Effect of zero‐valent iron nanoparticles on the remediation of a clayish soil contaminated with γ‐hexachlorocyclohexane (lindane) in a bioelectrochemical slurry reactor
Bibliographic record
Abstract
Abstract In this research we evaluated the effect of adding zero‐valent iron nanoparticles (ZVI‐NP) to a complete mix bioelectrochemical slurry reactors (BESR) on the remediation of a clayish soil with a high content of organic matter, contaminated with lindane. Five BESR were loaded with a clayish polluted soil (100 mg lindane/kg ds ), known concentrations of ZVI‐NP, liquid medium, and sulphate‐reducing inoculum to give a 33% w/v soil concentration. A one‐factor experimental design was used, where the effect of nanoparticles concentration [NP] on lindane removal efficiency ( η lin ) and other response variables were evaluated. The [NP] levels were 0.0 (background control with electrical connection, BCWC), 2.5 (Exp1), 5.0 (Exp2), and 7.5 gNP/kg ds (Exp3). Maximum η lin (95%) was attained in Exp2 (5 gNP/kg ds ). Beyond this level the η lin slightly decreased (Exp3 with 85% η lin .) Approximately 40%‐57% of lindane was removed in the first 24 hours during a rapid kinetics phase. No metabolites of lindane degradation were detected after 30 days of operation in all the BESR. Energy production increased with [NP]; Exp3 generated 4.3 MJ/tonne ds at 30 days, whereas the other treatments produced energy between 1.6 MJ/tonne ds ‐1.2 MJ/tonne ds . Bioelectrical energy could partially offset the requirements of BESR mixing energy. Overall performance evaluation using an ad hoc multicriteria framework indicated that BESR followed the order Exp2 > Exp3 > Exp1 ~ BCWC > ABCWOC (abiotic control without electrical connection). There was a significant, positive effect of the combined BESR and ZVI‐NP technology for the remediation of heavy soils contaminated with lindane.
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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) | 0.000 | 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 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".