Adsorption by Granular Activated Carbon and Nano Zerovalent Iron from Wastewater: A Study on Removal of Selenomethionine and Selenocysteine
Bibliographic record
Abstract
Selenomethionine (SeMet) and selenocysteine (SeCys) are the most common forms of organic selenium, which is often found in the effluent of industrial wastewater. These organic selenium compounds are toxic, bioavailable and most likely to bioaccumulate in aquatic organisms. This study investigated the use of two adsorbent candidates (granular activated carbon (GAC) and nano zerovalent iron (nZVI)) as treatment technologies for SeMet and SeCys removal. Batch experiments were performed and inductively coupled plasma optical emission spectrometer (ICP-OES) was used for sample analysis. Experimental data showed GAC demonstrated a higher affinity towards the removal of SeMet and SeCys compared to nZVI. The removal efficiency of SeCys and SeMet by GAC was 96.1% and 86.7%, respectively. NZVI adsorption capacity for SeCys was 39.4% and SeMet < 1.1%. Irrespective of the adsorbent, SeMet is more refractory to be adsorbed compared to SeCys. Kinetics data of GAC and nZVI agreed well with the pseudo-second-order model (R2 > 0.990). The experimental data of SeCys was characterized by Langmuir model, indicating monolayer adsorption. The adsorption capacity of nZVI for SeCys increased significantly by about 35%, with a decrease in pH from 9.0 to 4.0, indicating that SeCy removal by nZVI is pH dependent. While electrostatic attraction is considered the driving mechanism for nZVI adsorption, GAC uptake capacity is controlled by weak van der Waal forces. The adsorption of binary adsorbates (SeMet and SeCys) exhibited an inhibitory effect due to the competitive interaction between contaminant molecules.
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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 | 0.000 | 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".