Effective Removal of Fe(III) Ions from Water Sample Using Activated Drinking Water Treatment Sludge: Isotherms and Kinetic Studies
Bibliographic record
Abstract
In this study, drinking water treatment sludge was activated and used as an efficient, cheap and cost effective sorbent in the removal of Fe(III) ion from water samples.The prepared material was characterized by Fourier transfer infrared spectroscopy (FT-IR), X-ray powder diffraction (XRD), scanning electronic microscopy (SEM), surface analysis (BET method) and X-ray fluorescence (XRF) analysis.The effects of various parameters such as the solution pH, adsorption time, adsorbent dosage, and initial metal ion concentration upon adsorption were investigated.Equilibrium isotherm studies were carried out with different initial concentrations of Fe(III) , and two models (Langmuir and Freundlich isotherms) were utilized to analyze the equilibrium adsorption data.The results revealed that the adsorption process obeyed the Langmuir model, with the maximum monolayer capacity (qmax) and the Langmuir constant (KL) calculated as 54.3 mg g-1 and 1.19 L mg-1, respectively.Kinetic studies indicated that the adsorption process followed a pseudo-second-order model.The results showed that activated sludge is an efficient and selective material for adsorption of Fe(III) .
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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) | 0.000 | 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".