Evaluating Powdered Activated Carbon for Adsorption of Nitrogenous Organics in Water Using HDPairFinder
Classification
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".
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it