Mechanoenzymology as a Novel Method for the Generation of Alginate Oligosaccharides from Alginate
Bibliographic record
Abstract
Alginate, a biopolymer primarily derived from brown seaweed, holds significant potential for various industrial applications, such as food production, pharmaceutical treatments, and agriculture.In its natural polymeric form, however, it is somewhat restricted in its practical use due to its low water solubility.Its depolymerization products, alginate oligosaccharides, on the other hand are much more versatile in these industrial applications because of their greater water solubility and variability in molecular weight.While there are many established methods of producing alginate oligosaccharides, each has drawbacks in aspects like waste production, water consumption, and affordability.This thesis explores a novel, sustainable approach to produce these oligomers through alginate depolymerization by alginate lyase using a mechanoenzymatic method (i.e.; ball-milling).By employing the enzyme under moist-solid conditions the study aims to reduce water waste and energy consumption while maintaining enzymatic efficiency.Key reaction parameters such as the liquid-to-solid ratio, milling time and frequency, incubation temperature, buffer pH, and enzyme loading were systematically optimized.The best mechanoenzymatic conditions yielded a reaction efficiency comparable to traditional aqueous methods, while reducing water consumption by ~680 times.The mechanoenzymatic method produced alginate oligosaccharides with narrow polydispersity and low molecular weights, comparable to those obtained in aqueous conditions.Overall, this research demonstrates the viability of mechanoenzymatic depolymerization as a greener alternative for alginate depolymerization, offering a new method of efficient alginate oligosaccharide production for further applications in medicine, agriculture, and food science.I would also like to thank everyone in the Auclair group for welcoming me and showing me the ropes until I got a hang of things, as well as for keeping me entertained in the lab when the days were particularly slow.In addition, I'd like to thank all of the other friends I made in the department for helping me to maintain a proper work-life balance.I'd specifically like to thank my closest friend Maddy for the (very) extended coffee breaks to decompress and debrief about our research woes or wins, as well as all of our adventures outside of grad school, I don't think there's anyone else I would've been able to relate to more.Lastly, I'd like to thank my family for all of their continued support throughout all of my studies leading up to and including this degree, and for sending me daily pictures of my dog to keep my spirits up.
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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) | 0.000 | 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 | 0.001 |
| 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 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".