Balancing the oxidation of endogenous organics and macronutrient recovery from human urine treated with fenton’s reagent: A targeted metabolomics study
Bibliographic record
Abstract
Human urine is a promising resource for circular fertiliser production, but its high concentrations of organic and inorganic compounds present both challenges and opportunities for effective treatment. This study evaluates Fenton oxidation for selectively degrading endogenous organic metabolites in acidified, unhydrolysed urine while preserving critical nutrients such as urea. Using targeted metabolomics, over 200 organic metabolites were identified in urine, with creatinine, citric acid, hippuric acid, and methylhistidine comprising half of the total organic metabolite load (ΣOMs = 3.23 g L⁻¹). Under optimised conditions (pH 4.0, 1:1 Fe²⁺: H₂O₂ molar ratio), 59% of ΣOMs were degraded in unconcentrated urine treated with 1 g H₂O₂ L⁻¹. Increasing the H 2 O 2 dose in unconcentrated urine, or treating concentrated urine obtained through evaporative water removal, resulted in higher ΣOMs degradation but also increased urea oxidation, highlighting a trade-off between efficient COD removal and nutrient recovery. COD removal was 38% at pH 4.0 and 27% at pH 6.0, suggesting that Fenton oxidation could be applied to H₂O₂ stabilised urine without strict pH adjustment. Real urine differed significantly from synthetic urine, requiring five times more Fe²⁺ catalyst for complete H₂O₂ activation, with peroxide consumption occurring within five minutes compared to two hours in synthetic urine. Organic compounds in urine scavenged Fe³⁺, forming iron-organic complexes that disrupted Fe²⁺ regeneration and contributed to iron precipitation at higher pH values. These findings demonstrate that Fenton oxidation can be optimised to achieve selective degradation of undesirable organics while preserving plant-essential nutrients in urine collected within resource-oriented sanitation systems.
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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) | 0.000 | 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 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".