Production of methanol from CO2 electroreduction at Cu2O and Cu2O/ZnO-based electrodes in aqueous solution

In this study, we examine the performance of Cu2O and Cu2O/ZnO surfaces in a filter-press electrochemical cell for the continuous electroreduction of CO2 into methanol. The electrodes are prepared by airbrushing the metal particles onto a porous carbon paper and then are electrochemically characterized by cyclic voltammetry analyses. Particular emphasis is placed on evaluating and comparing the methanol production and Faradaic efficiencies at different loadings of Cu2O particles (0.5, 1 and 1.8 mg cm−2), Cu2O/ZnO weight ratios (1:0.5, 1:1 and 1:2) and electrolyte flow rates (1, 2 and 3 ml min−1 cm−2). The electrodes including ZnO in their catalytic surface were stable after 5 h, in contrast with Cu2O-deposited carbon papers that present strong deactivation with time. The maximum methanol formation rate and Faradaic efficiency for Cu2O/ZnO (1:1)-based electrodes, at an applied potential of −1.3 V vs. Ag/AgCl, were r = 3.17 × 10−5 mol m−2 s−1 and FE = 17.7 %, respectively. Consequently, the use of Cu2O–ZnO mixtures may be of application for the continuous electrochemical formation of methanol, although further research is still required in order to develop highly active, selective and stable catalysts the electroreduction of CO2 to methanol.

Real-time 3D semi-local surface patch extraction using GPGPU

Feature vectors can be anything from simple surface normals to more complex feature descriptors. Feature extraction is important to solve various computer vision problems: e.g. registration, object recognition and scene understanding. Most of these techniques cannot be computed online due to their complexity and the context where they are applied. Therefore, computing these features in real-time for many points in the scene is impossible. In this work, a hardware-based implementation of 3D feature extraction and 3D object recognition is proposed to accelerate these methods and therefore the entire pipeline of RGBD based computer vision systems where such features are typically used. The use of a GPU as a general purpose processor can achieve considerable speed-ups compared with a CPU implementation. In this work, advantageous results are obtained using the GPU to accelerate the computation of a 3D descriptor based on the calculation of 3D semi-local surface patches of partial views. This allows descriptor computation at several points of a scene in real-time. Benefits of the accelerated descriptor have been demonstrated in object recognition tasks. Source code will be made publicly available as contribution to the Open Source Point Cloud Library.