Remediation of bio-refinery wastewater containing organic and inorganic toxic pollutants by adsorption onto chitosan-based magnetic nanosorbent
Bibliographic record
Abstract
Abstract The novelty of the current study deals with the application of magnetic nanosorbent, chitosan-coated magnetic nanoparticles (cMNPs), to be utilized for the management of lignocellulosic bio-refinery wastewater (LBW) containing three heavy metals and 26 phenolic compounds. The magnetic property of the adsorbent, confirmed by elemental and vibrating sample magnetometer analysis (saturation magnetization of 26.96 emu/g), allows easy separation of the particles in the presence of an external magnetic field. At pH 6.0, with optimized adsorbent dosage of 2.0 g/L and 90 min contact time, maximum removal of phenol (46.2%), copper (42.2%), chromium (18.7%) and arsenic (2.44%) was observed. The extent of removal of phenolic compounds was in the order: polysubstituted > di-substituted > mono-substituted > cresol > phenol. Overall, the adsorption capacity (qe) of cMNPs varies among the different contaminants in the following manner: copper (1.03 mg/g), chromium (0.20 mg/g), arsenic (0.04 mg/g) and phenol (0.56 mg/g). Post-adsorption, retrieving the cMNPs using an external magnetic field followed by single-step desorption via acid–base treatment is attractive for implementation in industrial settings. Reusability of the adsorbent was studied by recycling the cMNPs for five consecutive rounds of adsorption followed by desorption, at the end of which, cMNPs retained 20% of their initial adsorption capacity.
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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".