Journal of Physics: Condensed Matter最新文献

A version of the Mexican-hat Hamiltonian is used to study high-temperature transport properties of a two-dimensional weakly doped semiconductor with electron-hole symmetric bands. For a finite doping level and a temperature-dependent band gap, we find a closed analytical form of the temperature-dependent chemical potential. The effective concentrations of charge carriers participating in transport coefficients are analyzed in the space spanned by the total electron concentration and temperature. It is shown that these concentrations are the sum of a residual contribution and two thermally activated contributions, with a complicated dependence on temperature. The analytical expression for the Hall coefficient RHis also found. It is argued that it is a non-monotonic function of the doping level with the maximum at the doping nmax that is a linear function of temperature at high enough temperatures. The analysis of the real part of the interband conductivity shows that it is inversely proportional to incoming photon energy at low temperatures and that it is nearly constant over a wide energy range at high temperatures. This results are expected to be of significant importance in understanding transport and optical properties of weakly doped two-dimensional semiconductors with nearly symmetric parabolic bands. .

Heterostructures, such as van der Waals (vdW) heterostructures, provide a versatile platform for engineering the physical properties of two-dimensional (2D) layered materials, spanning electronics, mechanics, optics, as well as electron-phonon couplings. Furthermore, vdW heterostructures, which are composed of metal/semiconductor or semiconductor/semiconductor combinations, not only maintain the unique properties of their individual constituents but also exhibit tunable physical and chemical properties that can be externally adjusted through strain, heat, and electric fields. These externally tunable properties offer significant advances in the fields of solid-state devices and renewable energy applications. Additionally, 2D material-based heterostructures, such as those composed of 0D clusters or quantum dots, as well as 1D nanotubes/wires in combination with 2D materials, also show immense potential for advancing next-generation nanodevices. The vast design space of vdW heterostructures enables their versatile applications spanning numerous fields, such as light-emitting diodes, field-effect transistors, photocatalysis, solar cells, photodetectors, and so on. In the Special Issue of Journal of Physics: Condensed Matter, entitled "Two-dimensional Materials-based Heterostructures for Next-generation Nanodevices", we have gathered a comprehensive collection of 14 articles, presenting the latest achievements in the fields of designing novel 2D materials and 2D heterostructures. Below, we have briefly condensed the essential research findings from these studies. .

We provide the superconducting density of states of the pnictide superconductor LaRu₂P₂(T_c= 4.1 K), measured using millikelvin Scanning Tunneling Microscopy. From the tunneling conductance, we extract a density of states which shows the opening of a s-wave single superconducting gap. The temperature dependence of the gap also follows BCS theory. Under magnetic fields, vortices present Caroli de Gennes Matricon states, although these are strongly broadened by defect scattering. From the vortex core size we obtain a superconducting coherence length of ξ= 50 nm, compatible with the value extracted from macroscopic H_c2measurements. We discuss the comparison between s-wave LaRu₂P₂and pnictide unconventional multiple gap and strongly correlated Fe based superconductors.

The striking electronic characteristics of graphene trigger immense interests and continual explora-tions for new two-dimensional (2D) Dirac materials. By first-principles electronic structure calculations, we here identify a new set of 2D semimetals in hydro-/halogen embedding trigonalδ₆borophene, namelyδ₆-B₃X (X = H, F, Cl), that possess the graphene-like massless Dirac fermions. Owing to the central hollow B atoms strongly hybridized to the hydro-/halogen adatoms, adequate charge transfer is induced from the hollow B to the basal honeycomb B sublattice, which electronically stabilizes the 2D sheet and decisively endows a robust (intrinsic and stable-against-strains) graphene-like Dirac cone state. The predicted high energetic, dynamic and thermal stabilities, combined with pretty geometrical match to the commonly utilized Ag/Au(111) substrates, support their experimental viabilities. Our prediction provides a new branch for exploring the intriguing 2D Dirac fermionic states in versatile boron element and its derivatives.

