Arsenic Oxidation by Flavin-Derived Reactive Species under Oxic and Anoxic Conditions: Oxidant Formation and pH Dependence
Bibliographic record
Abstract
Flavins are ubiquitous redox-active compounds capable of producing reactive oxygen (O 2 •–, • OH, and H 2 O 2 ) and flavin radical species in natural environments, yet their roles in the redox transformations of environmental contaminants, such as arsenic (As), remain to be investigated. Here, we show that reduced flavins can be a source of effective oxidants for As(III) under both oxic and anoxic conditions. For instance, in the presence of 15 μM reduced riboflavin (RBFH 2 ), 22% of 30 μM As(III) is oxidized in aerated solution at pH 7.0. The co-oxidation of As(III) with RBFH 2 is pH-dependent, with a faster reaction rate under mildly acidic relative to alkaline conditions. Quencher tests with 2-propanol (for • OH) and catalase (for H 2 O 2 ) indicate that As(III) oxidation under oxic conditions is likely controlled by flavin-derived • OH at pH 5.2 and 7.0, and by H 2 O 2 at pH 9.0. Kinetic modeling further implies that flavin-derived reactive oxygen species are mainly responsible for As(III) oxidation under oxic conditions, whereas oxidation of As(III) under anoxic conditions at pH 9.0 is attributed to riboflavin radicals (RBFH • ) generated from co-existing oxidized and reduced riboflavin. The demonstrated ability of flavins to catalyze As(III) oxidation has potential implications for As redox cycling in the environment.
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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".