Hourglass fermion in an electronic band structure is protected by the nonsymmorphic symmetry of a layered semimetal Nb3SiTe6. Angle-resolved photoemission spectroscopy demonstrates the band features of an hourglass fermion in k-space, such as band crossings and nodal loops. Read more »
ALS Work Using ARPES
Angle-resolved photoemission spectroscopy (ARPES) is a technique in which a highly focused beam of x-rays is used to kick electrons out of the sample. By analyzing the ejected electrons' direction and energy, the researchers can obtain the material's band structure—a map of the electrons' behavior in the material.
Crossing from One to Two Dimensions in a Single Material
Low-dimensional materials exhibit excellent properties for use in next-generation electronic devices. Now, researchers have discovered an ideal platform for tuning between 1D and 2D physics, expanding the possibilities for device engineering and offering a versatile platform for the exploration of intriguing low-dimensional physics. Read more »
A Topological-Insulator Sandwich for Efficient Microelectronics
Researchers synthesized a topological insulator between two ferromagnetic layers and found that it is electronically characterized by a large magnetic band gap. The results open a new path toward lossless charge transport and perfect spin polarization, which could lead to the development of ultralow-energy electronics and spintronics. Read more »
A Novel Insulating State Emerges in a 2D Material
Researchers found a unique insulating state in an atomically thin material, driven by the combined effects of lattice–charge interactions and atomic-bond formation. The work provides a better understanding of charge ordering in two-dimensional materials and opens up new possibilities for achieving designer electronic properties. Read more »
What Drives Electron–Hole Asymmetry in Graphene?
Using the ALS, researchers determined that interactions between electrons are what give rise to the divergent effects observed when graphene is doped with electrons versus holes. A better understanding of this electron–hole asymmetry could lead to new avenues for generating exotic material phases, including unconventional superconductivity. Read more »
Scientists Uncover Surprising New Clues to Exotic Superconductors’ Superpowers
Researchers studied a model material (CeCoIn5) that mimics a cuprate superconductor that can be switched on and off using high magnetic fields. In chemical compositions where the superconductivity is strongest, the number of free electrons jumps, signifying a transition point. The researchers attributed this transition to the behavior of electrons associated with the cerium atoms. Read more »
Interlayer Coupling Drives Mysterious Phase Transition
Researchers found that a mysterious phase transition in an iron-based superconductor is driven by interactions between the material’s 2D layers. The results counter the assumption that interlayer coupling is negligible in such materials, suggesting instead that the interactions can be an effective way to tune superconductivity. Read more »
Scientists Discover ‘Secret Sauce’ Behind Exotic Properties of New Quantum Material
Kagome metals have long mystified scientists for their ability to exhibit collective behavior when cooled below room temperature. A research team has discovered that the kagome electrons’ unusual synchronicity is due to another behavior known as an electronic singularity, or the Van Hove singularity, which involves the relationship between the electrons’ energy and velocity. Read more »
The Elusive Electronic Structure of Liquid Metals Unveiled
Over 50 years ago, renowned physicists formulated theoretical models for the electronic structure of liquid metals. Now, for the first time, researchers observed the distinct spectral features predicted by those models, at the interface of a crystalline insulator (black phosphorus) and disordered dopants (alkali metals). Read more »
Autonomous Data Acquisition for Scientific Discovery
Researchers at large scientific facilities such as the ALS have applied a robust machine-learning technique to automatically optimize data gathering for a variety of experimental techniques. The work promises to enable experiments with large, complex datasets to be run more quickly, efficiently, and with minimal human intervention. Read more »
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