Green Alginate Extraction from <i>Macrocystis pyrifera</i> for Bioplastic Applications: Physicochemical, Environmental Impact, and Chemical Hazard Analyses
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide To scale alginate production for widespread use, a focus on advancing sustainable extraction methods is needed. This study investigates greener extraction methods and their effect on alginate properties to advance sustainable methods suitable for bio-based plastic applications. Three alginate extraction protocols were selected by evaluating the hazard levels to human and environmental health of solvents and reagents used in 23 alginate extraction methods. The three methods were tested on samples of Macrocystis pyrifera algae and evaluated based on the efficiency of extraction, uronic acid composition (M/G ratio), molecular weight, and carbohydrate composition. Finally, the extraction methods were assessed using a partial life cycle assessment, assessing the water, chemical, and energy consumption and respective greenhouse gas emissions. The study found that the choice of extraction protocol significantly influenced the properties of the resulting alginate. Protocols 1 and 2, using operating conditions of high pH and temperature and short alkaline extraction times, produced alginates with a high M/G ratio (1.23 and 1.53, respectively) and comparable molecular weight to commercial alginate, but they had increased energy and water consumption and emissions (987 and 389 kg CO 2 -eq (CO 2 equivalents), respectively) compared to the other protocol evaluated. Protocol 3, using ambient conditions and an alkaline chelating sodium citrate treatment, resulted in alginates with a decreased M/G ratio (0.63), reduced greenhouse gas emissions (177 kg CO 2 -eq), and high yield (26.7%) but produced alginates with lower molecular weight (59.5 kDa). This research highlights the adaptability of extraction protocols for achieving the desired alginate properties and evaluates the safety and environmental implications of the selected methods.
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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 | 0.000 |
| 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 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".