Effects of riboflavin and desferrioxamine B on Fe(II) oxidation by O2
Bibliographic record
Abstract
Flavins and siderophores secreted by various plants, fungi and bacteria under iron (Fe) deficient conditions play important roles in the biogeochemical cycling of Fe in the environment. Although the mechanisms of flavin and siderophore mediated Fe(III) reduction and dissolution under anoxic conditions have been widely studied, the influence of these compounds on Fe(II) oxidation under oxic conditions is still unclear. In this study, we investigated the kinetics of aqueous Fe(II) (17.8 μM) oxidation by O2 at pH 5‒7 in the presence of riboflavin (oxidized (RBF) and reduced (RBFH2)) and desferrioxamine B (DFOB) as representative flavins and siderophores, respectively. Results showed that the addition of RBF/RBFH2 or DFOB markedly accelerates the oxidation of aqueous Fe(II) by O2. For instance, at pH 6, the rate of Fe(II) oxidation was enhanced 20‒70 times when 10 μM RBFH2 was added. The mechanisms responsible for the accelerated Fe(II) oxidation are related to the redox reactivity and complexation ability of RBFH2, RBF and DFOB. While RBFH2 does not readily complex Fe(II)/Fe(III), it can activate O2 and generate reactive oxygen species, which then rapidly oxidize Fe(II). In contrast, both RBF and DFOB do not reduce O2 but react with Fe(II) to form RBF/DFOB-complexed Fe(II), which in turn accelerates Fe(II) oxidation. Furthermore, the lower standard reduction potential of the Fe(II)-DFOB complex, compared to the Fe(II)-RBF complex, correlates with a higher oxidation rate constant for the Fe(II)-DFOB complex. Our study reveals an overlooked catalytic role of flavins and siderophores that may contribute to Fe(II)/Fe(III) cycling at oxic-anoxic interfaces.
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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".