journal article·2024-04

Mesoporous composite aerogel derived from cellulosic waste for efficient adsorption and eco-friendly degradation of cationic dye

Sapna Chaudhary, D. T. K Dora, Suheel K Porwal

Journal of Porous Materials· Volume 31· Issue 2
Publisher page

Abstract

Water pollution caused by industrial effluents, containing organic dyes and metal ions, has significantly depleted freshwater sources. As a result, there is a pressing need to discover sustainable, cost-effective and eco-friendly approaches for wastewater treatment. In this context, a mesoporous composite aerogel was prepared using waste tissue paper and polyvinyl alcohol through a sol-gel and freeze-drying process. Characterization of the aerogel revealed impressive features, including high specific surface areas (301.9 m2/g), low density (0.0276 g/cm3) and high porosity (88.5%). To investigate the sorption capabilities, methylene blue (MB) was subjected to various operating conditions, such as initial concentration, temperature, and pH. The maximum sorption capacity of MB was found to be 898.52 mg/g, which aligned with the theoretical value obtained through a response surface methodology-based central composite design (CCD) and analysis of variance (ANOVA) using a quadratic model. The sorption kinetics of MB followed a pseudo-second-order model and the Langmuir adsorption isotherm provided an excellent fit to the experimental data, suggesting monolayer sorption. Thermodynamic analysis further confirmed that the sorption process was spontaneous and endothermic. Additionally, the composite aerogel demonstrated remarkable noise reduction properties, with a noise reduction coefficient of 0.71, highlighting its potential as an effective acoustic insulator.
Source: expertdata · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Aerogels and thermal insulation · Adsorption and biosorption for pollutant removal · Surface Modification and Superhydrophobicity

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

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