Viability of Using Flue Gases as Carbon Source for Microalgae Cultivation
Bibliographic record
Abstract
Current rates of CO 2 emissions into the atmosphere are causing severe impacts on the planet. To reduce, or at least stabilize, CO 2 concentrations in the atmosphere technological solutions will be needed, such as enhancing biological C-fixation, thus capturing and storing CO 2 . Following this premise, the capture of carbon content in flue gas emissions (one important anthropogenic source of CO 2 ) would contribute to the decrease of this gas in the atmosphere. In this study we have tested the potential of microalgae to use CO 2 from flue gas as source of carbon to produce biomass. We evaluated the growth of four microalgal cultures during direct injection of flue gas. We used both freshwater and marine microalgal cultures. We observed that the four microalgae tested were able to grow using this source of carbon, and that although pH of the cultures decreased in the first hour of flue gas addition, it did not reach inhibitory growth levels. These results show the potential of utilizing this kind of technology to both reduce CO 2 emissions, and, at the same time, to produce green biomass with many biotechnological applications. Besides, the use of flue gas as source of carbon makes the whole cultivation process cheaper, contributing to the development of viable, sustainable culturing techniques to the production of microalgae biomass.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".