Adaptation of microalgae bioprospected from stressed environments in Northern Ontario for the production of lipids
Bibliographic record
Abstract
Photosynthetic green microalgae are a promising bio-feedstock that can be used to generate lipids for transesterification into biodiesel and/or various human health products such as polyunsaturated fatty acids. Unfortunately, due to their cultivation requirements, such as high energy requirements and carbon dioxide (CO2) as a carbon source, large-scale biomass production generally remains uneconomical. To address this issue, the use of industrial flue gas as a low-cost source of CO2 and a biorefinery approach to help mend the economic burden of microalgae-based products with an emphasis on creating co-products from lipid-extracted biomass (LEB) are assessed in this thesis. Microalgae’s ability to sequester CO2 through photosynthesis is also advantageous in mitigating harmful industrial emissions. While these flue gases can have high concentrations of CO2, they also can contain numerous contaminants (e.g., heavy metals, particulate matter) that discourage microalgae growth, and therefore their ability to fix CO2. But, the most significant issue can be high nitrous oxide (NOx) and sulphuric dioxide (SO2) concentrations within a flue gas that cause acidification when bubbled through liquid media. It is due to this acidification that finding the productive microalgae species to grow in those systems can be problematic. To address this, bioprospecting acid-tolerant microalgae from low pH environments in active and non-active mining sites was explored and the acidophilic species present identified through DNA sequence analysis. Bioprospected algal species were then grown in acidic conditions similar to those created by bubbling flue gas from a nickel smelter into water (pH 2.5). From this work, it was found that the acid-tolerant green microalgae in the genus Coccomyxa acclimated to the acidic conditions with suitable growth rates (0.136 day-1) and biomass production (25.71 mg L-1day-1). However, anabolic production of target biochemical molecules, such as lipids, is the key step in the bio-product process. It is known that microalgae have the ability to accumulate bioactive compounds when placed in stressed environments, such as high illumination and low nutrient availability, but little is known an about the impact of low pH and in particular the lipid composition of acidophilic microalgae. Research confirmed that the lipid compositions of bioprospected acid-tolerant microalgae was in the target range (13%). However, further work showed that an increased total lipid content (up to 27%), with a desirable rise in the relative level of health beneficial higher polyunsaturated fatty acid, could be achieved by applying dark stress at the end of the exponential growth phase. It is, therefore, proposed that this approach could be an easy, low-cost method to enhance lipid productivity
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".