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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".