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Record W4396889561 · doi:10.1021/acssusresmgt.4c00019

Green Alginate Extraction from <i>Macrocystis pyrifera</i> for Bioplastic Applications: Physicochemical, Environmental Impact, and Chemical Hazard Analyses

2024· article· en· W4396889561 on OpenAlexafffund
Hayley A. Smith, Ludwig Paul B. Cabling, N. Leonard, Kristian L. Dubrawski, Heather L. Buckley

Bibliographic record

VenueACS Sustainable Resource Management · 2024
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaMitacsCanada Foundation for InnovationBritish Columbia Knowledge Development FundUniversity of Victoria
KeywordsBioplasticMacrocystis pyriferaExtraction (chemistry)Environmental hazardChemistryEnvironmental scienceEnvironmental chemistryPulp and paper industryWaste managementBiologyEngineeringBotanyKelpChromatographyEcology

Abstract

fetched live from OpenAlex

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.

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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.272
Teacher spread0.258 · 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

Citations14
Published2024
Admission routes2
Has abstractyes

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