Cyanobacteria: Photosynthetic cell factories for biofuel production
Bibliographic record
Abstract
Cyanobacteria are photoautotrophic prokaryotes that perform oxygenic photosynthesis through photo oxidation of water. They have been widely used as model organisms for studying photosynthesis. In recent decades, photosynthetic organisms, including cyanobacteria, have been chosen as potential hosts for biofuel production due to their remarkable ability to convert carbon dioxide into biofuel without the input of an external energy source. Biofuel, an excellent substitute for fossil fuels, have received a lot of attention due to their eco-friendly properties. Cyanobacteria have emerged as one of the leading potential candidates for biofuel production due to their superior growth rate over other photosynthetic organisms employed in biofuel production and the presence of a significant amount of lipids (over 50% of dry cell weight) in the cells. Furthermore, they have higher photosynthetic efficiency, especially in CO2-rich environments, making them more desirable. In addition, their inherent ability to uptake exogenous deoxyribonucleic acid (DNA) in conjunction with homologous recombination makes them ideal candidates for transformation into photosynthetic cell factories to produce biofuels. The genetic and metabolic modifications have successfully enabled biofuel production in cyanobacteria; however, major challenges such as energy-intensive downstream processing, low yield, slow growth, and cytotoxicity are impeding its scale-up. This review discusses the production of various types of biofuels in cyanobacteria, as well as the current state of global biofuel production. It also emphasizes the major challenges in biofuel production and strategies for overcoming them.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".