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 \nlipids for transesterification into biodiesel and/or various human health products such as \npolyunsaturated fatty acids. Unfortunately, due to their cultivation requirements, such as high \nenergy requirements and carbon dioxide (CO2) as a carbon source, large-scale biomass \nproduction generally remains uneconomical. To address this issue, the use of industrial flue gas \nas a low-cost source of CO2 and a biorefinery approach to help mend the economic burden of \nmicroalgae-based products with an emphasis on creating co-products from lipid-extracted \nbiomass (LEB) are assessed in this thesis. \nMicroalgae’s ability to sequester CO2 through photosynthesis is also advantageous in mitigating \nharmful industrial emissions. While these flue gases can have high concentrations of CO2, they \nalso can contain numerous contaminants (e.g., heavy metals, particulate matter) that discourage \nmicroalgae growth, and therefore their ability to fix CO2. But, the most significant issue can be \nhigh nitrous oxide (NOx) and sulphuric dioxide (SO2) concentrations within a flue gas that cause \nacidification when bubbled through liquid media. It is due to this acidification that finding the \nproductive microalgae species to grow in those systems can be problematic. \nTo address this, bioprospecting acid-tolerant microalgae from low pH environments in active and \nnon-active mining sites was explored and the acidophilic species present identified through DNA \nsequence analysis. Bioprospected algal species were then grown in acidic conditions similar to \nthose created by bubbling flue gas from a nickel smelter into water (pH 2.5). From this work, it \nwas 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). \nHowever, anabolic production of target biochemical molecules, such as lipids, is the key step in \nthe bio-product process. It is known that microalgae have the ability to accumulate bioactive \ncompounds when placed in stressed environments, such as high illumination and low nutrient \navailability, but little is known an about the impact of low pH and in particular the lipid \ncomposition of acidophilic microalgae. Research confirmed that the lipid compositions of \nbioprospected acid-tolerant microalgae was in the target range (13%). However, further work \nshowed that an increased total lipid content (up to 27%), with a desirable rise in the relative level \nof health beneficial higher polyunsaturated fatty acid, could be achieved by applying dark stress \nat the end of the exponential growth phase. It is, therefore, proposed that this approach could be \nan easy, low-cost method to enhance lipid productivity
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.
How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".