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Record W3172280542

Adaptation of microalgae bioprospected from stressed environments in Northern Ontario for the production of lipids

2020· dissertation· en· W3172280542 on OpenAlexfundaboutno aff
Sabrina M. Desjardins

Bibliographic record

VenueLu Zone Ul (Laurentian University) · 2020
Typedissertation
Languageen
FieldEnergy
TopicAlgal biology and biofuel production
Canadian institutionsnot available
FundersMitacs
KeywordsAdaptation (eye)Production (economics)Environmental scienceEngineeringBiologyEconomics
DOInot available

Abstract

fetched live from OpenAlex

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

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.724

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.010
GPT teacher head0.181
Teacher spread0.171 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes2
Has abstractyes

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