Evaluating Powdered Activated Carbon for Adsorption of Nitrogenous Organics in Water Using HDPairFinder
Bibliographic record
Abstract
Amino-containing compounds are key precursors to highly toxic nitrogenous disinfection byproducts (DBPs) and odorous DBPs, posing a critical challenge for drinking water utilities. This study systematically evaluated the adsorption performance of six commercial powdered activated carbons (PACs) for removing soluble amino-containing compounds using amino acids as model compounds. Among them, PHF and AN PAC demonstrated superior removal efficiencies for six tested amino acids, ranging from 77 to 98% for PHF PAC and 83 to 96% for AN PAC. Subsequent analysis focused on PHF, AN, and HB PACs to investigate adsorption kinetics and effects of water parameters, including initial amino acid concentration, pH, and natural organic matter (NOM) on removal efficiencies. Optimal removal efficiencies were observed for PHF and AN PACs at pH levels between 6 and 8, while increased NOM levels significantly reduced amino acid adsorption. Finally, a hydrogen/deuterium isotopic labeling-based nontargeted analysis was applied to evaluate the removal of amino-containing compounds from source water (represented by Suwannee River standard reference materials). PHF exhibited the highest removal efficiency, achieving a 47% reduction in the total ion chromatogram (TIC) intensity of labeled amino-containing features, followed by AN at 21% and HB at 19%. The decrease in the TIC intensity and number of labeled amino-containing features aligned with the trends observed in adsorption, establishes a consistent ranking of PHF > AN > HB PAC. PAC can be seamlessly integrated into existing drinking water treatment processes and applied on an as-needed basis. Our results could provide valuable guidance for its effective application in water treatment plants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".