Removal of Organoselenium from Aqueous Solution by Nanoscale Zerovalent Iron Supported on Granular Activated Carbon
Bibliographic record
Abstract
Nanoscale zerovalent iron particles (nZVI) immobilized on coconut shell-based granular activated carbon (GAC) were studied to remove organoselenium from wastewater. A chemical reduction technique that involves the application of sodium borohydride was adopted for the adsorbent preparation. The texture, morphology and chemical composition of the synthesized adsorbents were analyzed with a scanning electron microscope (SEM), nitrogen adsorption–desorption isotherms and X-ray diffraction (XRD). Batch experiment with various pHs and contact times were conducted to evaluate nZVI/GAC adsorption performance. The results showed that nZVI/GAC has a strong affinity to adsorb selenomethionine (SeMet) and selenocysteine (SeCys) from wastewaters. The maximum removal efficiency for the composite (nZVI/GAC) was 99.9% for SeCys and 78.2% for SeMet removal, which was significantly higher than that of nZVI (SeCy, 59.2%; SeMet, 10.8%). The adsorption kinetics were studied by pseudo-first-order (PFO) and pseudo-second-order (PSO) kinetic models. Amongst the two, PSO seemed to have a better fit (SeCy, R2 > 0.998; SeMet, R2 > 0.999). The adsorption process was investigated using Langmuir and Freundlich isotherm models. Electrostatic attraction played a significant role in the removal of organoselenium by nZVI/GAC adsorption. Overall, the results indicated that GAC-supported nZVI can be considered a promising and efficient technology for removing organoselenium from wastewater.
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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 | 0.001 | 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".