Pseudomonas putida Biofilm Facilitates Fine Solids, Water and Oil Separation from Oil Sands Tailings
Bibliographic record
Abstract
Aims: The generation of the tailings, poor settling slurry contaminated with emulsified bitumen, significantly increases the negative impact of oil sands operations on the environment and human health (contamination of surface and ground water with hydrocarbons and naphthenic acids, methane emission), as well as operation cost. Poor effectiveness of conventional tailings settling and clean-up technologies contributes to the daily increase of the quantity of tailings deposited in ponds covering now more than 130 km. There is an urgent need for development of novel tailings settling technologies. The aim of the present study is a comparative analysis of the impact of Pseudomonas putida planktonic and biofilm populations on oil, solids and water separation in tailings, and the investigation of the mechanisms involved in bioseparation. Methodology: Mature fine tailings (MFT) were exposed to Pseudomonas putida planktonic populations and biofilms at agitation followed by static conditions for settling. Oil-solids-water separation was determined by water and oil release from MFT in comparison with untreated tailings. Interaction of tailings with microbial populations was Research Article Kostenko et al.; JSRR, Article no. JSRR.2014.004 111 investigated with scanning electron microscopy (SEM), confocal scanning laser microscopy (CSLM) and energy-dispersive X-ray (EDAX) spectroscopy. Results: The exposure of mature fine tailings to microbial cultures, and especially to biofilms, significantly increase tailings densification, dewatering and bitumen release. The separation efficiency is associated with fine clay aggregation due to the interaction with the microbial cells, biofilm colonies and extracellular polymeric substances (EPS). Conclusion: The mechanism driving the observed biodensification is the aggregation of fine solids via flocculation by biofilm-produced EPS and bacterial cells. Microorganisms were also observed to destabilize emulsions and enhanced residual bitumen release from tailings.
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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".