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Scientists Discover Strange New Quantum Behavior in Superconducting Material




A research team has provided the first experimental proof that flat electronic bands in a kagome superconductor are active and directly shape electronic and magnetic behaviors.

Researchers from Rice University, working with international partners, have found the first clear evidence of active flat electronic bands within a kagome superconductor. The discovery marks an important step toward creating new strategies for designing quantum materials, including superconductors, topological insulators, and spin-based electronics, which could play a central role in advancing future electronics and computing.

The findings, published on August 14 in Nature Communications, focus on the chromium-based kagome metal CsCr₃Sb₅, a material that becomes superconducting when placed under pressure.

Kagome metals are defined by their unique two-dimensional lattice of corner-sharing triangles. Recent theories have suggested that these structures can host compact molecular orbitals, or standing-wave patterns of electrons, which may enable unconventional superconductivity and unusual magnetic states driven by electron correlation effects.

In most known materials, such flat bands are positioned too far from the relevant energy levels to influence behavior. In CsCr₃Sb₅, however, they play an active role and directly shape the properties of the material.

“Our results confirm a surprising theoretical prediction and establish a pathway for engineering exotic superconductivity through chemical and structural control,” said Dai, the Sam and Helen Worden Professor of Physics and Astronomy.

The finding provides experimental proof for ideas that had only existed in theoretical models. It also shows how the intricate geometry of kagome lattices can be used as a design tool for controlling the behavior of electrons in solids.

“By identifying active flat bands, we’ve demonstrated a direct connection between lattice geometry and emergent quantum states,” said Yi, an associate professor of physics and astronomy.

Experimental Techniques and Findings

The research team employed two advanced synchrotron techniques alongside theoretical modeling to investigate the presence of active standing-wave electron modes. They used angle-resolved photoemission spectroscopy (ARPES) to map electrons emitted under synchrotron light, revealing distinct signatures associated with compact molecular orbitals. Resonant inelastic X-ray scattering (RIXS) measured magnetic excitations linked to these electronic modes.

“The ARPES and RIXS results of our collaborative team give a consistent picture that flat bands here are not passive spectators but active participants in shaping the magnetic and electronic landscape,” said Si, the Harry C. and Olga K. Wiess Professor of Physics and Astronomy, “This is amazing to see given that, until now, we were only able to see such features in abstract theoretical models.”

Theoretical support was provided by analyzing the effect of strong correlations starting from a custom-built electronic lattice model, which replicated the observed features and guided the interpretation of results. Fang Xie, a Rice Academy Junior Fellow and co-first author, led that portion of the study.

Obtaining such precise data required unusually large and pure crystals of CsCr₃Sb₅, synthesized using a refined method that produced samples 100 times larger than previous efforts, said Zehao Wang, a Rice graduate student and co-first author.

The work underscores the potential of interdisciplinary research across fields of study, said Yucheng Guo, a Rice graduate student and co-first author who led the ARPES work.

“This work was possible due to the collaboration that consisted of materials design, synthesis, electron and magnetic spectroscopy characterization, and theory,” Guo said.

#HighEnergyPhysics#ParticlePhysics#QuantumPhysics#AstroparticlePhysics#ColliderPhysics#HiggsBoson#LHC#QuantumFieldTheory#NeutrinoPhysics#PhysicsResearch#ComputationalScience#DataScience#ScientificComputing#NumericalMethods#HighPerformanceComputing#MachineLearningInScience#BigData#AlgorithmDevelopment#SimulationScience#ParallelComputing

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