Tunneling spectroscopic signatures of charge doping and Mott-phase transition in α{\alpha}-RuCl3{_3} in proximity to graphite

Layered Mott insulator ${\alpha}$-RuCl${_3}$ has been intensely investigated as a possible candidate for Kitaev quantum spin liquid. In this letter, we report electron tunneling measurements on few-layer ${\alpha}$-RuCl${_3}$ in proximity to graphite, using a scanning tunneling microscopy. Signatures of charge doping have been observed in ${\alpha}$-RuCl${_3}$ layers stacking on the surface of graphite. Upon charge doping, we observed characteristic tunneling spectra that are dependent on the number of layers of ${\alpha}$-RuCl${_3}$. For a single ${\alpha}$-RuCl${_3}$ layer that is in direct contact with graphite, data shows charge states emerging in the Mott-gap regime with conservation of the Hubbard bands. For a bilayer of ${\alpha}$-RuCl${_3}$, data indicates an unconventional Mott-phase transition, where the Hubbard bands collapse accompanied by a dramatical gap-reduction. The results have thus demonstrated that tunneling into doped few-layer ${\alpha}$-RuCl${_3}$ is a useful probe to investigate this otherwise insulating spin-liquid candidate, providing fundamental information concerning electronic properties and theoretically proposed strong correlation physics in ${\alpha}$-RuCl${_3}$.Comment: Original data are available upon resonable request from corresponding autho

Experimental investigation of phase equilibria in the Cu-Ni-Si ternary system

The phase equilibria in the Cu-Ni-Si ternary system have been investigated experimentally by means of electron probe microanalysis (EPMA), X-ray diffraction (XRD) and differential scanning calorimetry (DSC) analysis on equilibrated ternary alloys. Three isothermal sections at 1073, 1173 and 1273 K are determined in the whole composition range. The existence of liquid phase and the ternary compound τ1 is confirmed at 1073 K. The binary γ (Cu 5Si), γ (Ni31Si12), δ (Ni 2Si) and 0 (Ni2Si) phases exhibit a considerable solubility of a third element. In addition, the c (Cu5Si) and h (Ni2Si) phases can be stabilized by the addition of Ni and Cu, respectively. ? 2013 Elsevier B.V. All rights reserved

Machine learning-guided design and development of metallic structural materials

In recent years, the advent of machine learning (ML) in materials science has provided a new tool for accelerating the design and discovery of new materials with a superior combination of mechanical properties for structural applications. In this review, we provide a brief overview of the current status of the ML-aided design and development of metallic alloys for structural applications, including high-performance copper alloys, nickel- and cobalt-based superalloys, titanium alloys for biomedical applications and high strength steel. We also present our perspectives regarding the further acceleration of data-driven discovery, development, design and deployment of metallic structural materials and the adoption of ML-based techniques in this endeavor

A literature review of green location routing problem: A comprehensive analysis of problems, objectives and methodologies

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