A comparative study of solid and liquid non‐aqueous phases for the biodegradation of hexane in two‐phase partitioning bioreactors
Bibliographic record
Abstract
A comparative study of the performance of solid and liquid non-aqueous phases (NAPs) to enhance the mass transfer and biodegradation of hexane by Pseudomonas aeruginosa in two-phase partitioning bioreactors (TPPBs) was undertaken. A preliminary NAP screening was thus carried out among the most common solid and liquid NAPs used in pollutant biodegradation. The polymer Kraton G1657 (solid) and the liquid silicone oils SO20 and SO200 were selected from this screening based on their biocompatibility, resistance to microbial attack, non-volatility and high affinity for hexane (low partition coefficient: K = C(g)/C(NAP), where C(g) and C(NAP) represent the pollutant concentration in the gas phase and NAP, respectively). Despite the three NAPs exhibited a similar affinity for hexane (K approximately 0.0058), SO200 and SO20 showed a superior performance to Kraton G1657 in terms of hexane mass transfer and biodegradation enhancement. The enhanced performance of SO200 and SO20 could be explained by both the low interfacial area of this solid polymer (as a result of the large size of commercial beads) and by the interference of water on hexane transfer (observed in this work). When Kraton G1657 (20%) was tested in a TPPB inoculated with P. aeruginosa, steady state elimination capacities (ECs) of 5.6 +/- 0.6 g m(-3) h(-1) were achieved. These values were similar to those obtained in the absence of a NAP but lower compared to the ECs recorded in the presence of 20% of SO200 (10.6 +/- 0.9 g m(-3) h(-1)). Finally, this study showed that the enhancement in the transfer of hexane supported by SO200 was attenuated by limitations in microbial activity, as shown by the fact that the ECs in biotic systems were far lower than the maximum hexane transfer capacity recorded under abiotic conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".