Optimization of medium‐chain‐length polyhydroxyalkanoate production by <i>Pseudomonas putida</i> KT2440 from co‐metabolism of glycerol and octanoate
Bibliographic record
Abstract
Abstract In this study, the co‐metabolism of glycerol and octanoate by Pseudomonas putida KT2440 significantly increased the production of medium‐chain‐length polyhydroxyalkanoate (mcl‐PHA). This was achieved through optimization of various parameters such as substrate concentration, nitrogen concentration, and other mixing conditions related to dissolved oxygen level. Experiments were performed in shake flasks and 5 L fermenter with findings assessed through single factor analysis and response surface methodology. The optimal concentration of substrates to improve mcl‐PHA production were determined to be 40 g glycerol/L, 12.23 g octanoate/L, and 1 g (NH 4 ) 2 SO 4 /L. Indirect regulation of dissolved oxygen was achieved by controlling the mixing conditions such as the initial medium volume (50 mL) (shake flask experiment), agitation speed (500 rpm), and aeration rate (10 L/minutes) (fermenter experiment). Optimized process parameters resulted in an mcl‐PHA titer of 8.47 g/L, which was significantly higher than that observed under un‐optimized conditions (3.95 g/L). The response surface method can be efficiently used to determine the optimal level of several factors related to mcl‐PHA production.
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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".