Adsorption of Selected Pharmaceuticals and an Endocrine Disrupting Compound by Granular Activated Carbon. 1. Adsorption Capacity and Kinetics
Bibliographic record
Abstract
The adsorption of two representative PhACs (naproxen and carbamazepine) and one EDC (nonylphenol) were evaluated on two granular activated carbons (GAC). The primary objective was to investigate preloading effects by natural organic matter (NOM) on adsorption capacity and kinetics under conditions and concentrations (i.e., ng/L) relevantfor drinking water treatment Isotherms demonstrated that all compounds were significantly negatively impacted by NOM fouling. Adsorption capacity reduction was most severe for the acidic naproxen, followed by the neutral carbamazepine and then the more hydrophobic nonylphenol. The GAC with the wider pore size distribution had considerably greater NOM loading, resulting in lower adsorption capacity. Different patterns forthe change in Freundlich K(F) and 1/n with time revealed different competitive mechanisms for the different compounds. Mass transport coefficients determined by short fixed-bed (SFB) tests with virgin and preloaded GAC demonstrated thatfilm diffusion primarily controls mass transfer on virgin and preloaded carbon. Naproxen suffered the greatest deteriorative effect on kinetic parameters due to preloading, followed by carbamazepine, and then nonylphenol. A type of surface NOM/biofilm, which appeared to add an additional masstransfer resistance layer and thus reduce film diffusion, was observed. In addition, electrostatic interactions between NOM/biofilm and the investigated compounds are proposed to contribute to the reduction of film diffusion. A companion paper building on this work describes treatability studies in pilot-scale GAC adsorbers and the effectiveness of a selected fixed-bed model.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| 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".