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Record W7033762765

Rhodosporidium Toruloides as Robust Yeasts for Advanced Biofuel Production Using Wood Hydrolysate

2023· other· en· W7033762765 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueYork University Digital Library (York University) · 2023
Typeother
Languageen
FieldSocial Sciences
TopicGerman Security and Defense Policies
Canadian institutionsYork University
FundersYork University
KeywordsHydrolysateBiofuelRaw materialBiomass (ecology)Renewable energyLignocellulosic biomassBioenergyPentoseFurfural
DOInot available

Abstract

fetched live from OpenAlex

In response to increasing global energy demand as well as greenhouse emissions from petroleum fuels, sustainable and renewable sources have been intensely researched to produce biofuel. For instance, microbial lipids have been recognized as a potential feedstock for biofuel production due to their similarities with vegetable oils in terms of fatty acids. Typically, microorganisms capable of accumulating more than 20% lipids are known as oleaginous microorganisms. These microorganisms can thrive on various renewable substrates and biochemically convert excess carbon into lipids. One such example of a renewable substrate is lignocellulosic biomass, which produces hydrolysate containing hexoses and pentoses sugars upon pre-treatment and saccharification, and thus could be employed as a potential substrate for microbial lipids. However, wood hydrolysate presents several challenges such as low consumption of pentose sugars, and the presence of microbial growth inhibitors such as furans, organic acids and phenols. In this sense, Rhodosporidium toruloides, an oleaginous yeast, could be employed to produce lipids due to its ability to accumulate 50-70% of lipids, consume C5 sugars, and tolerate inhibitors. Thus, the present thesis explores the ability of R. toruloides-1588 to thrive on undetoxified hydrolysate derived from forestry residues (hardwood and softwood sawdust) and accumulate lipids. Additionally, several strategies were employed to increase the lipid titer such as carbon and nitrogen ratios, fed-batch fermentation, and carbohydrate supplementation such as crude glycerol, which resulted in maximum lipid accumulation of 56.3% (w/w) along with more than 90% consumption of carbohydrates. A life cycle assessment has been also performed to identify the hotspots in terms of energy consumption, greenhouse gas emission, and waste produced during the lipid production process. Lastly, the strain was accessed for its ability to thrive on microbial growth inhibitor such as furfural and use it as an energy source. Based on the above findings, the current dissertation concludes that R. toruloides-1588 can thrive on undetoxified wood hydrolysate, accumulate lipids that can serve as a feedstock for biofuel production and provide aid in the further development of biorefinery industries.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.152
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.221
Teacher spread0.192 · 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