In the study of strongly correlated electrons, one of the challenging core tasks is to develop the potential techniques for direct detection of the many-body correlations of strongly correlated electrons. The(γ,2e)photoemission technique has been developed to investigate the two-body correlations of the target correlated electrons. In this article, we will focus on this technique for the correlated electrons near the Fermi energy in condensed matter. The coincidence detection probability of the two emitted electrons in the(γ,2e)photoemission measurement is shown to be relevant to a two-body Bethe-Salpeter wave function, which describes the dynamical two-body correlations of the target correlated electrons near the Fermi energy. As the coincidence detection probability involves an electron-electron interaction matrix element, the arbitrary momentum and/or energy transfer due to this electron-electron interaction makes the(γ,2e)photoemission technique fail to reveal the inner-pair structures of the two-body Bethe-Salpeter wave function. However, the center-of-mass momentum and energy of the two-body Bethe-Salpeter wave function can be distinctly resolved. Thus, the(γ,2e)photoemission technique can provide the center-of-mass physics of the two-body correlations of the target correlated electrons. It will be one potential technique to study the center-of-mass physics of the Cooper pairs in superconductor.

We study a hybrid system of a plasmonic cavity coupled to a pair of different molecular vibration modes with the strong optomechanical-like interactions. Here, this plasmonic cavity is considered as a quantum data bus and then assist several applications. For instance, it can first establish a bimolecular interface to ensure the reciprocal or non-reciprocal information transmission, and then engineer both molecules into the steady-state quantum entanglement of the continuous variable through the dissipative method. In contrast to the traditional optomechanical system, this hybrid system can provide the stronger optomechanical-like interactions and more convenient controls to the molecular quantum units. This investigation is believed to be able to further expand the practical application range of quantum technology. .

The search for room-temperature superconductors is a major challenge in modern physics. The discovery of copper-oxide superconductors in 1986 brought hope but also revealed complex mechanisms that are difficult to analyze and compute. In contrast, the traditional electron-phonon coupling (EPC) mechanism facilitated the practical realization of superconductivity in metallic hydrogen. Since 2015, the discovery of new hydrogen compounds has shown that EPC can enable room-temperature superconductivity under high pressures, driving extensive research. Advances in computational capabilities, especially exascale computing, now allow for the exploration of millions of materials. This paper reviews newly predicted superconducting systems in 2023-2024, focusing on hydrides, boron-carbon systems, and compounds with nitrogen, carbon, and pure metals. Although many computationally predicted high-Tc superconductors were not experimentally confirmed, some low-temperature superconductors were successfully synthesized. This paper provides a review of these developments and future research directions.

The combination of antiferromagnetism and topological properties in Mn3X (X = Sn, Ge,Ga) offers a unique platform to explore novel spin-dependent phenomena and develop innovative spintronic devices. Here, we have systematically investigated the phase transition of Mn3Ga thin films on SiO2(001)/Si substrates under various growth parameters such as seeding layer structure, annealing conditions, and film thickness. The relatively thick Mn3Ga films grown with Ru seeding exhibit a variety of polycrystalline hexagonal phases, including (002), and (201). The addition of a Ta layer to the conventional Ru seeding layer promotes the formation of nearly single-crystal antiferromagnetic Mn3Ga(002) phase from the relatively thin Mn3Ga films after annealing at 773 K. The investigation of the growth mechanism of Mn3Ga polycrystalline thin films provides a reference strategy for exploring Mn-based antiferromagnetic spintronic devices. .

Atom probe tomography (APT) is a powerful technique for three-dimensional atomic-scale imaging, enabling the accurate analysis on the compositional distribution at the nanoscale. How to accurately reconstruct crystallographic information from APT data, however, is still a great challenge due to the intrinsic nature of the APT technique. In this paper, we propose a novel approach that consists of the modified forward simulation process and the backward machine learning process to recover the tested crystal from APT data. The high-throughput forward simulations on Al single crystals of different orientations generate 10,000 original 3D images and data augmentation is implemented on the original images, resulting in 100,000 3D images. The big data allows the development of deep learning models and three deep learning algorithms of Convolutional Neural Network (CNN), Vision Transformer (ViT), and Variational Autoencoder (VAE) are used in the backward process. After training, the ViT model performs superior than the CNN and VAE models, which can recover the crystalline orientation outstandingly, as evaluated by the coefficient of determination R^2 and the Mean Percent Error (MPE), viz., R^2=0.93 and MPE=0.43%, R^2=0.97 and MPE=0.35%, and R^2=0.93 and MPE=0.77% for the rotation angles ϕ, ψ and θ, respectively, on the test dataset. The present work clearly demonstrates the capability of deep learning models in the recovery of the tested crystals from APT data, thereby paving the way for the further development of large artificial intelligent models of APT.