Abstract: Ando's surface roughness model is applied to metallic nanowires and extended beyond small roughness size and infinite barrier limit approximations for the wavefunction overlaps, such as the Prange-Nee approximation. Accurate and fast simulations can still be performed without invoking these overlap approximations by averaging over roughness profiles using finite domain distribution functions to obtain an analytic solution for the scattering rates. The simulations indicate that overlap approximations, while predicting a resistivity that agrees more or less with our novel approach, poorly estimate the underlying scattering rates. All methods show that a momentum gap between left- and right-moving electrons at the Fermi level, surpassing a critical momentum gap, gives rise to a substantial decrease in resistivity.

Abstract: The tunneling current between two crossed graphene ribbons is described invoking the empirical pseudopotential approximation and the Bardeen transfer Hamiltonian method. Results indicate that the density of states is the most important factor determining the tunneling current between small (similar to nm) ribbons. The quasi-one dimensional nature of graphene nanoribbons is shown to result in resonant tunneling.

Abstract: We have fabricated 9.7% efficient Cu2ZnSnSe4/CdS/ZnO solar cells by H2Se selenization of sequentially sputtered metal layers. Despite the good efficiency obtained, process control appears to be difficult. In the present contribution we compare the electrical and physical properties of two devices with nominal same fabrication procedure, but 1% and 9.7% power conversion efficiency respectively. We identify the problem of the lower performing device to be the segregation of ZnSe phases at the backside of the sample. This ZnSe seems to be the reason for the strong bias dependent photocurrent observed in the lower performing devices, as it adds a potential barrier for carrier collection. The reason for the different behavior of the two nominally same devices is not fully understood, but speculated to be related to sputtering variability.

Abstract: Additives in semiconductor metal oxides are commonly used to improve sensing behavior of gas sensors. Due to complicated effects of additives on the materials microstructure, adsorption sites and reactivity to target gases the sensing mechanism with modified metal oxides is a matter of thorough research. Herein, we establish the promoting effect of nanocrystalline zinc oxide modification by 1-7 at.% of indium on the sensitivity to CO gas due to improved nanostructure dispersion and concentration of active sites. The sensing materials were synthesized via an aqueous coprecipitation route. Materials composition, particle size and BET area were evaluated using X-ray diffraction, nitrogen adsorption isotherms, high-resolution electron microscopy techniques and EDX-mapping. Surface species of chemisorbed oxygen, OH-groups, and acid sites were characterized by probe molecule techniques and infrared spectroscopy. It was found that particle size of zinc oxide decreased and the BET area increased with the amount of indium oxide. The additive was observed as amorphous indium oxide segregated on agglomerated ZnO nanocrystals. The measured concentration of surface species was higher on In2O3-modified zinc oxide. With the increase of indium oxide content, the sensor response of ZnO/In2O3 to CO was improved. Using in situ infrared spectroscopy, it was shown that oxidation of CO molecules was enhanced on the modified zinc oxide surface. The effect of modifier was attributed to promotion of surface OH-groups and enhancement of CO oxidation on the segregated indium ions, as suggested by DFT in previous work.

Abstract: Non-charge-based logic devices are promising candidates for future logic circuits. Interest in studying and developing these devices has grown dramatically in the past decade as they possess key advantages over conventional CMOS technology. Due to their novel designs, a large number of micromagnetic simulations are required to fully characterize the behavior of these devices. The number and complexity of these simulations place large computational requirements on device development. We use state-of-the-art machine learning techniques to expedite identification of their behavior. Several intelligent sampling strategies are combined with machine learning multi-class classification models. These techniques are applied to a recently developed exchange-driven magnetic logic scheme that utilizes direct exchange coupling as the main driver.