Aerobic biodegradation of sulfolane using <i>Archaea</i> and <i>Pseudomonas</i> strains
Bibliographic record
Abstract
Abstract BACKGROUND Sulfolane, an industrial solvent commonly used for sweetening natural gas, has recently emerged as a contaminant of concern in Alberta because of its widespread detection around many gas processing sites. In the work reported in this paper, the aerobic biodegradation of sulfolane by Pseudomonas strain or Archaea strain and mixed bacterial cultures was studied. The evaluation was furtherly conducted using Pseudomonas which had been acclimated to water contaminated with sulfolane. The impacts of co‐contaminant, initial sulfolane concentration and soil content on biodegradation of sulfolane using acclimated Pseudomonas were also investigated. RESULTS The results showed that Pseudomonas degraded sulfolane at a rate of 2.03 mg L−1 h−1 while Archaea strain degraded sulfolane at a rate of 0.04 mg L−1 h−1. Pseudomonas and Archaea inoculated with aquifer sediments containing indigenous microbes achieved a higher sulfolane degradation rate. Acclimation of Pseudomonas to sulfolane environment sufficiently mitigated the lag period before the onset of the biodegradation process. CONCLUSIONS Pseudomonas is a good candidate for aerobic biodegradation of sulfolane in groundwater. Aerobic biodegradation of sulfolane by Pseudomonas can be significantly enhanced through inoculation with sulfolane‐contaminated sediments or acclimatization to sulfolane environment. © 2022 Society of Chemical Industry
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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 | 0.001 | 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".