Removal of organic contamination from wastewater using granular activated carbon modified—Polyethylene glycol: Characterization, kinetics and isotherm study
Bibliographic record
Abstract
To effectively remove Diazinon (DZ), Amoxicillin (AMX), and Crystal Violet (CV) from aquatic environments, a novel granular activated carbon (GAC) modified with Polyethylene glycol 600 (PEG) was created and manufactured. The chemical properties were investigated using a variety of characteristic analyses, including FT-IR, XRD, FESEM, and N2 adsorption/desorption. The effectiveness of GAC-PEG's adsorption for the removal of DZ, AMX, and CV was assessed under a variety of conditions, including a pH of 4-9 for the solution, 0.003-0.05 g doses of adsorbent, 50-400 ppm starting concentration, and a reaction time of 5-25 min. For DZ, AMX, and CV adsorption, the maximum adsorption capacity (Qmax) was 1163.933, 1163.100, and 1150.300 mg g-1, respectively. The Langmuir isotherm described all of the data from these adsorption experiments, and the pseudo-second-order well explains all-adsorption kinetics. Most contacts between molecules, electrostatic interactions, π-π interactions, hydrogen bonding, and entrapment in the modified CAG network were used to carry out the DZ, AMX, and CV adsorption on the GAC-PEG. The retrievability of the prepared adsorbent was successfully investigated in studies up to two cycles without loss of adsorption efficiency, and it was shown that it can be efficiently separated.
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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".