Issue 4

Substitution of fossil fuel into alternative energy sources, such as biogas, needs to be applied to prevent various problems in the environment. Biogas purification can be an alternative technology to improve the quality of biogas, which increases the heat value by reducing CO2. Purification can be carried out by the adsorption method using solid adsorbents, for example natural zeolite and activated carbon. This study used 12 reactors from PVC, with 2 different diameters, 3 different mesh size (20,50,80) and was given both zeolite (A1-A3; B1-B3) and activated carbon (A4-A6; B4-B6). Biogas purification with the addition of zeolite has an optimal compound content in reactor A3 which has an increase in CH4 (63.63%) and decrease in CO2 composition (25.07%), which is better than other reactors. Meanwhile, the results of measurements of gas content on the addition of activated carbon showed optimal performance in reactor A6, which was increased to 76.86% for CH4 levels and CO2 levels dropped to 19.79%.

Water pollution caused by various toxic contaminants has become one of the world's most serious issues. Nanotechnology has received a lot of attention in recent decades, and various nanomaterials for water remediation have been developed. For this purpose, MWCNTs were directly synthesized using a chemical vapor deposition process, which was then purified and functionalized using an acid treatment. At various temperatures, the synthesis of MWCNTs by chemical vapor deposition (CVD) of acetylene is investigated. The wet impregnation method is used to prepare catalysts using Fe/Co as a catalyst in the presence of zirconium oxide and aluminum oxide as support materials. Zirconium oxide was found to be a good support material for the deposition of high-purity MWCNTs with high yield 88.9 %. The effect of growing time and temperature on carbon yield was investigated, and it was discovered that the amount of MWCNTs deposited increased as reaction time increased, with the optimum temperature for the reaction being 700 0C. The effect of temperature on the crystal size of synthesized MWCNTs was investigated using XRD, and it was found that the crystallite size declined as the temperature goes up. SEM and TEM were used to examine the structure and purity of synthesized MWCNTs at various temperatures. The MWCNTs that were synthesized have a web-like network structure, a homogeneous distribution on the catalyst surface, and a smooth surface. The prepared CNTs were purified by chemical oxidation, and the effect of acid treatment on the surface of the CNTs was investigated using XRD and FTIR. The functionalized CNTs were used for copper ion adsorption, achieving an adsorption potential of 238.09 mg/g and a high regeneration efficiency.

Pure and Zr doped ZnO thin films were prepared using SILAR technique. The influence of Zr doping on structural, morphological, optical and gas sensing properties of ZnO has been reported. X-ray diffraction study confirmed the formation of wurtzite structure of ZnO thin film (JCPDS 36-1451) fabricated by SILAR technique and the caluculated crystallites size of pure and doped ZnO were 39 and 36 nm respectively . SEM analysis of thin films has shown a completely different surface morphology. EDAX spetrum cnfirmed the presence of different compositional element in the fabriated thin films. Zr (3 wt%) doped ZnO thin film exhibited the best properties with a good transmittance and it has wide band gap of 3.26 eV. Photoluminescence emissions indicated increase in concentration of oxygen vacancies with introduction of dopant. NH3 vapour sensors were fabricated out of fabricated samples and it was observed that doped samples have significantly high sensing response, good selectivity, fast response and recovery time to ammonia vapoutr at room temperature.

Climate data composes of time series and space series with unknown. These unknown series contains complex co-variation relations of climate data. The extraction of these relations is essential for further revealing the complex representations between time series and space series in climate data. As an important application, through extracting these co-variation relations, we can further predict the change of climate to provide early warning for natural disasters, e.g., Greenhouse effect. Hence, it is a challenge to explore the relations between climate data. To address this, this work propose a deep neural network. Based on Brenier theorem, the loss function is derived. Since Brenier theorem rigorously proves that the data distribution in background space is consistent with the data distribution in the feature space with greatest probability, ensuring that the relations extracted from the latent space are as close to that of in background space as possible. Then, the parameters of time series consisting of eight variables are encoded by the first hidden-layer in the proposed model. The remaining two hidden-layers encode the latitude and longitude in spatial series, respectively. Experimental results show that the proposed method outperforms the state-of-the-art methods with respect to climate relations extracted. Hence, the proposed method is considered a good alternative in capturing relations between climate variables, as well as, between carbon dioxide (CO2) and surface temperature