The behaviour of strongly correlated materials, and in particular unconventional superconductors, has been studied extensively for decades, but is still not well understood. This lack of theoretical understanding has motivated the development of experimental techniques for studying such behaviour, such as using ultracold atom lattices to simulate quantum materials. Here we report the realization of intrinsic unconventional superconductivity-which cannot be explained by weak electron-phonon interactions-in a two-dimensional superlattice created by stacking two sheets of graphene that are twisted relative to each other by a small angle. For twist angles of about 1.1 degrees-the first `magic' angle-the electronic band structure of this `twisted bilayer graphene' exhibits flat bands near zero Fermi energy, resulting in correlated insulating states at half-filling. Upon electrostatic doping of the material away from these correlated insulating states, we observe tunable zero-resistance states with a critical temperature of up to 1.7 kelvin. The temperature-carrier-density phase diagram of twisted bilayer graphene is similar to that of copper oxides (or cuprates), and includes dome-shaped regions that correspond to superconductivity. Moreover, quantum oscillations in the longitudinal resistance of the material indicate the presence of small Fermi surfaces near the correlated insulating states, in analogy with underdoped cuprates. The relatively high superconducting critical temperature of twisted bilayer graphene, given such a small Fermi surface (which corresponds to a carrier density of about 1011 per square centimetre), puts it among the superconductors with the strongest pairing strength between electrons. Twisted bilayer graphene is a precisely tunable, purely carbon-based, two-dimensional superconductor. It is therefore an ideal material for investigations of strongly correlated phenomena, which could lead to insights into the physics of high-critical-temperature superconductors and quantum spin liquids.
We demonstrate analytically and numerically that the dispersive Dirac cone emulating an epsilon-near-zero (ENZ) behavior is a universal property within a family of plasmonic crystals consisting of two-dimensional (2D) metals. Our starting point is a periodic array of 2D metallic sheets embedded in an inhomogeneous and anisotropic dielectric host that allows for propagation of transverse-magnetic (TM) polarized waves. By invoking a systematic bifurcation argument for arbitrary dielectric profiles in one spatial dimension, we show how TM Bloch waves experience an effective dielectric function that averages out microscopic details of the host medium. The corresponding effective dispersion relation reduces to a Dirac cone when the conductivity of the metallic sheet and the period of the array satisfy a critical condition for ENZ behavior. Our analytical findings are in excellent agreement with numerical simulations.
The water-oxygen-gold interface is important in many surface processes and has potential influence on heterogeneous catalysis. Herein, it is shown that water facilitates the migration of atomic oxygen on Au(110), demonstrating the dynamic nature of surface adsorption. We demonstrate this effect for the first time, using in situ scanning tunnelling microscopy (STM), temperature-programmed reaction spectroscopy (TPRS) and first-principles theoretical calculations. The dynamic interaction of water with adsorbed O maintains a high dispersion of O on the surface, potentially creating reactive transient species. At low temperature and pressure, isotopic experiments show that adsorbed oxygen on the Au(110) surface exchanges with oxygen in (H2O)-O-18. The presence of water modulates local electronic properties and facilitates oxygen exchange. Combining experimental results and theory, we propose that hydroxyl is transiently formed via proton transfer from the water to adsorbed oxygen. Hydroxyl groups easily recombine to regenerate water and adsorbed oxygen atoms, the net result of which is migration of the adsorbed oxygen without significant change in its overall distribution on the surface. The presence of water creates a dynamic surface where mobile surface oxygen atoms and hydroxyls are present, which can lead to a better performance of gold catalysis in oxidation reactions.
Defects on surfaces of semiconductors have a strong effect on their reactivity and catalytic properties. The concentration of different charge states of defects is determined by their formation energies. First-principles calculations are an important tool for computing defect formation energies and for studying the microscopic environment of the defect. The main problem associated with the widely used supercell method in these calculations is the error in the electrostatic energy, which is especially pronounced in calculations that involve surface slabs and two-dimensional materials. We present an internally consistent approach for calculating defect formation energies in inhomogeneous and anisotropic dielectric environments and demonstrate its applicability to the cases of the positively charged Cl vacancy on the NaCl (100) surface and the negatively charged S vacancy in monolayer MoS2.
The surface structure and composition of a multi-component catalyst are critical factors in determining its catalytic performance. The surface composition can depend on the local pressure of the reacting species, leading to the possibility that the flow through a nanoporous catalyst can affect its structure and reactivity. Here, we explore this possibility for oxidation reactions on nanoporous gold, an AgAu bimetallic catalyst. We use microscopy and digital reconstruction to obtain the morphology of a two-dimensional slice of a nanoporous gold sample. Using lattice Boltzmann fluid dynamics simulations along with thermodynamic models based on first-principles total-energy calculations, we show that some sections of this sample have low local O-2 partial pressures when exposed to reaction conditions, which leads to a pure Au surface in these regions, instead of the active bimetallic AgAu phase. We also explore the effect of temperature on the surface structure and find that moderate temperatures (approximate to 300-450 K) should result in the highest intrinsic catalytic performance, in apparent agreement with experimental results. Published by AIP Publishing.
