Preformance of an Electrobiochemical Slurry Reactor for the Treatment of a Soil Contaminated with Lindane
Bibliographic record
Abstract
Lindane is a chlorinated pesticide known for its toxicity and persistence in the environment. Recently, it has been proposed that soil microbial fuel cell technology (SMFC) could be applied to enhance the removal of organic matter, phenol, and petroleum hydrocarbon in contaminated soil with simultaneous electricity output. Yet, there is no information on the application to remediation of soils polluted with pesticides. The purpose of this research was to evaluate the biodegradation of lindane with simultaneous electricity generation in an electrobiochemical slurry reactor (EBCR). The EBCR was inoculated with a sulfate reducing inoculum acclimated to lindane, it was further characterized, and batch operated for 30 day at room temperature. No external carbon source was supplemented in the experiment 1; the substrate was the soluble natural organic matter (NOM) of the soil. In the experiment 2 the EBCR was supplemented with a stock solution of sucrose: sodium acetate: lactate to give a final concentration of 2g COD/L in the reactor. Results from electrochemical impedance spectroscopy characterization in the EBCR (Experiment 1) showed that the equivalent circuit had a high anodic resistance R1=2064 Ω, cathodic resistance R3 = 192 Ω; and electrolyte/membrane resistance R2 = 7?, totaling a relatively high overall internal resistance Rint of 2263 Ω. During the batch operation, the EBCR showed a 30% lindane removal efficiency along with a maximum volumetric power of 165 mW m-3.This value compared favorably with results corresponding to sediments microbial fuel cells that are used to power weather monitoring systems. The organic matter removal was very high (72% as soluble COD, NOM) whereas the coulombic efficiency was low (5.4%). The latter, although, was higher than values reported for microbial fuel cells that degraded leachate-like effluents. In Experiment 2 of the EBCR both cell characteristics and performance significantly improved. The internal resistance as determined by polarization curve was 102 Ω when the two-electrode sets were connected in parallel. During the batch operation, the EBCR showed a 78% lindane removal and a maximum power volumetric of 634 mW m-3, the organic matter removal was 76% and coulombic efficiency was 15%. Finally, it can be concluded that our EBCR showed a high lindane removal capability and mixing of the slurry phase was associated to improvement of bioremediation and electricity performances of the device.
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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".