Analytical characterization of N-halogenated peptides produced by disinfection: Formation, degradation, and occurrence in water
Bibliographic record
Abstract
Organic N -haloamines are water disinfection byproducts resulting from reactions between dissolved organic nitrogen and disinfectants . N -halogenated peptides, an important group of organic N -haloamines, have been overlooked over the past. In this review, we highlight peptides as the precursors for N -halogenated peptides during wastewater and drinking water disinfection. We discuss the analysis of N -halogenated peptides using methods based on titration , spectrophotometry , chromatography , and advanced liquid chromatography–mass spectrometry and the studies of the formation, degradation, and occurrence of N -halogenated peptides in water. While each method contributes to the evaluation of mechanisms and kinetics of formation and degradation of N -halogenated peptides in laboratory solution, only the development of highly sensitive and selective mass spectrometric methods enables the detection of trace levels of N -halogenated peptides in authentic disinfected water samples. Overall, reactions between peptides and disinfectants take place in milliseconds through an electrophilic substitution process. The N -halogenated peptides formed are relatively stable, leading to their sustainability in disinfected water. The recent detection of N -chlorinated peptides as disinfection byproducts using liquid chromatography–mass spectrometry in drinking water brings attention to these compounds. Further investigation of N -halogenated peptides on their occurrence and toxicological impacts are required to evaluate their health relevance.
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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) | 0.001 | 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 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".