No Arabic abstract
With the advent of atomic-resolution transmission electron microscopy (AR-TEM) achieving sub-{AA}ngstrom image resolution and submillisecond time resolution, an era of visual molecular science where chemists can visually study the time evolution of molecular motions and reactions at atomistic precision has arrived. However, the appearance of experimental TEM images often differs greatly from that of conventional molecular models, and the images are difficult to decipher unless we know in advance the structure of the specimen molecules. The difference arises from the fundamental design of the molecular models that represent atomic connectivity and/or the electronic properties of molecules rather than the nuclear charge of atoms and electrostatic potentials that are felt by the e-beam in TEM imaging. We found a good correlation between the atomic number (Z) and the atomic size seen in TEM images when we consider shot noise in digital images. We propose here Z-correlated (ZC) atomic radii for modeling AR-TEM images of single molecules and ultrathin crystals, with which we can develop a good estimate of the molecular structure from the TEM image much more easily than with conventional molecular models. Two parameter sets were developed for TEM images recorded under high-noise (ZCHN) and low-noise (ZCLN) conditions. The new molecular models will stimulate the imaginations of chemists planning to use AR-TEM for their research.
Quantum simulation methods based on density-functional theory are currently deemed unfit to cope with atomic heat transport within the Green-Kubo formalism, because quantum-mechanical energy densities and currents are inherently ill-defined at the atomic scale. We show that, while this difficulty would also affect classical simulations, thermal conductivity is indeed insensitive to such ill-definedness by virtue of a sort of gauge invariance resulting from energy extensivity and conservation. Based on these findings, we derive an expression for the adiabatic energy flux from density-functional theory, which allows heat transport to be simulated using ab-initio equilibrium molecular dynamics. Our methodology is demonstrated by comparing its predictions with those of classical equilibrium and ab-initio non-equilibrium (Muller-Plathe) simulations for a liquid-Argon model, and finally applied to heavy water at ambient conditions.
Excitonic insulators (EI) arise from the formation of bound electron-hole pairs (excitons) in semiconductors and provide a solid-state platform for quantum many-boson physics. Strong exciton-exciton repulsion is expected to stabilize condensed superfluid and crystalline phases by suppressing both density and phase fluctuations. Although spectroscopic signatures of EIs have been reported, conclusive evidence for strongly correlated EI states has remained elusive. Here, we demonstrate a strongly correlated spatially indirect two-dimensional (2D) EI ground state formed in transition metal dichalcogenide (TMD) semiconductor double layers. An equilibrium interlayer exciton fluid is formed when the bias voltage applied between the two electrically isolated TMD layers, is tuned to a range that populates bound electron-hole pairs, but not free electrons or holes. Capacitance measurements show that the fluid is exciton-compressible but charge-incompressible - direct thermodynamic evidence of the EI. The fluid is also strongly correlated with a dimensionless exciton coupling constant exceeding 10. We further construct an exciton phase diagram that reveals both the Mott transition and interaction-stabilized quasi-condensation. Our experiment paves the path for realizing the exotic quantum phases of excitons, as well as multi-terminal exciton circuitry for applications.
Recording atomic-resolution transmission electron microscopy (TEM) images is becoming increasingly routine. A new bottleneck is then analyzing this information, which often involves time-consuming manual structural identification. We have developed a deep learning-based algorithm for recognition of the local structure in TEM images, which is stable to microscope parameters and noise. The neural network is trained entirely from simulation but is capable of making reliable predictions on experimental images. We apply the method to single sheets of defected graphene, and to metallic nanoparticles on an oxide support.
The electronic and optical properties of the paradigmatic F4TCNQ-doped pentacene in the low-doping limit are investigated by a combination of state-of-the-art many-body emph{ab initio} methods accounting for environmental screening effects, and a carefully parametrized model Hamiltonian. We demonstrate that while the acceptor level lies very deep in the gap, the inclusion of electron-hole interactions strongly stabilizes dopant-semiconductor charge transfer states and, together with spin statistics and structural relaxation effects, rationalize the possibility for room-temperature dopant ionization. Our findings reconcile available experimental data, shedding light on the partial vs. full charge transfer scenario discussed in the literature, and question the relevance of the standard classification in shallow or deep impurity levels prevailing for inorganic semiconductors.
The standard technique for sub-pixel estimation of atom positions from atomic resolution scanning transmission electron microscopy images relies on fitting intensity maxima or minima with a two-dimensional Gaussian function. While this is a widespread method of measurement, it can be error prone in images with non-zero aberrations, strong intensity differences between adjacent atoms or in situations where the neighboring atom positions approach the resolution limit of the microscope. Here we demonstrate mpfit, an atom finding algorithm that iteratively calculates a series of overlapping two-dimensional Gaussian functions to fit the experimental dataset and then subsequently uses a subset of the calculated Gaussian functions to perform sub-pixel refinement of atom positions. Based on both simulated and experimental datasets presented in this work, this approach gives lower errors when compared to the commonly used single Gaussian peak fitting approach and demonstrates increased robustness over a wider range of experimental conditions.