Recent advances in bioethanol production from lignocelluloses: a comprehensive review with a focus on enzyme engineering and designer biocatalysts
Bibliographic record
Abstract
Many countries have their biofuel policy programs in place as part of their overall strategy to achieve sustainable development. Among biofuels, bioethanol as a promising alternative to gasoline is of substantial interest. However, there is limited availability of a sufficient quantity of bioethanol to meet demands due to bottlenecks in the present technologies to convert non-edible feedstocks, including lignocelluloses. This review article presents and critically discusses the recent advances in the pretreatment of lignocellulosic biomass, with a focus on the use of green solvents, including ionic liquids and deep eutectic solvents, followed by enzymatic saccharification using auxiliary proteins for the efficient saccharification of pretreated biomass. Different techniques used in strain improvement strategies to develop hyper-producing deregulated lignocellulolytic strains are also compared and discussed. The advanced techniques employed for fermentation of mixed sugars contained in lignocellulosic hydrolysates for maximizing bioethanol production are summarized with an emphasis on pathway and transporters engineering for xylose assimilation. Further, the integration of different steps is suggested and discussed for efficient biomass utilization and improved ethanol yields and productivity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.004 | 0.002 |
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".