Nitrogen-doped epitaxial graphene grown on SiC(000?1) was prepared by exposing the surface to an atomic nitrogen flux. Using Scanning Tunneling Microscopy (STM) and Spectroscopy (STS), supported by Density Functional Theory (DFT) calculations, the simple substitution of carbon by nitrogen atoms has been identified as the most common doping configuration. High-resolution images reveal a reduction of local charge density on top of the nitrogen atoms, indicating a charge transfer to the neighboring carbon atoms. For the first time, local STS spectra clearly evidenced the energy levels associated with the chemical doping by nitrogen, localized in the conduction band. Various other nitrogen-related defects have been observed. The bias dependence of their topographic signatures demonstrates the presence of structural configurations more complex than substitution as well as hole-doping.Comment: 5 pages, accepted in PR

Single-molecule break-junction measurements are intrinsically stochastic in nature, requiring the acquisition of large datasets of "breaking traces" to gain insight into the generic electronic properties of the molecule under study. For example, the most probable conductance value of the molecule is often extracted from the conductance histogram built from these traces. In this letter, we present an unsupervised and reference-free machine learning tool to improve the determination of the conductance of oligo(phenylene ethynylene)dithiol from mechanically controlled break-junction (MCBJ) measurements. Our method allows for the classification of individual breaking traces based on an image recognition technique. Moreover, applying this technique to multiple merged datasets makes it possible to identify common breaking behaviors present across different samples, and therefore to recognize global trends. In particular, we find that the variation in the extracted molecular conductance can be significantly reduced resulting in a more reliable estimation of molecular conductance values from MCBJ datasets. Finally, our approach can be more widely applied to different measurement types which can be converted to two-dimensional images.

Enhanced upper critical field, critical current density, and thermal activation energy in new ytterbium doped CeFeAsO0.9F0.1 superconductor J. Appl. Phys. 113, 043924 (2013) Temperature-and field-dependent critical currents in [(Bi,Pb)2Sr2Ca2Cu3Ox]0.07(La0.7Sr0.3MnO3)0.03 thick films grown on LaAlO3 substrates J. Appl. Phys. 113, 043916 (2013) Transport critical current measurement apparatus using liquid nitrogen cooled high-Tc superconducting magnet with variable temperature insert Rev. Sci. Instrum. 84, 015113 (2013) Additional information on Appl. Phys. Lett.

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When coherent charge carriers cross micron-scale cavities, their dynamics can be governed by a few resonant states, also called "quantum scars", determined by the cavity geometry. Quantum scars can be described using theoretical tools but have also been directly imaged in the case of high-quality semiconductor cavities as well as in disordered graphene devices, thanks to scanning gate microscopy (SGM). Here, we discuss spatially resolved SGM images of low-temperature charge transport through a mesoscopic ring fabricated from high-quality monolayer graphene lying on top of hexagonal boron nitride. SGM images are decorated with a pattern of radial scars in the ring area, which is found to evolve smoothly and reappear when varying the charge-carrier energy. The energies separating recurrent patterns are found to be directly related to geometric dimensions of the ring. Moreover, a recurrence is also observed in simulations of the local density of states of a model graphene quantum ring. The observed recurrences are discussed in the light of recent predictions of relativistic quantum scars in mesoscopic graphene cavities.

Based on micro-Raman spectroscopy (μRS) and X-ray photoelectron spectroscopy (XPS), we study the structural damage incurred in monolayer (1L) and few-layer (FL) graphene subjected to atomic-layer deposition of HfO2 and Al2O3 upon different oxygen plasma power levels. We evaluate the damage level and the influence of the HfO2 thickness on graphene. The results indicate that in the case of Al2O3/graphene, whether 1L or FL graphene is strongly damaged under our process conditions. For the case of HfO2/graphene, μRS analysis clearly shows that FL graphene is less disordered than 1L graphene. In addition, the damage levels in FL graphene decrease with the number of layers. Moreover, the FL graphene damage is inversely proportional to the thickness of HfO2 film. Particularly, the bottom layer of twisted bilayer (t-2L) has the salient features of 1L graphene. Therefore, FL graphene allows for controlling/limiting the degree of defect during the PE-ALD HfO2 of dielectrics and could be a good starting material for building field effect transistors, sensors, touch screens and solar cells. Besides, the formation of Hf-C bonds may favor growing high-quality and uniform-coverage dielectric. HfO2 could be a suitable high-K gate dielectric with a scaling capability down to sub-5-nm for graphene-based transistors.

We studied the electron-transport properties of ten different amino acids and one dimer (di-methionine) using the mechanically controlled break-junction (MCBJ) technique. For methionine and cysteine, additional measurements were performed with the scanning tunneling microscope break-junction (STM-BJ) technique. By means of a statistical clustering technique, we identified several conductance groups for each of the molecules considered. Ab initio calculations revealed that the observed broad conductance distribution stems from the possibility of various binding geometries which can be formed during stretching combined with a multitude of possible conformational changes. The results suggest that it would be helpful to explore different experimental techniques such as recognition tunneling and conditions to help identify the nature of amino-acid-based junctions even further, for example, with the goal to establish a firm platform for their unambiguous recognition by tunneling break-junction experiments.

Machine-learning analyses enable identifying signatures of peptide conformers in single molecule electron transport experiments.