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/kgds), 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 (ηlin) 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/kgds (Exp3). Maximum ηlin (95%) was attained in Exp2 (5 gNP/kgds). Beyond this level the ηlin slightly decreased (Exp3 with 85% ηlin.) 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/tonneds at 30 days, whereas the other treatments produced energy between 1.6 MJ/tonneds‐1.2 MJ/tonneds. 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 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".