Iron-impregnated biochar for the capture and degradation of stormwater-derived trace organic contaminants
Bibliographic record
Abstract
Bioretention cells are widely used to treat stormwater runoff and can capture trace organic contaminants (TrOCs) via sorption to soil. However, these systems are ineffective for very hydrophilic TrOCs, and can accumulate persistent TrOCs due to slow microbial degradation rates. Soil amendments such as biochar, a carbon-rich material produced through biomass pyrolysis, can be added to these systems to enhance capture of polar TrOCs, but do not address concerns around persistence. We hypothesized that redox-active iron-impregnated biochar amendments could generate reactive intermediates (e.g., hydroxyl radical and ferryl iron) via heterogeneous Fenton reactions and trigger abiotic transformations to enhance degradation of TrOCs in biochar-amended bioretention systems. Herein, we developed an “iron-impregnated biochar” amendment by co-pyrolyzing biochar with an iron nitrate solution. The resulting materials contained approximately 30% iron based on surface characterization with energy dispersive X-ray spectroscopy. The iron impregnation process reduced the surface area of the biochars by 75-93%, and consequently resulted in decreases in sorption for a suite of TrOCs (fipronil, caffeine, benzotriazole, carbamazepine, sulfamethoxazole, and 6PPDQ) compared to the unmodified biochars. We then assessed whether redox cycling between Fe(II) and Fe(III) at the iron-impregnated biochar surface could produce reactive oxygen species. We found that the iron-impregnated biochars produced both ∙OH (3.4–6.5 μmol mg -1 biochar) and Fe(IV), and that the iron-enhanced amendment could be reactivated 2-3 times through subsequent redox cycles. Overall, our findings suggest that iron-impregnated biochar could enhance the degradation of stormwater-derived TrOCs through generation of reactive oxygen species.
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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".