Fine Chemical Engineering

Spectrophotometric Determination of Exchangeable (Mobile) Aluminum in Soil Using Arsenazo I

Svetlana Lokhanina — 2024-11-19

The possibility of using arsenazo I as a reagent for the spectrophotometric determination of the content of exchangeable (mobile) aluminum in soils of various types (clay, loamy, sandy, and sandy loam) is considered. An algorithm of analysis has been developed, taking into account the methodological guidelines MGU 4.1.2466, calibration dependencies have been established, and soil samples have been analyzed according to certified and proposed experimental methods. As a certified methodology, the methodology for performing measurements of GOST 26485 "Soils. Determination of exchangeable [mobile] aluminum by the SICAA method", in which one of the reagents is xylene orange has been used. To determine the effect of soil type on the results of aluminum determination, samples with different humus content and metabolic acidity were analyzed, and an artificial soil humification Al(III) reagent was used to study the effect of organic compounds on the analytical signal. It was determined that humus affects the measurement results only when its content is significant. It has been established that the use of arsenazo I for the spectrophotometric determination of exchangeable (mobile) aluminum in soil is possible along with the reagents provided by a certified measurement technique-xylene orange and chromazurol C. A number of advantages of using arsenazo I to determine the content of mobile aluminum have been established.

Evaluating the Potentials of Local Soil Samples and Alum in Removing Color from Slaughterhouse Wastewater by Coagulation and Filtration

Cornelius Tsamo — 2024-11-19

A large quantity of slaughterhouse wastewater (SWW), containing many pollutants, is generated during the slaughtering and cleaning process and needs to be treated before discharge to protect the environment. Slaughterhouse effluent treatment by filtration using soil/sand media, which is cheaper and more affordable than most advanced technologies that are not affordable in developing countries, is scarce in the literature. In this study, coagulation and filtration, carried out independently, were used to remove the color from SWW. Local alum (coagulant), gravel, sand, and soil were used for treating the wastewater. The physico-chemical parameters of raw SWW and treated SWW were determined. SWW concentration, reactor volume, and coagulant dose were used to evaluate the batch coagulation process, while soil type, soil/sand mixtures at different bed heights, SWW initial pH, SWW concentration, and backwashing of the column were used to evaluate the filtration process. The local alum used is mainly composed of SiO₂ (2.17%) and Al₂O₃ (67.12%). At the end of this study, 0.2 g of local alum was chosen as the optimum dosage, and it was concluded that local alum is as good as commercial alum in the treatment of SWW. Results also show that coagulation efficiency increases with an increase in the volume of SWW (maximum value 87.61%) and the concentration of SWW (maximum value 86.79%). The % of soil/sand mixture, bed height, pH, concentration, and backwashing have an effect on filtration efficiency. A bed height of 30 cm, having 25% soil and 75% sand, gave the best color removal (with a mean filtration efficiency of 74.22 ± 7.78%) in SWW. Filtration efficiency increases as pH increases, decreases as wastewater concentration increases, and increases with backwashing. Combined coagulation and filtration using local soils and sand produced improved SWW with properties close to the World Health Organization drinking water standards, reducing biological oxygen demand (BOD) by 98.26% and chemical oxygen demand (COD) by 98.89%.

Di-hydrogen Storage in Novel (C_nGe_n)₂ Nanostructures: Theoretical Study at Density Functional Theory (DFT) Level

Sellam Djamila — 2024-12-17

The Di-hydrogen solid-state is formed only at very low temperatures and pressures exceeding 1.5 million atmospheres. These draconian conditions are harmful to the economic and safe use of hydrogen, even if several studies refer to solid hydrogen which in reality is hydrogen adsorbed or absorbed on metallic surfaces or other. The objective of this research is to find new nanostructures with cages form that are able to confine a larger number of hydrogen molecules, potentially suggesting the geometry of the unit cell of solid hydrogen under standard conditions of (P, T). For this purpose, we use the density functional theory (DFT) method with the B3LYP and ωB97XD functional with the 6-31+G* basis. The MP2/6-311G++(d,p) level leads to the same results. The calculations of energies formation, infrared (IR) spectra, the shape of the molecular orbitals frontiers (MOF) and the Energy Gap will be done at theoretical level. These cage nanostructures are potential candidates for the Di-hydrogen storage. The 2H₂ complex adopts a planar geometry, whereas 3H₂ assumes a bi-pyramidal geometry with a square base.

Box-Behnken Design for MAFM Precision Surface Finishing of Al-6063/SiC/B₄C Composites: A Comparative Study with Nature-Inspired Algorithms

Gagandeep Chawla — 2024-10-25

Precision polishing is difficult for advanced materials like silicon carbide and boron carbide. Magnetic abrasive flow machining (MAFM) has become an effective method for cleaning, deburring, and polishing metal and high-tech engineering parts. By finishing hybrid Al/SiC/B₄C-metal matrix composites (MMCs), this research uses MAFM for experimental readings. The present work is innovative due to the aluminum workpiece fixture, hybrid composites, and response surface methodology (RSM) modeling. The neural simulation of the MAFM process and nature-inspired error reduction make it unique. Using six input and two output parameters, a generic framework is created. Box-Behnken design (BBD) of response surface methodology plans and executes 54 runs of experimentation. The hybrid artificial neural network (ANN) technique is used to compare the MAFM process systematically. ANN is used to model parameter input-output relations. To anticipate the created surface accurately, regression models must be precise. These hybrid particle swarm optimization (PSO)-genetic algorithm (GA)-simulated annealing (SA) algorithms optimize the MAFM process. Additionally, trained ANN models outperform the BBD model in prediction. For optimal error reduction, the neural network uses Bayesian regularization with 112 iterations. The ANN model regression graph shows a correlation between inputs and outputs. A scanning electron microscope (SEM) with 300-magnification examines the workpiece surface. According to SEM, MAFM provides fine surface textures, thus reducing abnormalities.

Fine Chemical Engineering

Spectrophotometric Determination of Exchangeable (Mobile) Aluminum in Soil Using Arsenazo I

Evaluating the Potentials of Local Soil Samples and Alum in Removing Color from Slaughterhouse Wastewater by Coagulation and Filtration

Di-hydrogen Storage in Novel (CnGen)2 Nanostructures: Theoretical Study at Density Functional Theory (DFT) Level

Box-Behnken Design for MAFM Precision Surface Finishing of Al-6063/SiC/B4C Composites: A Comparative Study with Nature-Inspired Algorithms

Di-hydrogen Storage in Novel (C_nGe_n)₂ Nanostructures: Theoretical Study at Density Functional Theory (DFT) Level

Box-Behnken Design for MAFM Precision Surface Finishing of Al-6063/SiC/B₄C Composites: A Comparative Study with Nature-Inspired Algorithms