Two-dimensional molybdenum disulfide (MoS2) is a promising material for the next generation of switchable transistors and photodetectors. In order to perform large-scale molecular simulations of the mechanical and thermal behavior of MoS2-based devices, an accurate interatomic potential is required. To this end, we have developed a Stillinger-Weber potential for monolayer MoS2. The potential parameters are optimized to reproduce the geometry (bond lengths and bond angles) of MoS2 in its equilibrium state and to match as closely as possible the forces acting on the atoms along a dynamical trajectory obtained from ab initio molecular dynamics. Verification calculations indicate that the new potential accurately predicts important material properties including the strain dependence of the cohesive energy, the elastic constants, and the linear thermal expansion coefficient. The uncertainty in the potential parameters is determined using a Fisher information theory analysis. It is found that the parameters are fully identified, and none are redundant. In addition, the Fisher information matrix provides uncertainty bounds for predictions of the potential for new properties. As an example, bounds on the average vibrational thickness of a MoS2 monolayer at finite temperature are computed and found to be consistent with the results from a molecular dynamics simulation. The new potential is available through the OpenKIM interatomic potential repository at https://openkim.org/cite/MO\_201919462778\_000. Published by AIP Publishing.
Electron transfer in molecular wires are of fundamental importance for a range of optoelectronic applications. The impact of electronic coherence and ionic vibrations on transmittance are of great importance to determine the mechanisms, and subsequently the type of wires that are most promising for applications. In this work, we use the real-time formulation of time-dependent density functional theory to study electron transfer through oligo-pphenylenevinylene (OPV) and the recently synthesized carbon bridged counterpart (COPV). A system prototypical of organic photovoltaics is setup by bridging a porphyrin-fullerene dyad, allowing a photo-excited electron to flow between the Zn-porphyrin (ZnP) chromophore and the C60 electron acceptor through the molecular wire. The excited state is described using the fully self-consistent.-SCF method. The state is then propagated in time using the real-time TD-DFT scheme, while describing ionic vibrations with classical nuclei. The charge transferred between porphyrin and C60 is calculated and correlated with the velocity autocorrelation functions of the ions. This provides a microscopic insight to vibrational and tunneling contributions to electron transport in linked porphyrin-fullerene dyads. We elaborate on important details in describing the excited state and trajectory sampling.
3D nanoporous metals made by alloy corrosion have attracted much attention due to various promising applications ranging from catalysis and sensing to energy storage and actuation. In this work we report a new process for the fabrication of 3D open nanoporous metal networks that phenomenologically resembles the nano-Kirkendall hollowing process previously reported for Ag/Au nanowires and nano particles, with the difference that the involved length scales are 10-100 times larger. Specifically, we find that dry oxidation of Ag70Au30 bulk alloy samples by ozone exposure at 150 C-omicron stimulates extremely rapid Ag outward diffusion toward the gas/alloy-surface interface, at rates at least 5 orders of magnitude faster than predicted on the basis of reported Ag bulk diffusion values. The micrometer-thick Ag depleted alloy region thus formed transforms into a 3D open nanoporous network morphology upon further exposure to methanol-O-2 at 150 C-omicron. These findings have important implications for practical applications of alloys, for example as catalysts, by demonstrating that large-scale compositional and morphological changes can be triggered by surface chemical reactions at low temperatures, and that dilute alloys such as Au97Ag3 are more resilient against such changes.
Two-dimensional (2D) materials offer a promising platform for exploring condensed matter phenomena and developing technological applications. However, the reduction of material dimensions to the atomic scale poses a challenge for traditional measurement and interfacing techniques that typically couple to macroscopic observables. We demonstrate a method for probing the properties of 2D materials via nanometer-scale nuclear quadrupole resonance (NQR) spectroscopy using individual atomlike impurities in diamond. Coherent manipulation of shallow nitrogen-vacancy (NV) color centers enables the probing of nanoscale ensembles down to approximately 30 nuclear spins in atomically thin hexagonal boron nitride (h-BN). The characterization of low-dimensional nanoscale materials could enable the development of new quantum hybrid systems, combining atomlike systems coherently coupled with individual atoms in 2D materials.
Wave polarization contains valuable information for electromagnetic signal processing; thus, the ability to manipulate it, can be extremely useful in modeling photonic devices. In this work, we propose designs comprised of one of the emerging and interesting media: black phosphorus (BP). Due to substantial in-plane anisotropy, a single slab of BP can be very efficient for controlling the polarization state of electromagnetic waves. We investigate BP slabs that filter the fields along one direction, or achieve polarization axis rotation, or convert linear polarization to circular. These slabs can be employed as components in numerous mid-IR integrated structures.
