Optimization of high-molecular-weight polycyclic aromatic hydrocarbons' degradation in a two-liquid-phase bioreactor
Bibliographic record
Abstract
A microbial consortium degrading the high-molecular-weight polycyclic aromatic hydrocarbons (HMW PAHs) pyrene, chrysene, benzo[a]pyrene and perylene in a two-liquid-phase reactor was studied. The highest PAH-degrading activity was observed with silicone oil as the water-immiscible phase; 2,2,4,4,6,8, 8-heptamethylnonane, paraffin oil, hexadecane and corn oil were much less, or not efficient in improving PAH degradation by the consortium. Addition of surfactants (Triton X-100, Witconol SN70, Brij 35 and rhamnolipids) or Inipol EAP22 did not promote PAH biodegradation. Rhamnolipids had an inhibitory effect. Addition of salicylate, benzoate, 1-hydroxy-2-naphtoic acid or catechol did not increase the PAH-degrading activity of the consortium, but the addition of low-molecular-weight (LMW) PAHs such as naphthalene and phenanthrene did. In these conditions, the degradation rates were 27 mg l-1 d-1 for pyrene, 8.9 mg l-1 d-1 for chrysene, 1.8 mg l-1 d-1 for benzo[a]pyrene and 0.37 mg l-1 d-1 for perylene. Micro-organisms from the interface were slightly more effective in degrading PAHs than those from the aqueous phase.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".