Eco-Friendly Adsorptive Treatment of Methylene Blue in Aqueous Media with Agricultural Waste

This research focuses on assessing the potential of raw artichoke waste powder as an economical and sustainable biosorbent for the removal of methylene blue (MB) from aqueous solutions. The prepared artichoke waste powder was characterized using FT-IR and XRD, confirming the presence of functional groups (hydroxyl, carboxyl, carbonyl) and a largely amorphous structure favorable for adsorption. Batch experiments were conducted to evaluate the effects of solution pH, adsorbent dosage, initial dye concentration, and contact time on MB removal. Optimal adsorption was observed at alkaline pH (10–12) and an adsorbent dosage of 0.6–1 g/L, achieving nearly complete dye removal. Adsorption isotherms were fitted using Langmuir, Freundlich, and Sips models, with the Sips model showing the lowest sum of squared errors (SSE = 156.9), indicating predominantly monolayer chemisorption with slight surface heterogeneity. Kinetic studies showed that the pseudo-second-order model (SSE = 31.41) accurately described the adsorption process, highlighting the importance of chemical interactions between MB molecules and the functional groups on the adsorbent surface. These findings demonstrate that raw artichoke waste powder is an effective, eco-friendly, and economical adsorbent for cationic dye removal from water.

Authors

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2025-12-19
DOI
https://doi.org/10.1051/e3sconf/202568000093/pdf
Primary Topic
Adsorption and biosorption for pollutant removal
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Eco-Friendly Adsorptive Treatment of Methylene Blue in Aqueous Media with Agricultural Waste

Younes Abrouki, Soukaina Nehhal, Majda Ben Ali, Souad El Hajjaji et al.
Springer Link (Chiba Institute of Technology)
Adsorption and biosorption for pollutant removal
article

Eco-Friendly Adsorptive Treatment of Methylene Blue in Aqueous Media with Agricultural Waste

Younes Abrouki, Soukaina Nehhal, Majda Ben Ali, Souad El Hajjaji, Khalid Ofqir
article en

Abstract

This research focuses on assessing the potential of raw artichoke waste powder as an economical and sustainable biosorbent for the removal of methylene blue (MB) from aqueous solutions. The prepared artichoke waste powder was characterized using FT-IR and XRD, confirming the presence of functional groups (hydroxyl, carboxyl, carbonyl) and a largely amorphous structure favorable for adsorption. Batch experiments were conducted to evaluate the effects of solution pH, adsorbent dosage, initial dye concentration, and contact time on MB removal. Optimal adsorption was observed at alkaline pH (10–12) and an adsorbent dosage of 0.6–1 g/L, achieving nearly complete dye removal. Adsorption isotherms were fitted using Langmuir, Freundlich, and Sips models, with the Sips model showing the lowest sum of squared errors (SSE = 156.9), indicating predominantly monolayer chemisorption with slight surface heterogeneity. Kinetic studies showed that the pseudo-second-order model (SSE = 31.41) accurately described the adsorption process, highlighting the importance of chemical interactions between MB molecules and the functional groups on the adsorbent surface. These findings demonstrate that raw artichoke waste powder is an effective, eco-friendly, and economical adsorbent for cationic dye removal from water.

Springer Link (Chiba Institute of Technology)
Zero hunger
Openalex Percentile: Top 38%
Adsorption and biosorption for pollutant removal
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.