We propose new carbon nanoribbons with Dirac bands. Hydrogen edge passivation triggers RTD behavior in molecular electronic devices.

In the present work we propose a novel treatment to investigate ballistic electron transport under mechanical strain in a 1-D molecular bridge composed of alternating simple and triple bonds (polyyne) connected between two Single-Wall Carbon Nanotube (SWCNT) electrodes. Calculations with the DFT-NEGF methodology were performed in order to analyze this system at low values of mechanical strain (compression and distension) and at equilibrium length in the presence of bias voltages applied along the longitudinal direction. The results show that, while the mechanical strain displaces the energy levels and changes the band gap in the nanotube caps, the applied bias breaks the degeneracy in the nanotube cap states and defines the electrical conductance along the system. The analysis of the PDOS suggests that the main contribution to the electrical current comes from the superposition of the nanotube cap states, which is in agreement with the transmission calculation, and this device can be employed as a transistor observed in the I–V curve.

The measurement and analysis of partial discharges (PD) are like medical examinations, such as Electrocardiogram (ECG), in which there are preestablished criteria. However, each patient will present his particularities that will not necessarily imply his condemnation. The consolidated method for PD processing has high qualifications in the statistical analysis of insulation status of electric generators. However, although the IEEE 1434 standard has well-established standards, it will not always be simple to classify signals obtained in the measurement of the hydro generator coupler due to variations in the same type of PD incidence that may occur as a result of the uniqueness of each machine subject to staff evaluation. In order to streamline the machine diagnostic process, a tool is suggested in this article that will provide this signal classification feature. These measurements will be established in groups that represent each known form of partial discharge established by the literature. It was combined with supervised and unsupervised techniques to create a hybrid method that identified the patterns and classified the measurement signals, with a high degree of precision. This paper proposes the use of data-mining techniques based on clustering to group the characteristic patterns of PD in hydro generators, defined in standards. Then, random forest decision trees were trained to classify cases from new measurements. A comparative analysis was performed among eight clustering algorithms and random forest for choosing which is the superior combination to make a better classification of the equipment diagnosis. R² was used for assessing the data trend.