Superior removal of dyes by mesoporous MgO/g-C(3)N(4) fabricated through ultrasound method: Adsorption mechanism and process modeling - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Environmental Research Année : 2021

Superior removal of dyes by mesoporous MgO/g-C(3)N(4) fabricated through ultrasound method: Adsorption mechanism and process modeling

Résumé

The present research concerns the synthesis of a mesoporous composite characterized with high surface area and superior adsorption capacity in order to investigate its efficacity in removing hazardous and harmful dyes molecules from water. The synthesized mesoporous composite, MgO/g-C(3)N(4) (MGCN), was successfully prepared through the sonication method in a methanolic solution followed by an evaporation and a calcination process. The configuration, crystalline phase, surface properties, chemical bonding, and morphological study of the fabricated nanomaterials were investigated via XRD, BET, FESEM, HRTEM, XPS, and FTIR instrumentation. The obtained nanomaterials were used as sorbents of Congo Red (CR) and Basic Fuchsin (BF) dyes from aqueous solutions. Batch elimination experimental studies reveal that the elimination of CR and BF dyes from an aqueous solution onto the MGCN surface was pH-dependent. The highest removal of CR and BF pollutants occurs, respectively, at pH 5 and 7. The absorptive elimination of CR and BF dyes into the MGCN surface was well-fitted with a pseudo-second-order kinetics and Langmuir model. In this concern, the maximum nanocomposite elimination capacity for CR and BF was observed to be 1250 and 1791 mg g(-1), respectively. This investigation confirms that MGCN composite is an obvious and efficient adsorbent of CR, BF, and other organic dyes from wastewater.

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Chimie
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Dates et versions

hal-03514274 , version 1 (06-01-2022)

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A Modwi, L Khezami, M G Ghoniem, P Nguyen-Tri, O Baaloudj, et al.. Superior removal of dyes by mesoporous MgO/g-C(3)N(4) fabricated through ultrasound method: Adsorption mechanism and process modeling. Environmental Research, 2021, 205, pp.112543. ⟨10.1016/j.envres.2021.112543⟩. ⟨hal-03514274⟩
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