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In situ Green Synthesis of Cellulose based Silver Nanocomposite and its Catalytic Dye Removal Potential Against Methylene Blue

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Abstract

Pollution of water by leaking industrial dyes has become a major problem for aquatic organisms and human-being. Polluted water treatment methods need efficient alternatives to conventional dye removing techniques. In this study, the catalytic activity of cellulose based silver nanocomposite against the pollutant methylene blue dye was searched. Cellulose based silver nanocomposite was synthesized by using in situ green synthesis method with Laurus nobilis extract. UV–VIS, FTIR, SEM, TEM, EDS, XRD, BET analyses were used to characterization of synthesized nanocomposite. Electron microscopy results confirmed that 25 nm diameter silver nanoparticles were supporting the 25 nm width cellulose nanofibers. Elemental composition of confirmed by UV–VIS, FTIR, XRD and EDS results which including silver and cellulose specific peaks. BET results showed that nanocomposite have 11.1 nm pore size with 199,8508 m2/g specific surface area. Catalytic activity of nanocomposite was performed against methylene blue in the presence of NaBH4. Catalytic activity was traced for up to 120 min and complete removal of methylene blue dye was estimated as 1 h. Moreover, the recycled nanocomposite was found reusable without activity loss for three times. These results offer an easy, eco-friendly and reusable dye removing material for wastewaters which were polluted by industrial dyes.

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by YK, MA, SH, and LY. The first draft of the manuscript was written by YK and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yigit Kucukcobanoglu.

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Kucukcobanoglu, Y., Ayisigi, M., Haseki, S. et al. In situ Green Synthesis of Cellulose based Silver Nanocomposite and its Catalytic Dye Removal Potential Against Methylene Blue. J Clust Sci 33, 1623–1633 (2022). https://doi.org/10.1007/s10876-021-02093-6

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  • DOI: https://doi.org/10.1007/s10876-021-02093-6

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