Bioremediation of toluene‐contaminated air using an external loop airlift bioreactor
Bibliographic record
Abstract
Abstract An external loop airlift bioreactor (ELAB) has been used to capture and degrade toluene from a contaminated air stream. Using a spinning sparger, the toluene could be transferred from small, uniform bubbles into the aqueous culture media with an overall mass transfer coefficient as high as 1.1 h−1. Due to the very volatile nature of toluene, Pseudomonas putida (ATCC 23973) was cultured and maintained on benzyl alcohol, the first intermediate compound in the metabolic degradation pathway for toluene. Consequently, before successful continuous operation of the ELAB with toluene‐contaminated air, Pseudomonas putida was acclimatized to toluene by using 30 min intermittent sparging of contaminated air into the bioreactor. Continuous sparging of toluene‐contaminated air could then be successfully carried out with 100% capture and biodegradation efficiency at a contaminated air concentration of 15 mg dm−3 and a loading rate of 35 mg dm−3 h−1. Higher concentrations and loading rates were only partially degraded. Although this capture matches only the low rates reported earlier using biofilters to remediate toluene, the ELAB operates using submerged culture and requires no packing which can plug during biofilter operation. In this study, Pseudomonas putida grew on toluene at a maximum specific growth rate of only 0.05 h−1. © 2003 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) | 0.001 | 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 | 0.001 |
| 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".