Optimization of Congo red dye adsorption from wastewater by a modified commercial zeolite catalyst using response surface modeling approach
Bibliographic record
Abstract
In the present work, Zeolite A was modified by using hexadecyltrimethylammonium bromide (HDTMABr) for adsorption of the Congo red (CR) dye from synthetic aqueous solutions. The Modified Zeolite A (MZA) was characterized by XRD, SEM, and FTIR. The influence of solution pH (in the 4-12 range), ionic strength (0.1-1 M), contact time (180 min), initial CR concentration (20-60 mg/L), temperature (24-36 °C), and an adsorbent dose (1-3 g m/L) on the % dye removal and adsorbent capacity were studied. A combined effect of the initial CR concentration and temperature on the CR removal % by MZA was also studied by applying response surface methodology (RSM). Experimental values were in a good agreement with those predicated by a second-order quartic model. A maximum of 99.24% dye removal and adsorbent capacity of 21.11 mg/g was achieved under the following conditions: pH = 7, initial CR concentration = 60 mg/L, temperature = 24 °C, ionic strength = 0.1 M, adsorbent dose = 3 g/L and 90 min contact time. The equilibrium data were subjected to the Langmuir, Freundlich and Temkin isotherms, with the latter providing the best fit while kinetic adsorption studies were conducted by applying three models. The results indicated that the removal process was best described by the pseudo-second-order model. The present study demonstrates that modified MZA can be utilized for the highly efficient CR dye removal.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".