Bayesian models for finding and grouping junctions

In this paper, we propose two Bayesian methods for detecting and grouping junctions. Our junction detection method evolves from the Kona approach, and it is based on a competitive greedy procedure inspired in the region competition method. Then, junction grouping is accomplished by finding connecting paths between pairs of junctions. Path searching is performed by applying a Bayesian A* algorithm that has been recently proposed. Both methods are efficient and robust, and they are tested with synthetic and real images.

Molecular signature of highly conductive metal-molecule-metal junctions

The simplicity of single-molecule junctions based on direct bonding of a small molecule between two metallic electrodes makes them an ideal system for the study of fundamental questions related to molecular electronics. Here we study the conductance properties of six different types of molecules by suspending individual molecules between Pt electrodes. All the molecular junctions show a typical conductance of about 1G0 which is ascribed to the dominant role of the Pt contacts. However, despite the metalliclike conductivity, the individual molecular signature is well expressed by the effect of molecular vibrations in the inelastic contribution to the conductance.

Intrinsic spin noise in MgO magnetic tunnel junctions

We consider two intrinsic sources of noise in ultra-sensitive magnetic field sensors based on MgO magnetic tunnel junctions, coming both from 25 Mg nuclear spins (I = 5/2, 10% natural abundance) and S = 1 Mg-vacancies. While nuclear spins induce noise peaked in the MHz frequency range, the vacancies noise peaks in the GHz range. We find that the nuclear noise in submicron devices has a similar magnitude than the 1/f noise, while the vacancy-induced noise dominates in the GHz range. Interestingly, the noise spectrum under a finite magnetic field gradient may provide spatial information about the spins in the MgO layer.