Response surface optimization of nitrite removal from aqueous solution by Fe3O4 stabilized zero-valent iron nanoparticles using a three-factor, three-level Box-Behnken design

Innovative optimization for enhancing Pb2+ biosorption from aqueous solutions using Bacillus subtilis

Introduction

Toxic heavy metal pollution has been considered a major ecosystem pollution source. Unceasing or rare performance of Pb2+ to the surrounding environment causes damage to the kidney, nervous, and liver systems. Microbial remediation has acquired prominence in recent decades due to its high efficiency, environment-friendliness, and cost-effectiveness.

Methods

The lead biosorption by Bacillus subtilis was optimized by two successive paradigms, namely, a definitive screening design (DSD) and an artificial neural network (ANN), to maximize the sorption process.

Results

Five physicochemical variables showed a significant influence (p < 0.05) on the Pb2+ biosorption with optimal levels of pH 6.1, temperature 30°C, glucose 1.5%, yeast extract 1.7%, and MgSO4.7H2O 0.2, resulting in a 96.12% removal rate. The Pb2+ biosorption mechanism using B. subtilis biomass was investigated by performing several analyses before and after Pb2+ biosorption. The maximum Pb2+ biosorption capacity of B. subtilis was 61.8 mg/g at a 0.3 g biosorbent dose, pH 6.0, temperature 30°C, and contact time 60 min. Langmuir's isotherm and pseudo-second-order model with R2 of 0.991 and 0.999 were suitable for the biosorption data, predicting a monolayer adsorption and chemisorption mechanism, respectively.

Discussion

The outcome of the present research seems to be a first attempt to apply intelligence paradigms in the optimization of low-cost Pb2+ biosorption using B. subtilis biomass, justifying their promising application for enhancing the removal efficiency of heavy metal ions using biosorbents from contaminated aqueous systems.

Optimizing Adsorption of 17α-Ethinylestradiol from Water by Magnetic MXene Using Response Surface Methodology and Adsorption Kinetics, Isotherm, and Thermodynamics Studies

Magnetic MXene composite Fe3O4@Ti3C2 was successfully prepared and employed as 17α-ethinylestradiol (EE2) adsorbent from water solution. The response surface methodology was employed to investigate the interactive effects of adsorption parameters (adsorption time, pH of the solution, initial concentration, and the adsorbent dose) and optimize these parameters for obtaining maximum adsorption efficiency of EE2. The significance of independent variables and their interactions were tested by the analysis of variance (ANOVA) and t-test statistics. Optimization of the process variables for maximum adsorption of EE2 by Fe3O4@Ti3C2 was performed using the quadratic model. The model predicted maximum adsorption of 97.08% under the optimum conditions of the independent variables (adsorption time 6.7 h, pH of the solution 6.4, initial EE2 concentration 0.98 mg L-1, and the adsorbent dose 88.9 mg L-1) was very close to the experimental value (95.34%). pH showed the highest level of significance with the percent contribution (63.86%) as compared to other factors. The interactive influences of pH and initial concentration on EE2 adsorption efficiency were significant (p < 0.05). The goodness of fit of the model was checked by the coefficient of determination (R2) between the experimental and predicted values of the response variable. The response surface methodology successfully reflects the impact of various factors and optimized the process variables for EE2 adsorption. The kinetic adsorption data for EE2 fitted well with a pseudo-second-order model, while the equilibrium data followed Langmuir isotherms. Thermodynamic analysis indicated that the adsorption was a spontaneous and endothermic process. Therefore, Fe3O4@Ti3C2 composite present the outstanding capacity to be employed in the remediation of EE2 contaminated wastewaters.