Microalgal cultivation with waste streams and metabolic constraints to triacylglycerides accumulation for biofuel production
Bibliographic record
Abstract
Abstract Global increases in the generation of waste streams, including liquid, gaseous, and solid waste, have been posing serious challenges for waste management as a result of their potential impacts on receiving environments and climate change. The conversion of waste streams into useful bioenergy, biofuels, and bioproducts through recycling and/or recovery has been presented as a promising alternative. Coupling the bioremediation of waste streams with microalgae‐based biofuel production, offers an alternative strategy to achieve waste‐to‐biofuel and bioenergy. A group of unicellular photosynthetic eukaryotes, microalgae require relatively simple nutrients and inorganic carbon sources to support their growth, while accumulating several biofuel precursors, such as starch or storage lipids. This review summarizes the current approaches to microalgal biomass production using waste streams, including waste‐water; waste or CO2‐enriched gas (flue gas and biogas); waste organics (i.e., crude glycerol); and waste heat, as well as the primary common operational challenges and corresponding mitigation strategies involved in cultivation approaches. Moreover, microalgal metabolic pathways supporting the biosynthesis of energy‐rich molecules such as triacylglycerides (TAG) and starch are discussed. Metabolic constraints and potential approaches for the enhancement of microalgal TAG accumulation are systematically and critically analyzed. © 2016 Society of Chemical Industry and John Wiley & Sons, Ltd
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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 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".