Combining chemical flocculation and bacterial co‐culture of <i>Cupriavidus taiwanensis</i> and <i>Ureibacillus thermosphaericus</i> to detoxify a hardwood hemicelluloses hydrolysate and enable acetone–butanol–ethanol fermentation leading to butanol
Bibliographic record
Abstract
Butanol, a fuel with better characteristics than ethanol, can be produced via acetone-butanol-ethanol (ABE) fermentation using lignocellulosic biomass as a carbon source. However, many inhibitors present in the hydrolysate limit the yield of the fermentation process. In this work, a detoxification technology combining flocculation and biodetoxification within a bacterial co-culture composed of Ureibacillus thermosphaericus and Cupriavidus taiwanensis is presented for the first time. Co-culture-based strategies to detoxify filtered and unfiltered hydrolysates have been investigated. The best results of detoxification were obtained for a two-step approach combining flocculation to biodetoxification. This sequential process led to a final phenolic compounds concentration of 1.4 g/L, a value close to the minimum inhibitory level observed for flocculated hydrolysate (1.1 g/L). The generated hydrolysate was then fermented with Clostridium acetobutylicum ATCC 824 for 120 h. A final butanol production of 8 g/L was obtained, although the detoxified hydrolysate was diluted to reach 0.3 g/L of phenolics to ensure noninhibitory conditions. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2753, 2019.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".