Microalgal cultivation with waste streams and metabolic constraints to triacylglycerides accumulation for biofuel production
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 CO 2 ‐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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it