Bioconversion of wheat straw lignocellulosic sugars to ethanol by recombinant <i>Escherichia coli</i>
Bibliographic record
Abstract
Recombinant microorganisms are a promising alternative for production of bioethanol from sugars produced from lignocellulosic materials. In the present work, recombinant Escherichia coli FBR16 has been utilized to produce bioethanol from simulated glucose-xylose mixtures and wheat straw hydrolysates. Hydrolysates were produced by sequential treatment of dilute acid pretreatment at 180 °C for 7 min using 0.5% (v/v) H2SO4 and enzymatic saccharification using cellulase from Trichoderma reesei and β-glucosidase from Aspergillus niger. With increased concentration of glucose-xylose sugar mixtures, ethanol yield and volumetric ethanol productivity decreased. At 22 g/l, ethanol yield of 0.34 g/g and volumetric ethanol productivity of 0.36 g/l·h were obtained which reduced to only 0.19 g/g and 0.17 g/l·h, respectively, at 160 g/l glucose-xylose sugar mixture. Fermentation kinetic parameters were also estimated and it was found that values of parameters were highly dependent on initial sugar concentration. Furthermore, it was observed that E. coli FBR16 is capable of producing bioethanol from almost all lignocellulosic monomeric sugars, especially glucose and xylose. At 16.4 g/l lignocellulosic hydrolysate concentration, ethanol yield of 0.32 g/g and productivity of 0.24 g/l·h were obtained. In order to see the effect of lignocellulosic sugar concentration on ethanol production, hydrolysates were concentrated to 50 g/l from the original concentration of 16.4 g/l. E. coli FBR16 was able to ferment the increased sugar concentration as well; however decreased ethanol yield of 0.29 g/g and volumetric ethanol productivity of 0.17 g/l·h were obtained.
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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 | 0.000 | 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".