Biodegradation of VOC mixtures of different hydrophobicities in two‐phase partitioning bioreactors containing tailored polymer mixtures
Bibliographic record
Abstract
Abstract BACKGROUND: There is a lack of systematic studies of biodegradation of mixtures of VOCs with different hydrophobicities in two‐phase partitioning bioreactors (TPPBs). The role of tailored mixtures of solid polymers on the biodegradation of MEK (low hydrophobicity), toluene (moderate hydrophobicity) and hexane (high hydrophobicity) was evaluated under steady state operation and transient loading (2‐ and 3.6‐fold 4 h step increase) in TPPBs. Two mixtures of polymer beads (A and B) were selected based on their 2 h partition coefficients for the target VOCs tested, biocompatibility and resistance to microbial attack. RESULTS: The addition of polymer mixture A (20%) into the bioreactor resulted in a severe microbial inhibition, likely due to the leaching of a polymer component, however, the presence of polymer mixture B (20%) supported removal efficiencies (REs) comparable with those recorded in the absence of polymers during steady state operation (hexane, toluene and MEK REs of 7%, 76% and 98%). However, the presence of polymer mixture B supported enhanced MEK REs during the 2‐fold step increase, and increased toluene removal during the 3.6‐fold step increase compared with the system without polymers. CONCLUSIONS: TPPBs with tailored polymer mixtures can improve process performance during VOC transient loadings, however, the interactions between the target VOCs and the solid polymers used should be a key selection criterion in order to avoid microbial inhibition during TPPB operation. Copyright © 2010 Society of Chemical Industry
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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 teacher head, 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".