Enhancement of a two‐phase partitioning bioreactor system by modification of the microbial catalyst: Demonstration of concept
Bibliographic record
Abstract
Application of two-phase partitioning bioreactors (TPPB) to the degradation of phenol and xenobiotics has been limited by the fact that many organic compounds that would otherwise be desirable delivery solvents can be utilized by the microorganisms employed. The ability to metabolize the solvent itself could interfere with xenobiotic degradation, limiting remediation efficiency, and hence represents a microbial characteristic incompatible with process goals. To avoid the issue of bioavailability, previous TPPB applications have relied on complex and often expensive delivery solvents or suboptimal catalyst-solvent pairings. In an effort to enhance TPPB activity and applicability, a genetically engineered derivative of Pseudomonas putida ATCC 11172 mutated in its ability to utilize medium-chain-length alcohols was generated (AVP2) and applied as the catalyst within a TPPB system with decanol as the delivery solvent. Kinetic analysis verified that the genetic alteration had not negatively affected phenol degradation. The volumetric productivity of AVP2 (0.48 g/L x h(-1)) was equivalent to that seen for wild-type ATCC 11172 (0.51 g/L x h(-1)), but a comparison of initial cell concentrations and yields revealed an improved phenol-degrading efficiency for the mutant under process conditions. Yield coefficients, cell dry weight, and viable count determinations all confirmed the stability of the modified phenotype. This work illustrates the possibilities for TPPB process enhancement through a careful combination of genetic modification and solvent selection.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".