Data on removal kinetics of pharmaceutical compounds, artificial sweeteners, and perfluoroalkyl substances from water using a passive treatment system containing zero-valent iron and biochar
Bibliographic record
Abstract
The data presented in this paper relate to the research paper “Removal of pharmaceutical compounds, artificial sweeteners, and perfluoroalkyl substances from water using a passive treatment system containing zero-valent iron and biochar” [1]. Four columns packed with different ratios of reactive media, including silica sand (SS), zero-valent iron (ZVI), and biochar (BC), were evaluated for simultaneous removal of 14 emerging contaminants from water. The target emerging contaminants included eight pharmaceuticals (carbamazepine, caffeine, sulfamethoxazole, 3,4-methylenedioxyamphetamine, 3,4-methylenedioxymethamphetamine, ibuprofen, gemfibrozil, and naproxen), four artificial sweeteners (acesulfame-K, sucralose, saccharin, and cyclamate), and two perfluoroalkyl substances (perfluorooctanoic acid and perfluorooctane sulfonic acid). The samples for target contaminant analysis were collected from the influent, effluent, and profile (along the flow direction) ports of each column. The removal data (concentration vs. residence time) for each target contaminant were fitted to the first-order (exponential decay equation) or zero-order (linear equation) model using SigmaPlot. The removal rate, removal rate constant (kobs), mass normalized rate constant (kM), surface area normalized rate constant (kSA, specific reaction rate constant), and half-life (t0.5) of target contaminants in Columns ZVI, BC, and (ZVI + BC) were calculated and summarized in this dataset.
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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".