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Aegle marmelos (AM), commonly known as bael fruit, is a medicinal plant that has been used for centuries in traditional Indian medicine. In this work, the predictive capabilities of response surface methodology (RSM) and artificial neural networks (ANN) using MATLAB in modeling brass corrosion inhibition by AM pulp extract in acidic environment (1M H2SO4) were comparatively analyzed. The RSM studies measured the corrosion rate of brass in a 1M H2SO4 solution under different conditions, i.e., time, temperature, and the concentration of AM extract. The maximum inhibition efficiency was achieved at a time period of 24.72 h, 600 ppm of AM pulp extract concentration, and a temperature of 25.8°C. The UV studies showed that AM pulp extract adsorbed on the surface of brass, forming a protective layer that prevented the metal from corroding. The SEM images showed that the surface of brass in the presence of AM pulp extract was smoother than that of brass in the absence of AM pulp extract. The AFM images showed that the roughness of brass in the presence of AM pulp extract was lower than that of brass in the absence of AM pulp extract. The FTIR spectra showed that the absorption bands of the metal oxide on the surface of brass were weakened in the presence of AM pulp extract, indicating that the formation of metal oxide was inhibited. The results of this study suggest that AM pulp extract is an effective and eco-friendly corrosion inhibitor for brass in acidic environment. The study also provides insights into the mechanism of corrosion inhibition by AM pulp extract. [ABSTRACT FROM AUTHOR]

Wastewater containing dye is usually treated by physical, chemical or biological treatment processes. Adsorption process is one of the effective and efficient techniques that have been successfully employed for colour removal from wastewater. The aim of the present study is to remove methylene mlue (MB) dye from its aqueous solution using Grevillea robusta silver nanoparticles (GR-Ag-NPs) as adsorbent. The variables covered were contact time, solution pH, adsorbent dosage, initial dye concentration and temperature. The adsorbents were characterised by using scanning electron microscopy (SEM), for its surface functional groups using Fourier Transform Infrared Spectroscopy (FTIR); furthermore response surface methodology (RSM) was adopted to study the correlations among the process variables affecting the process and to optimise the process to give higher % dye removal. [ABSTRACT FROM AUTHOR]

The present study uses a bio-waste, i.e., skin of Allium cepa, for green synthesis of zinc oxide nanoparticles. The nanoparticles A. cepa-ZnONP were tested for their photo catalytic degradation efficacy toward harmful dyes. Additionally, the anti-bacterial properties of A. cepa-ZnONP were evaluated against four organisms, namely Escherichia coli (EC), Pseudomonas aeruginosa (PA), Bacillus cereus (BC), and Staphylococcus aureus (SA). The synthesized A. cepa-ZnONP was characterized with scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM–EDX), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR) methods. The A. cepa-ZnONP demonstrated efficient degradation of Crystal Violet (CV), Rhodamine B (RB), and Methylene blue (MB) dyes, achieving maximum degradation percentages of 99.21%, 95.63%, and 92.34%, respectively, while operating under optimal process conditions. The rate constant values for CV, RB, and MB dyes at a temperature of 328 K were determined to be 0.1063, 0.0758, and 0.0447 min−1, respectively. The activation energy values for CV, RB, and MB dyes were determined to be 12.28, 18.437, and 50.623 kJ/mol, respectively. The successful regeneration of photo catalytic material A. cepa-ZnONP is a crucial milestone in guaranteeing their long-term effectiveness and practical usability. This work also emphasizes the possible uses of A. cepa-ZnONP in environmental remediation. This demonstrates the wide range of applications and importance of this substance in addressing current problems. [ABSTRACT FROM AUTHOR]

The goal of this research was to employ copper-doped zinc oxide nanoparticles (Cu/ZnONPs) as an adsorbent to remove the potentially toxic azo dye Congo red (CR). The Cu/ZnONPs were made using a chemical coprecipitation method, and their characteristics were examined using XRD, SEM, EDS, and FTIR methods. The response surface methodology (RSM) central composite design (CCD) is used to optimize the operational parameters' agitation time, adsorbent dosage, solution pH, and initial concentration of CR solution during the adsorption process. The agitation period of 29.48 min, the Cu/ZnONP dosage of 0.301 g/L, the solution pH of 6.96, and the CR initial concentration of 90 mg/L resulted in a maximum CR adsorption of 94.14% and a desirability of 0.976. The kinetic findings fit the pseudo-second-order kinetic equation, and the equilibrium data agreed with the Langmuir isotherm (maximum uptake capacity qmax = 250 mg/g). During the thermodynamic experiments, endothermic, spontaneous, and physical adsorptions were observed. [ABSTRACT FROM AUTHOR]

Considering the toxicity, the removal of heavy metals from the liquid effluent is of great significance. This article addresses the impact of the adsorbent bed height and flowrate on the adsorption performance and breakthrough curves in column studies. To explore the potential of Liagora viscida biosorbent, adsorption experiments were conducted in a packed column at various bed heights in the range of 2 to 6 cm and flow rates in the range of 10 to 20 ml/min. The maximum uptake of 53.90 mg/g has been achieved at the flow rate of 20 ml/min for an initial metal solution concentration of 20 ppm and pH and bed height of 6 and 2 cm, respectively. In the current study, the breakthrough (tb) and exhaust times (ts) correspond to 0.1 and 0.9, respectively. To predict the breakthrough characteristics, the experimental data were tested using Bohart‐Adam (B‐A), Bed Depth Service Time (BDST), Thomas, and Yoon‐Nelson (Y‐N) models. The experimental results confirmed that all three models except the Bohart‐Adams model were suitable for the experimental data with a good correlation coefficient. [ABSTRACT FROM AUTHOR]

Cadmium biosorption capacity of the biosorbent prepared from Gelidiella acerosa (brown algae) from synthetic cadmium solutions at optimum process conditions was investigated. The surface functional groups of the biosorbent responsible for biosorption were identified using FTIR analysis and the surface texture and porosity of the biosorbent was analyzed by SEM analysis. Response surface methodology (RSM) and artificial neural network (ANN) combined with central composite design (CCD) were used for modeling and optimization of biosorption and to study interaction effects of the three effective process variables such as solution pH, initial cadmium ion concentration and biosorbent dosage. Prediction capacities of RSM and ANN were compared and found that RSM showed better prediction performance than ANN. Kinetic data were well fitted to second order rate equation showing maximum biosorption capacity of 21.73 mg/g for 100 mg/l metal solution concentration. It was further confirmed by fitting the data to Elovich model. Biosorption mechanism was investigated using Intra-particle diffusion and Boyd models. The optimum cadmium removal efficiency of the biosorbent was found as 90.25%. [ABSTRACT FROM AUTHOR]