Study on Biodegradation of Tricyclic Terpanes in the Subsurface Reservoirs by GC–MS, FT-ICR MS, and Molecular Simulation
Bibliographic record
Abstract
Understanding petroleum microbial biodegradation behaviors under in situ conditions in subsurface oil reservoirs is helpful for oil pollution mitigation by microbial methods. In this study, a total of six heavily biodegraded oil samples from the Bongor Basin, Chad are collected to investigate the tricyclic terpane (TT) biodegradation behaviors in combination with GC–MS, FT-ICR MS, and quantum mechanical calculations. A series of 17-nor-tricyclic terpanes (NTTs) including C 19 and C 20 NTTs rarely identified in previous reports are observed in extremely biodegraded oils where hopanes and steranes are partly or completely removed. This suggests the occurrence of NTTs present in oil with biodegradation scale reaching PM 7–8 levels. The discovery of a low member of NTTs also indicates that the formation of NTTs is likely the removal of the methyl group from the corresponding TT compounds rather than the biological origin. From the molecular simulation results, the carbon atom in the C-10 position of C 23 TT is enriched with a positive charge indicating a strongly electrophilic reaction ability. It is evidenced by a much higher electrophilic index at the site suggesting that the C-10 methyl group is easiest for oxidation and removal rather than that at the end of the branch, which is also supported by a lower activation energy for its removal. Based on the FT-ICR MS data, it shows a linear relation between the relative contents of hopanoic and tricyclic terpanoic acids, which implies a likely similar biodegradation behavior between hopane and TTs. In combination with the biodegradation of hopanes and computational calculation results, we hypothesized that NTTs likely are formed by the oxidation and removal of the methyl group at C-10 of TTs. Although tricyclic terpanoic acids likely are the major intermediates, they are not essential and may not be yielded during the formation of NTTs. This study provides a multimethod for the TT biodegradation mechanism investigation.
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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) | 0.000 | 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".