Imidazolium ion-terminated self-assembled monolayer (SAM)-modified electrodes achieve CO2 conversion while suppressing hydrogen evolution. Immobile imidazolium ion on gold (Au) electrodes reduce CO2 at low overpotential. The distance between electrode and imidazolium ion separated by alkane thiol affects CO2 reduction activity. CO2 reduction current depends on the tunnel current rate. Although the product of CO2 reduction at the bare Au electrode is CO, SAM-modified electrodes produce ethylene glycol in aqueous electrolyte solution without CO evolution. The faradaic efficiency reached a maximum of 87%. CO2 reduction at SAM-modified electrodes is unaffected by reduction activity of Au electrode. This phenomenon shows that the reaction field of CO2 reduction is not the electrode surface but the imidazolium ion monolayer.

This study fabricates dye-sensitized solar cells (DSSCs) based on TiO(2)/multi-walled carbon nanotube (MWCNT) nanocomposite photoanodes obtained by the modified acid-catalyzed sol-gel procedure. Results show that incorporating MWCNTs into a TiO(2)-based electrode efficiently improves the physicochemical properties of the solar cell. The results of dye adsorption and cell performance measurements indicate that introducing MWCNTs would improve the roughness factor (from 834 to 1267) of the electrode and the charge recombination of electron/hole (e(-)/h(+)) pairs. These significant changes could lead to higher adsorbed dye quantities, photocurrent and DSSC cell performance. Nevertheless, a higher loading of MWCNTs causes light-harvesting competition that affects the light adsorption of the dye-sensitizer, and consequently reduces the cell efficiency. This study suggests an optimum MWCNT loading in the electrode of 0.3 wt%, and proposes a sol-gel synthesis procedure as a promising method of preparing the TiO(2)-based nanocomposite.

A major problem in metropolitan areas is searching for parking spaces. In this paper, we propose a novel method for parking space detection. Given input video captured by a camera, we can distinguish the empty spaces from the occupied spaces by using an 8-class Support Vector Machine (SVM) classifier with probabilistic outputs. Considering the inter-space correlation, the outputs of the SVM classifier are fused together using a Markov Random Field (MRF) framework. The result is much improved detection performance, even when there are significant occlusion and shadowing effects in the scene. Experimental results are given to show the robustness of the proposed approach.

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