Application of a novel nanocomposites carbon nanotubes functionalized with mesoporous silica-nitrenium ions (CNT-MS-N) in nitrate removal: Optimizations and nonlinear and linear regression analysis
2021
Altowayti, Wahid Ali Hamood | Othman, Norzila | Goh, Pei Sean | Alshalif, Abdullah Faisal | Al-Gheethi, Adel Ali | Algaifi, Hassan Amer
In recent years, the increase in the use of agricultural fertilizers in industrial development has produced poisonous inorganic ions such as nitrates in water and soil. Nitrates in drinking water which may come from nitrogen fertilizers are a potential health risk. Removal of nitrates from the environment is a big challenge. Following the series of investigation, the present study proposes the multiwall carbon nanotubes functionalized with mesoporous silica-nitrenium ions (CNT-MS-N) as a novel adsorbent for removing nitrate ions (NO3−) from aqueous solution. The ability of CNT-MS-N to remove nitrate ions from aqueous solutions was studied at different operating conditions. The maximum removal (98%) was obtained under the optimum conditions: adsorbent dosage of 70 mg and pH 7 and for initial concentration of 80 (ppm) at 30 °C for 5 h contact time. FTIR spectroscopy showed the contribution of amine, amide groups in removing nitrate and the FESEM-EDX results confirmed the adsorption of nitrate ions on the function groups of CNT-MS-N. In addition, nonlinear and linear isotherms and kinetics models were used to evaluate the equilibrium adsorption results. The coefficient of determination (R²) was used to determine the best-fit model expected by each approach. The results showed that the non-linear Langmuir isotherm model is a better way to achieve adsorption parameters illustrating the adsorption of nitrate ions onto CNT-MS-N with R² (0.9829). Likewise, it was found that the nonlinear Pseudo-second order rate model using the non-linear regression approach better predicted experimental results with R² (0.9921). The present investigation confirmed the nonlinear method as an appropriate technique to predict the optimum adsorption isotherm and kinetic data.
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