinc, a nonmagnetic element, caused local distortions and defects within the FeSe crystal structure, increasing electron scattering, yet the superconducting properties did not simply degrade as anticipated. Measurements of low-temperature specific heat provided further evidence supporting a two-gap superconducting state, a model where two distinct energy gaps govern the behavior of superconducting electrons.

The relative contributions of these two energy gaps remained largely unchanged even as the zinc concentration varied, suggesting that the scattering induced by zinc between different electronic bands is relatively weak. One gap exhibits an anisotropic extended s-wave structure, while the other is an isotropic s-wave gap, a specific arrangement of electron pairing. The team’s phase diagram, generated from magnetization, resistivity and specific-heat measurements, visually demonstrates this non-monotonic behavior of the critical temperature as a function of zinc concentration, providing a detailed map of the material’s superconducting properties under varying doping levels.

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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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