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Relativistic massless charged particles in a two-dimensional conductor can be guided by a onedimensional electrostatic potential, in an analogous manner to light guided by an optical fiber. We use a carbon nanotube to generate such a guiding potential in graphene and create a single mode electronic waveguide. The nanotube and graphene are separated by a few nanometers and can be controlled and measured independently. As we charge the nanotube, we observe the formation of a single guided mode in graphene that we detect using the same nanotube as a probe. This single electronic guided mode in graphene is sufficiently isolated from other electronic states of linear Dirac spectrum continuum, allowing the transmission of information with minimal distortion.Like a photon, an electron can be used as a carrier of information [1]. However, there is a limited number of tools to control a single electron [2] and the simple fact of guiding it coherently in a solid, like an optical fiber for light, is a technological feat [3, 4]. One-dimensional materials such as semiconducting nanowires naturally provide guidance for electrons, but in these materials, electrons can only be transmitted over short distances before losing its information [5]. Another possibility is through the edge channel of a two-dimensional electron gas in the quantum Hall regime, but a large magnetic field is required for the channel to be a single mode [6], which is crucial for the carried information not to be distorted during propagation.An alternative approach, conceptually similar to an optical fiber [7], is to use an electrostatic potential well on a two-dimensional electron gas to confine the movement of electrons along one direction ( Fig. 1a) [8][9][10][11]. Particularly, massless quasiparticles in graphene is an ideal platform for the realization of such electron guide. The quasirelativistic linear energy dispersion in graphene allows the wavefunction of the Dirac fermions travel with minimal distortion. Furthermore, it has been demonstrated that high mobility [12] allows electrons to be transmitted ballistically over several microns even at room temperature [13]. In addition, graphene can be encapsulated between thin dielectric layers of hexagonal-boron nitride (h-BN) [1], providing tunable electrostatic potential on the scale of a few nanometers, without degradation of the mobility. Electrostatic gating has produced various electron-optical elements, including lenses with negative refractive index [15] and filter-collimator switches [16].An ideal single mode electronic guide requires a deep potential well with a width much smaller than the wavelength of electrons in order to suppress scattering in the core of the waveguide [17]. The wavelength can reach around one hundred nanometers with experimentally accessible densities, and it is therefore crucial to be able to place extremely narrow gates close to the electron gas. The electronic modes generated by such a 1-dimensional -U 0 NT Au BN BN G 10µm 500nm CNT (a) (b) (c) charged CNT x y U(x,y) (d)...

We report the molecular beam epitaxial growth and characterization of high quality topological insulator Bi 2 Se 3 thin films on hexagonal boron nitride (h-BN). A two-step growth was developed, enhancing both the surface coverage and crystallinity of the films on h-BN. High-resolution transmission electron microscopy study showed an atomically abrupt and epitaxial interface formation between the h-BN substrate and Bi 2 Se 3 . We performed gate tuned magnetotransport characterizations of the device fabricated on the thin film and confirmed a high mobility surface state at the Bi 2 Se 3 /h-BN interface. The Berry phase obtained from Shubnikov−de Haas oscillations suggested this interfacial electronic state is a topologically protected Dirac state.

We have measured Coulomb drag between an individual single-walled carbon nanotube (SWNT) as a one-dimensional (1D) conductor and the two-dimensional (2D) conductor monolayer graphene, separated by a few-atom-thick boron nitride layer. The graphene carrier density is tuned across the charge neutrality point (CNP) by a gate, while the SWNT remains degenerate. At high temperatures, the drag resistance changes sign across the CNP, as expected for momentum transfer from drive to drag layer, and exhibits layer exchange Onsager reciprocity. We find that layer reciprocity is broken near the graphene CNP at low temperatures due to nonlinear drag response associated with temperature dependent drag and thermoelectric effects. The drag resistance shows power-law dependences on temperature and carrier density characteristic of 1D-2D Fermi liquid/Dirac fluid drag. The 2D drag signal at high temperatures decays with distance from the 1D source slower than expected for a diffusive current distribution, suggesting additional interaction effects in the graphene in the hydrodynamic transport regime.

Experiment planning algorithms are a required component of autonomous platforms for scientific discovery. Selecting a suitable optimization algorithm for a novel application is an important yet difficult choice a researcher has to make based on past empirical performance on similar tasks. To facilitate the evaluation of various algorithms on chemistry and materials science optimization tasks, we previously introduced OLYMPUS (Mach. Learn.: Sci. Technol. 2, 035021, 2021), a Python package providing a consistent and easy-to-use interface to numerous optimization algorithms and benchmark datasets. While the original package was limited to continuous parameters and single objectives, in this work we expand OLYMPUS' capabilities to include mixed (continuous, discrete, and categorical) parameter types and multiple objectives. Several experiment planning algorithms already contained in OLYMPUS are extended to handle categorical and discrete parameter types, and five additional planners are implemented (23 in total). We also provide 23 additional benchmark datasets taken from the chemistry and materials science literature (33 in total), covering a wide range of research areas, from chemical reaction optimization to materials manufacturing. Finally, the visualization capabilities of OLYMPUS are enhanced to allow for easy inspection of the results, and the core functionality of the package is embedded in a Streamlit web application for code-free usage. We demonstrate how OLYMPUS enables researchers to rapidly benchmark different optimization strategies and gain insight into their behavior by focusing on two case studies: the optimization of a Suzuki-Miyaura cross-coupling reaction with categorical reaction conditions, and the multi-objective optimization of redox-active materials. The updated OLYMPUS package provides practitioners with a large suite of tools to efficiently benchmark and analyze experiment planning algorithms on mixed-parameter and multi-objective optimization tasks.