Toluene degradation by Cupriavidus metallidurans CH34 in nitrate-reducing conditions and in Bioelectrochemical Systems
Bibliographic record
Abstract
Bioelectrochemical remediation of hydrocarbons is a technology that exploits the ability of specific microorganisms to use as electron acceptor an electrode, thus potentially lowering the operational costs related to classical bioremediation. Several well-characterized hydrocarbonoclastic strains might be electroactive, thus their biodegradation performances in Bioelectrochemical Systems should be studied. Cupriavidus metallidurans CH34 is a model metal-resistant strain whose capacity to degrade benzene aerobically has recently been described. In this study, toluene degradation under anaerobic conditions and the exoelectrogenic capacity of Cupriavidus metallidurans CH34 were determined. Strain CH34 was grown anaerobically with toluene as sole carbon source in sealed serum bottles and then inoculated in a Microbial Electrolysis Cell (MEC) to assess its exoelectrogenic capacity. It was demonstrated for the first time that strain CH34 is able to degrade toluene under nitrate-reducing conditions (up to 45 mgtoluene/L were removed within 17 days, corresponding to 73% of toluene amended). Nitrate consumption and cellular growth were observed during toluene removal. In the MEC, toluene degradation was linked to current production, showing current peaks after every toluene addition (maximum current density 48 mA/m2). Coulombic efficiency of the toluene biodegradation process increased with time, from 11% (first batch cycle), up to 77% (last batch cycle).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".