Optimization of process parameters and medium components for carbon monoxide bioconversion with <i>Clostridium autoethanogenum</i>
Bibliographic record
Abstract
Abstract Clostridium autoethanogenum is a model microbe capable of converting carbon monoxide (CO) into bioethanol and useful biochemicals. Successful process optimization when using CO as the primary carbon source requires an understanding of the influence of process parameters and growth medium components on cell growth and end‐product formation. We conducted a full factorial analysis of the effects of CO total pressure, pH, yeast extract (YE), and cysteine concentrations on acetic acid, ethanol, and biomass production utilizing C. autoethanogenum with CO as the primary substrate. Maximum ethanol production of 0.71 g L–1 was obtained at a pH of 4.5, yeast extract concentration of 0.5 g L–1 , CO pressure of 1.8 atm, and cysteine concentration of 1.5 g L–1. There was a tenfold enhancement when the pH was lowered from 6 to 4.5 and the YE concentration was reduced to 0.5 g L–1. Response optimization using the reduced gradient algorithm confirmed these results. The information presented in this study could prove valuable for process engineering design by assisting with the selection of parameters that yield syngas blends specifically intended for increased ethanol 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".