Recent Advances in Petroleum Microbiology
Bibliographic record
Abstract
Recent advances in molecular biology have extended our understanding of the metabolic processes related to microbial transformation of petroleum hydrocarbons. The physiological responses of microorganisms to the presence of hydrocarbons, including cell surface alterations and adaptive mechanisms for uptake and efflux of these substrates, have been characterized. New molecular techniques have enhanced our ability to investigate the dynamics of microbial communities in petroleum-impacted ecosystems. By establishing conditions which maximize rates and extents of microbial growth, hydrocarbon access, and transformation, highly accelerated and bioreactor-based petroleum waste degradation processes have been implemented. Biofilters capable of removing and biodegrading volatile petroleum contaminants in air streams with short substrate-microbe contact times (<60 s) are being used effectively. Microbes are being injected into partially spent petroleum reservoirs to enhance oil recovery. However, these microbial processes have not exhibited consistent and effective performance, primarily because of our inability to control conditions in the subsurface environment. Microbes may be exploited to break stable oilfield emulsions to produce pipeline quality oil. There is interest in replacing physical oil desulfurization processes with biodesulfurization methods through promotion of selective sulfur removal without degradation of associated carbon moieties. However, since microbes require an environment containing some water, a two-phase oil-water system must be established to optimize contact between the microbes and the hydrocarbon, and such an emulsion is not easily created with viscous crude oil. This challenge may be circumvented by application of the technology to more refined gasoline and diesel substrates, where aqueous-hydrocarbon emulsions are more easily generated. Molecular approaches are being used to broaden the substrate specificity and increase the rates and extents of desulfurization. Bacterial processes are being commercialized for removal of H(2)S and sulfoxides from petrochemical waste streams. Microbes also have potential for use in removal of nitrogen from crude oil leading to reduced nitric oxide emissions provided that technical problems similar to those experienced in biodesulfurization can be solved. Enzymes are being exploited to produce added-value products from petroleum substrates, and bacterial biosensors are being used to analyze petroleum-contaminated environments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.031 |
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".