Observation of Mott instability at the valence transition of f-electron system

Abstract

Mott physics plays a critical role in materials with strong electronic correlations. Mott insulator-to-metal transition can be driven by chemical doping, external pressure, temperature and gate voltage, which is often seen in transition metal oxides with 3d electrons near the Fermi energy (e.g. cuprate superconductor). In 4f-electron system, however, the insulator-to-metal transition is mostly driven by Kondo hybridization and the Mott physics has rarely been explored in experiments. Here, by combining the angle-resolved photoemission spectroscopy and strongly correlated band structure calculations, we show that an unusual Mott instability exists in YbInCu4 accompanying its mysterious first-order valence transition. This contrasts with the prevalent Kondo picture and demonstrates that YbInCu4 is a unique platform to explore the Mott physics in Kondo lattice systems. Our work provides important insight for the understanding and manipulation of correlated quantum phenomena in the f-electron system.

Localization degree of 4f electrons in Ce3Pb intermetallic compound: A DFT + DMFT study

“…Note, however, that j = 7/2 manifold is nearly empty. In addition, j = 7/2 bands are almost above the Fermi level and removed from the Fermi level, implying that j = 7/2 manifold has no contribution to the conductive electrons 25 …”

Recent Progress in Anti‐Tumor Nanodrugs Based on Tumor Microenvironment Redox Regulation

“…Up to now, a number of related therapies (e.g., PDT, SDT, and RT) have been applied in the clinical anti-tumor treatment or entered the clinical trial stage (Table 4). [240][241][242][243][244] Compared with traditional surgery, new ROS-mediated therapies such as PDT and SDT have multiple therapeutic advantages such as high selectivity, a wide range of applications, and low toxic side effects. However, nanodrugs using the above strategies to treat skin superficial and deep tumors are mainly still in the preclinical research phase.…”

Automated Terrain Classification with a Bayesian Hyperparameter Optimized Deep Supervised Autoencoder Model

“…To perform well in prediction, a typical goal is representation learning, in which the input xi is transformed into a new representation from which a simple predictor like a linear predictor may be learned. AEs are known for their ability to perform representation learning-see, for example, Yang et al (2022).…”