The Impact of UV/H<sub>2</sub>O<sub>2</sub> Advanced Oxidation on Molecular Size Distribution of Chromophoric Natural Organic Matter
Bibliographic record
Abstract
The impact of hydroxyl radical (*OH) on the molecular weight distribution of natural organic matter (NOM) was investigated. *OH was generated via the photolysis of hydrogen peroxide (H2O2) by ultraviolet (UV) radiation of 254 nm, also known as UV/ H2O2 advanced oxidation (AO). Additionally, the impact of combined membrane and UV/H2O2 treatment on the molecular weight distribution of NOM was studied. High performance size exclusion chromatography (HPSEC) was used to determine the apparent molecular weight (AMW) distribution of chromophoric NOM (CNOM). Prior to AO, 33% of the CNOM in the water had AMW greater than 1400 Da. Meanwhile, lower AMW CNOM made up smaller amounts of the CNOM, with CNOM of AMW less than 450 Da making up 5% of the total. Under the AO conditions typically applied in drinking water treatment applications, NOM was not mineralized but was partially oxidized resulting in significant reduction in aromaticity. *OH preferentially reacted with higher AMW CNOM and the fragmentation of high AMW CNOM led to the formation of smaller AMW CNOM. Ultrafiltration removed all CNOM greater than 1400 Da AMW and a large portion of other high AMW fractions. In the absence of high AMW CNOM, *OH reacted more readily with all AMW fractions leading to a reduction in concentration of most AMW fractions. Whereas *OH reacted nonspecifically with all AMW fractions, the reaction rate between *OH and CNOM was observed to be dependent on molecular size.
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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) | 0.000 | 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 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".