Preparation and Performance of a Modified Iraqi Red Kaolin Clay-Based Sorbent for Methylene Blue Removal in Acidic and Basic Solutions
Bibliographic record
Abstract
In recent years, the removal of dyes from wastewater via adsorption has attracted significant interest, particularly in the development of high-performance and low-cost sorbents that function effectively in both acidic and basic solutions.The goal of the present study was to synthesize a high-performance sorbent by modifying Iraqi red kaolin clay (IRK) with cetyltrimethylammonium bromide (CTAB), exchanging the interlayer of the clay to produce a hydrophobic material (CTAB-IRK) for the elimination of methylene blue (MB).Additionally, the adsorption of MB dye by CTAB-IRK was studied, focusing on the impact of agitation time, sorbent dosage, initial MB concentration, shaker speed, and pH on the adsorption mechanism.M MB removal efficiency remained stable (87.8-98.7%)over a pH range of 2 to 10.The surface modification with CTAB enhanced the hydrophobicity and adsorption capacity of IRK, facilitating MB removal.The optimum removal efficiency (100%) was achieved with an agitation time of 120 min, a dosage of 0.05 g/100 mL, pH 7, a shaker speed of 200 rpm, and an MB concentration of 50 mg/L.The Freundlich isotherm model (R² =0.926) accurately represented the isotherm data with maximum adsorption capacity of 833.33 mg/g, while the pseudo-second-order model was more applicable with kinetic data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".