Crystal violet removal using algae-based activated carbon and its composites with bimetallic Fe0-Cu

Abstract The textile industry is considered a source of pollution because of the discharge of dye wastewater. The dye wastewater effluent has a significant impact on the aquatic environment. According to the World Bank, textile dyeing, and treatment contribute 17 to 20% of water pollution. This pape...

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Bibliographic Details
Published inMaterials research express Vol. 11; no. 6; pp. 065801 - 65822
Main Authors Abdel-Aziz, A B, Mohamed, Nora, El-taweel, Reem M, Husien, Sh, Hung, Yung-Tse, Said, Lobna A, Fahim, Irene Samy, Radwan, Ahmed G
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.06.2024
Subjects
Activated carbon
Adsorbents
Adsorption
Algae
Aquatic environment
Artificial neural networks
Bimetals
Cations
Composite materials
Copper
crystal violet dye
Crystals
Dyeing
Dyes
Efficiency
Pollution sources
Response surface methodology
Wastewater
Zeta potential
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Summary:Abstract The textile industry is considered a source of pollution because of the discharge of dye wastewater. The dye wastewater effluent has a significant impact on the aquatic environment. According to the World Bank, textile dyeing, and treatment contribute 17 to 20% of water pollution. This paper aims to prepare the bimetallic nano zero-valent iron-copper (Fe0-Cu), algae-activated carbon, and their composites (AC-Fe0-Cu), employed as adsorbents. In this paper, synthetic adsorbents are prepared and examined for the adsorption and removal of soluble cationic crystal violet (CV) dye. The influence of synthetic adsorbents on the adsorption and removal of soluble cationic crystal violet (CV) dye is investigated using UV-V spectroscopy at different pH (3 - 10), time intervals (15 – 180) minutes, and initial dye concentrations (50 - 500 ppm). Kinetic and isothermal models are used to fit the data of time and concentration experiments. DLS, zeta potential, FT-IR, XRD, and SEM characterize the prepared materials. Response surface methodology (RSM) is used to model the removal efficiency and then turned into a numerical optimization approach to determine the optimum conditions for improving dye removal efficiency. An artificial neural network (ANN) is also used to model the removal efficiency.
Bibliography:MRX-128116.R1
ISSN:2053-1591
2053-1591
DOI:10.1088/2053-1591/ad4e9c