Using microbial fuel cells for the remediation of hydrocarbon-contaminated aquifers
Bibliographic record
Abstract
Bio-electrochemical systems (BES) have been proposed as an emerging technology for enhancing groundwater remediation and are an interesting alternative for hydrocarbon-contaminated reducing aquifers where natural attenuation may be slow. BES take advantage of the ability of exoelectrogenic bacteria to transfer electrons from organic substrates to an extracellular electron acceptor, such as the anode of a microbial fuel cell (MFC). An electrical connection between the oxidizing and reducing compartments of the MFC allows reduction of oxygen at the cathode coupled to the oxidation of the reduced contaminant in the reducing compartment, accompanied by electricity production. Electricity production has been proposed as a proxy to monitor the progress of the remediation. The effects of additional electron donors, like ferrous iron, over the contaminant degradation efficiency and electricity production in BES have not been thoroughly studied. This research applied chemical, mineralogical, and microbiological analyses to study the degradation of naphthalene in a series of MFC experiments. The main objective was to test whether a reactor inoculated with native microorganisms from a local contaminated aquifer could successfully remediate naphthalene contamination in a reducing environment where iron was potentially an electron donor. An additional experiment was developed to address naphthalene sorption to electrodes and other reactor materials. The sorption experiment revealed that naphthalene dynamics in the MFC were significantly affected by sorption/desorption to reactor materials, so interpretation of MFC results required the consideration of naphthalene sorption and diffusion processes. The MFC experiments in this study did not find any advantage in providing an electrical connection between reducing and oxidizing zones of the bioreactors in terms of naphthalene degradation achieved in the system. However, the former did show the additional benefit of generating a small current. MFC experiments showed an increased electricity production when iron was available, however, the experiments with no iron achieved higher removal of naphthalene. The results from this study suggest that measuring electricity production is no substitute for direct measurement of contaminant biodegradation, since iron, sulfur, and naphthalene metabolites were involved in electricity production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".