Utilizing Response Surface Method for Optimization of the Removing Cu(II) Ions from Aqueous Solutions Using New Magnetic Chitosan-Based Nanocomposites Containing N-Nicotinyl Phosphoric Triamides

Rezaei Jamalabadi, Shahin; Oroujzadeh, Nasrin

doi:10.30492/ijcce.2023.560003.5512

Utilizing Response Surface Method for Optimization of the Removing Cu(II) Ions from Aqueous Solutions Using New Magnetic Chitosan-Based Nanocomposites Containing N-Nicotinyl Phosphoric Triamides

Document Type : Research Article

Authors

Department of Chemical Technologies, Iranian Research Organization for Science and Technology (IROST), Tehran, I.R. IRAN

10.30492/ijcce.2023.560003.5512

Abstract

One of the newest methods to remove pollutants from water is the use of magnetic chitosan nanocomposites. Here for the first time, we used two phosphoramide compounds, N-Nicontinyl-N', N"-bis (piperidinyl) phosphoric triamide (P1) and N-Nicontinyl- N', N"-bis (4-methyl piperidinyl) phosphoric triamide (P2), in the structure of chitosan-based magnetic nanocomposites for the removal of Cu (II) ions from aqueous solution. The two chitosan/Fe₃O₄/ phosphoric triamide nanocomposites, NC1 and NC2, prepared using P1 and P2 respectively, were characterized using XRD, EDX, VSM, SEM, BET, and BJH methods. Removal of Cu (II) ions from polluted water was tested in different user conditions: pH (3-11), adsorbent dosages (5-25mg), and contact time of adsorbent with aqueous solutions (5-600 min). AAS (Atomic Absorption Spectroscopy) results showed that with increasing pH from 3 to 9, the amount of removal of Cu(II) ion increased, and with increasing pH from 9 to 11, the removal rate decreased. Heightening the contact time until 451 minutes also enhanced the removal efficiency up to 97.44%; After this time, the reduction of the removal amount was observed probably due to the desorption phenomena. Similarly, with the increasing amount of adsorbent (from 5 to 25 mg), the removal of Cu(II) ion increased too. Using Response Surface Modeling (RSM) the different conditions were transformed into RSM parameters and a second-order quadratic model was built to minimize the number of runs (36 runs) and also predict responses. A good correlation (with R² = 0.9237) was found between the experiment and the statistical model, for removing Cu(II) ions from the aqueous solution using these adsorbents.

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Main Subjects

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