New trends in microbial lipid-based biorefinery for fermentative bioenergy production from lignocellulosic biomass
Bibliographic record
Abstract
Using oleaginous microbial lipid-based biorefinery from lignocellulosic biomass (LCB) to produce fermentative bioenergy (i.e., biodiesel) represents an innovative second-generation fuel production technology. These lipids are predominantly intracellular triglycerides that accumulate through the metabolism of sugars in fermentation following pretreatment and enzymatic hydrolysis of LCB. This review investigates the recent advances in the microbial lipid production from LCB, focusing on the factors influencing the lead microbial lipid producers, different pretreatment methods (i.e., physical, chemical, biological, and combined pretreatment), enzymatic hydrolysis approaches, novel bioprocessing strategies (i.e., microbes-specific and fermentation model specific), and engineering techniques of the oleaginous microbes (i.e., genetic and metabolic alterations). The study demonstrates that oleaginous yeasts can synthesize significantly higher quantities of lipids when incorporated into the system, known as separated hydrolysis and lipid production, following various combined pretreatment methods. Interestingly, CRISPR is found to be the most suitable way of engineering microbes genetically and metabolically for increased lipid synthesis. The study also explores economically viable strategies for fermentative lipid production, addressing associated challenges, and outlines future directions, including comprehensive techno-economic and life cycle assessments. This review offers invaluable insights into microbial lipid production from LCB, highlighting the potential for significant technological and environmental enhancements through ongoing research and development efforts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".