Batch and continuous reactor studies for the adsorption of As(III) from wastewater using a hybrid biochar loaded with transition metal oxides: Kinetics and mass transfer analysis
Bibliographic record
Abstract
As(III) presence in low concentration (1-5 mg/L) in water presents a challenging problem in its removal. In the present study, biochar prepared by the pyrolysis of mustard cake and loaded with Fe-Mn binary oxides through hydrothermal technique was used for adsorptive removal of As(III) from water in batch and continuous modes. The synthesised biochar exhibited mesoporous structures in the range of 2-50 nm (based on BET analysis). The maximum adsorption capacity (95.7 mg/g) obtained using biochar loaded with both Fe-Mn oxides was found to be 1.4 times higher than that of pristine biochar. The adsorption equilibria was best described by Freundlich isotherm (based on R2 and χ2) suggesting that the As(III) adsorption was multilayered. The external mass transfer coefficients (βL = 10-5 cm2/s) were observed to be higher than the film (Df = 10-7 – 10-9 cm2/s) and intraparticle (Di = 10-9 cm2/s) diffusivities in batch mode. In column studies, Thomas model gave the best correlation coefficient (R2 > 0.95) and the adsorption was limited by external mass transfer. Kinetic rate constant decreased with increase in initial As(III) concentration and flow rate. The oxide loaded biochar exhibited reusability up to three times for As(III) 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".