Microbial interactions with magnetite enhance methane production from hydrocarbon biodegradation
Bibliographic record
Abstract
Indigenous microbial communities in fine tailings (FT) biodegrade residual diluent hydrocarbons and support CH 4 emissions from oil sands tailings ponds and end-pit lakes. We investigated the effect of added crystalline Fe mineral magnetite on microbial metabolism of hydrocarbons in FT collected from methanogenically less and more active sites of an end-pit lake. Magnetite accelerated CH 4 production by enhancing the biodegradation of hydrocarbons, with a more prominent effect on complex/relatively recalcitrant aliphatics (C 8 -C 11 compounds) and monoaromatics. Interestingly, 86–92 % of total magnetite added in FT remained stable even after the metabolism of labile hydrocarbons (∼45 % of total diluent hydrocarbons). This may be due to magnetite enabling mineralogical direct interspecies electron transfer (mDIET) rather than iron reduction to enhance the methanogenic biodegradation of hydrocarbons. Enrichment of Coriobacteriaceae along with Desulfosporosinus, Syntrophus, Peptococcaceae, Smithella, Methanosaeta , and Methanoregula in magnetite-supplemented FT during hydrocarbon biodegradation suggested their potential role in developing mDIET. These results suggest that magnetite, when present, accelerates methanogenesis and potentially may increase rather than suppress CH 4 emissions from FT, and also suggest the potential use of magnetite to accelerate bioremediation of other hydrocarbon-contaminated anaerobic environments. • Magnetite enhanced methane production from oil sands tailings. • Magnetite increased kinetics of hydrocarbon biodegradation under methanogenic conditions. • A broad range of hydrocarbons were biodegraded in the presence of magnetite. • Microbial characterization implicated magnetite in mDIET for accelerated biodegradation. • Results also suggested magnetite's role in bioremediation of contaminated environments.
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".