Crystalline iron oxide mineral (magnetite) accelerates methane production from petroleum hydrocarbon biodegradation
Bibliographic record
Abstract
Methane (CH 4 ) emissions are a factor in climate change; in addition, CH 4 production may affect reclamation of fluid fine tailings (FFT) in tailings ponds, and end-pit lakes (EPLs). In laboratory cultures, we investigated the effect of crystalline iron mineral (magnetite) on CH 4 production from the biodegradation of hydrocarbons added to FFT collected from methanogenically more and less active sites in a demonstration EPL. Magnetite enhanced CH 4 production from both sites, having a greater effect in more active FFT, where it increased the CH 4 production rate as much as 48% (from 6.67 μmol d −1 to 9.87 μmol d −1 ) compared to FFT without magnetite. Correspondingly, magnetite hastened biodegradation of hydrocarbons (monoaromatics, n -alkanes and iso -alkanes), with a pronounced effect on o- xylene, ethylbenzene, m/p- xylenes, n -octane, n -nonane, and 2-methyloctane, where biodegradation rates increased by 46, 117, 11, 45, 28 and 37%, respectively, compared to FFT without magnetite. Little Fe II was produced, suggesting that magnetite is not being used as an electron acceptor but rather functions as a conduit for electron transfer. Thus, magnetite may be a suitable amendment to enhance bioremediation of anaerobic environments contaminated with hydrocarbons. Importantly, our observations imply that magnetite may increase CH 4 emissions from terrestrial ecosystems, thus affecting carbon budget estimations. • First study to document the role of magnetite in hydrocarbon biodegradation. • Magnetite increased methanogenic biodegradation kinetics of hydrocarbons. • Higher biodegradation kinetics enhanced methane production from oil sands tailings. • The results suggest the role of magnetite in bioremediation of contaminated sites. • Magnetite may have implications in methane emission from terrestrial ecosystem.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".