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Research Article | Open Access

Adsorbent performance of nipa (nypafruticans) frond in methylene blue dye degradation: Response surface methodology optimization

Meriatna1( ) Zulmiardi2Lukman Hakim1 Faisal2 Suryati1Mizwa Widiarman1,2
Department of Chemical Engineering, Malikussaleh University, Lhokseumawe 24352, Indonesia
Department of Mechanical Engineering, Malikussaleh University, Lhokseumawe 24352, Indonesia
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Abstract

This study primarily focused on optimizing the performance of nipa (Nypafruticans)frond as an adsorbent for methylene blue (MB) dye, using the response surface methodology (RSM). The process of preparing nipafrond includes several key steps, such as washing, drying, and size reduction to 100 mesh. Subsequently, the adsorbent is activated using a 5%v/v HCl activator for 24 hours, and it is characterized by its functional groups using Fourier transform infrared spectroscopy (FT-IR). The composition of both the natural and activated nipa frond is examined through X-ray fluorescence (XRF), while the surface area of the samples is characterized using Brunauer-Emmett-Teller surface srea (BET) analysis. The adsorption characteristics are then tested as a function of two independent factors, including adsorbent mass (2.0, 3.0, and 4.0 g) and contact time (80–100 minutes), with an initial concentration of 50 mg/l MB. The statistical analysis, specifically analysis of variance (ANOVA), shows the substantial influence of these variables on the adsorption process. By applying the RSM model, the optimal conditions for MB adsorption are determined. These optimal conditions include nipa frond powder mass of 3.7071 g and a contact time of 83.1142 minutes, resulting in an optimum adsorption capacity of 2499 mg/g. The corresponding optimum adsorption efficiency is 99.7224%, with a desirability value of 0.974.

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AIMS Environmental Science
Pages 38-56

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Cite this article:
Meriatna, Zulmiardi, Hakim L, et al. Adsorbent performance of nipa (nypafruticans) frond in methylene blue dye degradation: Response surface methodology optimization. AIMS Environmental Science, 2024, 11(1): 38-56. https://doi.org/10.3934/environsci.2024003

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Received: 23 November 2023
Revised: 16 January 2024
Accepted: 26 January 2024
Published: 15 February 2024
©2024 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)