Bioprocess strategies for enhancing lipid content in microalgae to improve biofuel production
Bibliographic record
Abstract
Microalgae have emerged as promising candidates for third- and fourth-generation biofuels due to their ability to efficiently fix CO₂, accumulate high lipid content, and adapt to extreme environments with minimal resource input. This review critically examines recent advances across the entire microalgae biofuel production chain. It highlights key species with high lipid productivity and strong environmental tolerance, including those capable of thriving in wastewater, saline, and acidic conditions. The review further synthesizes current strategies for enhancing lipid accumulation, encompassing both genetic interventions and environmental manipulations. Innovations in post-harvest processing—such as integrated fermentation, thermochemical conversion, and anaerobic digestion—have also demonstrated improvements in overall biofuel yield and energy recovery. Despite these advancements, challenges related to scalability, cost-effectiveness, and industrial CO₂ integration remain significant barriers to commercialization. This review underscores the importance of continued efforts in strain engineering, direct CO₂ utilization from industrial emissions, and life cycle sustainability assessments, while also highlighting emerging opportunities through systems biology, AI-driven modeling, smart biorefineries, and circular bioeconomy integration to enhance the overall viability and environmental performance of microalgae biofuels in meeting future global energy demands.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".