Nanoribbons of molybdenum disulfide (MoS2) are interesting one-dimensional (1D) nanostructures with intriguing electronic properties, consisting of a semiconducting bulk bounded by edges with metallic character. Edges of similar character can also be expected in other transition-metal dichalcogenide (TMDC) nanostructures. We report first-principles electronic structure calculations for the total energy and the band structure of four representative TMDCs, MoS2, MoSe2, WS2, WSe2, in various 1D nanoribbon configurations. We compare the thermodynamic stability and the electronic structure of the 2D bulk and 35 different quasi-1D nanoribbons for each of the four materials. In each case, we consider the reconstructions of the zigzag metal-terminated edge by adding different amounts of chalcogen adatoms. The 1D structures we investigated have positive edge energies when the chalcogen chemical potential is close to the energy of the bulk chalcogen phase, and negative edge energies for higher chemical potential values. We find that the reconstruction with two chalcogen adatoms per edge metal atom is the most stable under usual experimental conditions and that all 1D nanoribbon structures exhibit metallic character.
Small polaron formation in transition metal oxides, like the prototypical material rutile TiO2, remains a puzzle and a challenge to simple theoretical treatment. In our combined experimental and theoretical study, we examine this problem using Raman spectroscopy of photoexcited samples and real-time time-dependent density functional theory (RT-TDDFT), which employs Ehrenfest dynamics to couple the electronic and ionic subsystems. We observe experimentally the unexpected stiffening of the A(1g) phonon mode under UV illumination and provide a theoretical explanation for this effect. Our analysis also reveals a possible reason for the observed anomalous temperature dependence of the Hall mobility. Small polaron formation in rutile TiO2 is a strongly nonadiabatic process and is adequately described by Ehrenfest dynamics at time scales of polaron formation.
Iodine-doped graphene has recently attracted significant interest as a result of its enhanced conductivity and improved catalytic activity. Using density functional theory calculations, we obtain the formation energy, desorption rate, and electronic properties for graphene systems doped with polyiodide chains consisting of 1-6 iodine atoms in the low-concentration limit. We find that I-3 and I-5 act as p-type surface dopants that shift the Fermi level 0.46 and 0.57 eV below the Dirac point, respectively. For these two molecules, molecular orbital theory and analysis of the charge density show that doping transfers electronic charge to iodine pi* molecular orbitals oriented perpendicular to the graphene sheet. For even-length polyiodides, we find that I-6 and I-4 decompose to I-2, which readily desorbs at 300 K. Adsorption energy calculations further show that I-3 acts as an effective catalyst for the oxygen reduction reaction on graphene by stabilizing the rate-limiting OOH intermediate.
The graphene/MoS2 heterojunction formed by joining the two components laterally in a single plane promises to exhibit a low-resistance contact according to the Schottky-Mott rule. Here we provide an atomic-scale description of the structural, electronic, and magnetic properties of this type of junction. We first identify the energetically favorable structures in which the preference of forming C-S or C-Mo bonds at the boundary depends on the chemical conditions. We find that significant non-carrier related charge transfer between graphene and undoped MoS2 is localized at the boundary. We show that the abundant 1D boundary states substantially pin the Fermi level in the lateral contact between graphene and MoS2, in close analogy to the effect of 2D interfacial states in the contacts between 3D materials. Furthermore, we propose specific ways in which these effects can be exploited to achieve spin-polarized currents.
The dynamical glass transition is typically taken to be the temperature at which a glassy liquid is no longer able to equilibrate on experimental timescales. Consequently, the physical properties of these systems just above or below the dynamical glass transition, such as viscosity, can change by many orders of magnitude over long periods of time following external perturbation. During this progress toward equilibrium, glassy systems exhibit a history dependence that has complicated their study. In previous work, we bridged the gap between structure and dynamics in glassy liquids above their dynamical glass transition temperatures by introducing a scalar field called ``softness,'' a quantity obtained using machine-learning methods. Softness is designed to capture the hidden patterns in relative particle positions that correlate strongly with dynamical rearrangements of particle positions. Here we show that the out-of-equilibrium behavior of a model glass-forming system can be understood in terms of softness. To do this we first demonstrate that the evolution of behavior following a temperature quench is a primarily structural phenomenon: The structure changes considerably, but the relationship between structure and dynamics remains invariant. We then show that the relaxation time can be robustly computed from structure as quantified by softness, with the same relation holding both in equilibrium and as the system ages. Together, these results show that the history dependence of the relaxation time in glasses requires knowledge only of the softness in addition to the usual state variables.
Many structural and mechanical properties of crystals, glasses, and biological macromolecules can be modeled from the local interactions between atoms. These interactions ultimately derive from the quantum nature of electrons, which can be prohibitively expensive to simulate. Machine learning has the potential to revolutionize materials modeling due to its ability to efficiently approximate complex functions. For example, neural networks can be trained to reproduce results of density functional theory calculations at a much lower cost. However, how neural networks reach their predictions is not well understood, which has led to them being used as a ``black box'' tool. This lack of understanding is not desirable especially for applications of neural networks in scientific inquiry. We argue that machine learning models trained on physical systems can be used as more than just approximations since they had to ``learn'' physical concepts in order to reproduce the labels they were trained on. We use dimensionality reduction techniques to study in detail the representation of silicon atoms at different stages in a neural network, which provides insight into how a neural network learns to model atomic interactions. Published by AIP Publishing.