journal article·2019-03

Enhanced adsorption capacity of designed bentonite and alginate beads for the effective removal of methylene blue

Ravi, Lalit M. Pandey

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Abstract

Abstract Hazardous materials are very harmful to the environment and their removal is quite challenging, and methylene blue is one of them. In the present study, we have successfully designed a bentonite encapsulated alginate bead system for the effective removal of methylene blue, extensively used in chemical industries. Synthesized beads were characterized using Field Emission Scanning Electron Microscope, Energy Dispersive X-Ray Spectroscopy (EDX), Fourier Transform Infrared Spectroscopy (FTIR), Brunauer Emmett Teller Surface Analyser, and Dynamic Light Scattering. Bentonite/alginate beads system was optimized at physiological pH 7, and 25 °C and fitted well to pseudo-second-order kinetic model. The maximum adsorption capacity of system as calculated by Langmuir isotherm was found to be 2024 mg g−1 or 6.33 mmol g−1, which is many folds higher as compared to the reported clay-related and other adsorbents. Adsorption process was found be combination of surface adsorption and intra-particle diffusion (IPD) with dominancy during initial phase (30 min) and later phase, respectively. The designed beads enhanced the rates of both surface adsorption and IPD as compared to only bentonite powder as adsorbent. The thermodynamic studies confirmed the spontaneous and exothermic nature of adsorption process. FTIR, and EDX analyses of the removal data confirmed the possibility of both chemisorption and physiosorption. The designed beads imparted recyclability to bentonite powder and exhibited excellent removal efficiency (85%) after four consecutive adsorption-desorption cycles.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Adsorption and biosorption for pollutant removal · Analytical chemistry methods development · Pickering emulsions and particle stabilization

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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