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.

Abstract: We investigate the diffusive electron-transport properties of charge-doped graphene ribbons and nanoribbons with imperfect edges. We consider different regimes of edge scattering, ranging from wide graphene ribbons with (partially) diffusive edge scattering to ribbons with large width variations and nanoribbons with atomistic edge roughness. For the latter, we introduce an approach based on pseudopotentials, allowing for an atomistic treatment of the band structure and the scattering potential, on the self-consistent solution of the Boltzmann transport equation within the relaxation-time approximation and taking into account the edge-roughness properties and statistics. The resulting resistivity depends strongly on the ribbon orientation, with zigzag (armchair) ribbons showing the smallest (largest) resistivity and intermediate ribbon orientations exhibiting intermediate resistivity values. The results also show clear resistivity peaks, corresponding to peaks in the density of states due to the confinement-induced subband quantization, except for armchair-edge ribbons that show a very strong width dependence because of their claromatic behavior. Furthermore, we identify a strong interplay between the relative position of the two valleys of graphene along the transport direction, the correlation profile of the atomistic edge roughness, and the chiral valley modes, leading to a peculiar strongly suppressed resistivity regime, most pronounced for the zigzag orientation.

Abstract: The study presents a 2D fluid plasma model of a gliding arc discharge for dissociation of CO2 which allows its subsequent conversion into value-added chemicals. The model is based on the balance equations of charged and neutral particles, the electron energy balance equation, the gas thermal balance equation and the current continuity equation. By choosing the modeling domain to be the plane perpendicular to the arc current, the numerical calculations are significantly simplified. Thus, the model allows us to explore the influence of the gas instabilities (turbulences) on the energy efficiency of CO2 conversion. This paper presents results for plasma parameters at different values of the effective turbulent thermal conductivity leading to enhanced energy transport.

Abstract: Identifying priority areas is an essential step in developing management strategies to reduce pesticide loads in surface water. A spatially explicit model-based approach was developed to detect priority areas for diffuse pesticide pollution at catchment scale. The method uses available datasets and considers different pesticide pathways in the environment post-application. The approach was applied in a catchment area in SE Flanders (Belgium) as a case study. Calculated risk areas were obtained using detailed landscape data and combining pesticide emissions and hydrological connectivity. The risk areas obtained were further compared with an alternative observation-based method, developed specifically for this study site that includes long-term field observations and local expert knowledge. Both methods equally classified 50% of the areas. The impact of crop rotation on the calculated risk was analysed. High-risk areas were identified and added to a cumulative map over all five years to evaluate temporal variations. The model-based approach was used for the initial identification of risk areas at the study site. The tool helps to prioritise zones and detect particular fields to target landscape mitigation measures to reduce diffuse pesticide pollution reaching surface water bodies.

Abstract: This work reports on developments in the field of wide band gap Cu2ZnXY4 (with X = Sn, Si or Ge, and Y = S, Se) kesterite thin film solar cells. An overview on recent developments and the current understanding of wide band gap kesterite absorber layers, alternative buffer layers, and suitable transparent back contacts is presented. Cu2ZnGe(S,Se)(4) absorbers with absorber band gaps up to 1.7 eV have been successfully developed and integrated into solar cells. Combining a CdS buffer layer prepared by an optimized chemical bath deposition process with a 1.36 eV band gap absorber resulted in a record Cu2ZnGeSe4 cell efficiency of 7.6%, while the highest open-circuit voltage of 730 mV could be obtained for a 1.54 eV band gap absorber and a Zn(O,S) buffer layer. Employing InZnOx or TiO2 protective top layers on SnO2:In transparent back contacts yields 85-90% of the solar cell performance of reference cells (with Mo back contact). These advances show the potential as well as the challenges of wide band gap kesterites for future applications in high-efficiency and low-cost tandem photovoltaic devices.