Low Cost Activated Carbon for Removal of NOM and DBPs: Optimization and Comparison
Bibliographic record
Abstract
Higher concentrations of disinfection byproducts (DBPs) in small water systems have been a challenge. Adsorption by tailored activated carbon (AC), developed from waste materials of a pulp and paper company using optimization of chemical activation by nitric acid followed by physical activation and metal coating, was tested for the removal of natural organic matter from water using synthetic and natural water. AC was coated with aluminum and iron salts in a ratio of 0.25 to 10.0% of metal: AC (wt:wt%). The optimization of dosage, pH, and time was performed to achieve the highest adsorption capacity. The modified AC of 0.75% Fe-AC and 1.0% Al-AC showed 35–44% improvement in DOC removal from natural water. An enhancement of 40.7% in THMs removal and 77.1% in HAAs removal, compared to non-modified, AC were achieved. The pseudo-second order was the best fitted kinetic model for DOC removal, representing a physiochemical mechanism of adsorption.
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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) | 0.000 | 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 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".