Sorption of DOM and hydrophobic organic compounds onto sewage-based activated carbon
Bibliographic record
Abstract
Treatment of stormwater via sorption has the potential to remove both colloidal and dissolved pollutants. Previous research shows that activated carbon produced from sewage sludge is very efficient in sorbing hydrophobic organic compounds (HOCs), frequently detected in stormwater. The aim of this research was to determine whether the presence of dissolved organic matter (DOM) has a negative effect on the adsorption of HOCs onto sludge-based activated carbon (SBAC) in batch adsorption tests. Batch adsorption tests were used to investigate the influence of two types of DOM - soil organic matter and humic acid (HA) technical standard - on the sorption of HOCs onto SBAC, and whether preloading adsorbent and adsorbates with DOM affects HOC sorption. The results indicate that soil DOM and HAs do not have a significant negative effect on the adsorption of HOCs under tested experimental conditions, except for a highly hydrophobic compound. In addition, preloading SBAC or HOCs with DOM did not lead to lower adsorption of HOCs. Batch adsorption tests appear to be inefficient for investigating DOM effects on HOC adsorption, as saturating the carbon is difficult because of high SBAC adsorption capacity and low HOC solubility, so that limited competition occurs on the sorbent.
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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 | 0.000 | 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 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".