Modeling the adsorption of emerging contaminants on activated carbon: classical and quantum QSAR approaches
Bibliographic record
Abstract
The adsorption of 115 emerging contaminants, mainly organic chemicals identified by the US Environmental Protection Agency's 2009 Contaminant Candidate List 3, was ranked using two published classical quantitative structure-activity relationship (QSAR) models and a newly developed quantum QSAR model. Approximately 75% of the investigated contaminants were predicted to be cost-effectively treatable, with an activated carbon usage rate below 10 mg/L. A limited experimental validation campaign was carried out by rapid small-scale column testing (RSSCT) using Lake Ontario water for eight selected compounds: 17β-estradiol, ibuprofen, diazinon, sulfamethoxazole, carbamazepine, 4-nonylphenol diethoxalyate, azithromycin and tylosin, with the activated carbon adsorption of the latter three having never been previously reported. The experimental results were consistent with the quantum chemistry model rankings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".