Universiti Teknologi Malaysia

About: Universiti Teknologi Malaysia is a education organization based out in Johor Bahru, Malaysia. It is known for research contribution in the topics: Membrane & Adsorption. The organization has 21644 authors who have published 39500 publications receiving 520635 citations.

Synergistic effect in plasmonic Au/Ag alloy NPs co-coated TiO2 NWs toward visible-light enhanced CO2 photoreduction to fuels

Abstract: Plasmonic Au/Ag alloy NPs supported on TiO 2 nanowires (TiO 2 NWs) have been designed and synthesized through a facile hydrothermal and photo-deposition method. The samples were characterized by XRD, FE-SEM, TEM, N 2 -adsorption-desorption, XPS, Raman, UV–vis and PL spectroscopy. Bimetallic Au/Ag NPs were presented over the TiO 2 NWs as an alloy, thus exhibited strong absorption of visible light due to the localized surface plasmon resonance (LSPR) excitation. The synergistic effect in plasmonic Au/Ag alloy NPs for selective photocatalytic CO 2 reduction with H 2 to CO and hydrocarbons under visible light irradiation was investigated. The present design of plasmonic Au/Ag NPs co-decorated TiO 2 NWs leads to remarkably enhanced photoactivity of CO 2 reduction to CO. The CO evolution rate as a main product over the Au-Ag alloy NPs coated TiO 2 NWs was 1813 μmole-g-catal. −1 h −1 at selectivity 98%. This amount was approximately 1.72 time larger comparing to Au-NPs/TiO 2 NWs, 1.84 fold more than the Ag-NPs/TiO 2 NWs, 72.52 fold than the TiO 2 NWs and 201 fold more than the amount of CO produced over the bare TiO 2 -NPs. This great enhancement can be attributed to synergistic effects in Au/Ag-NPs, enhanced visible light absorption due to Au-Ag alloy formation and improved charge separation in LSPR-excited TiO 2 NWs. In addition, turnover productivity is introduced to investigate the effect of operating parameters on the performance of photocatalysts. The plasmonic reaction mechanism of Au-Ag NPs in conjunction with LSPR excitation and charge transport to understand the reaction pathway is described.

A review on removal of pharmaceuticals from water by adsorption

Abstract: Pharmaceuticals and personal care products are recognized as emerging pollutants in water resources. Various treatment options have been investigated for the removal of pharmaceuticals that include both conventional (e.g., biodegradation, adsorption, activated sludge) and advanced (e.g., membrane, microfiltration, ozonation) processes. This article reviews literature for adsorptive removal of pharmaceuticals from water sources. Adsorbents from various origins were reviewed for their capacity to remove pharmaceuticals from water. These adsorbents include carbonaceous materials, clay minerals, siliceous adsorbents, and polymeric materials. The adsorption capacity of adsorbents to adsorb pharmaceuticals from water is discussed in this study. The review discusses the mechanism for adsorption of pharmaceuticals onto adsorbents as well. Finally, effectiveness of processing parameters during adsorption processes is presented.

Modification of landslide susceptibility mapping using optimized PSO-ANN technique

Abstract: In the present study, we applied artificial neural network (ANN) optimized with particle swarm optimization (PSO) for the problem of landslide susceptibility mapping (LSM) prediction. Many studies have revealed that the ANN-based techniques are reliable methods for estimating the LSM. However, most ANN training models facing with major problems such as slow degree of learning system as well as being trapped in their local minima. Optimization algorithms (OA) such as PSO can improve performance results of ANN. Existing applications of PSO model to ANN training have not been used in area of landslide mapping, neither assess the optimal architecture of networks nor the influential factors affecting this problem. Hence, the present study focused on the application of a hybrid PSO-based ANN model (PSO-ANN) to the prediction of landslide susceptibility hazardous mapping. To prepare training and testing datasets for the ANN and PSO-ANN network models, large data collection (i.e., a database consists 168970 training datasets and 42243 testing datasets) were provided from an area of Layleh valley, located in Kermanshah, west of Iran. All the variables of PSO algorithm (e.g., in addition to the network parameter and network weights) were optimized to achieve the most reliable maps of landslide susceptibility. The input dataset includes elevation, slope aspect, slope degree, curvature, soil type, lithology, distance to road, distance to river, distance to fault, land use, stream power index (SPI) and topographic wetness index (TWI), where the output was taken landslide susceptibility value. The predicted results (e.g., from ANN, PSO-ANN) for both of datasets (e.g., training and testing) of the models were assessed based on two statistical indices namely, coefficient of determination (R2) and root-mean-squared error (RMSE). In this study, to evaluate the ability of all methods, color intensity rating (CER) based on the result of above indices was developed. Apart from CER, the total ranking system was also used to rank the obtained statistical indexes. As a result, both models presented good performance, however, according to the introduced ranking system, the PSO-ANN model could perform a better performance compared to ANN. According to R2 and RMSE values of (0.9717 and 0.1040) and (0.99131 and 0.0366) were found for training dataset and values of (0.9733 and 0.111) and (0.9899 and 0.0389) obtained for testing dataset, respectively, for the ANN and PSO-ANN approximation models, it can be resulted that PSO-ANN model showed higher reliability in estimating the LSM compared to the ANN.