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Record W7131841499 · doi:10.48336/119

Beach-cast brown seaweed (Ascophyllum nodosum) for the extraction of bioactive compounds and production of hydrochar using subcritical water as a green solvent

2025· other· en· W7131841499 on OpenAlexfundno aff
Yu Zhang

Bibliographic record

VenueOpen MIND · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersGovernment of Canada
KeywordsBioproductsExtraction (chemistry)Hydrothermal circulationAscophyllumRaw materialBiomass (ecology)ExtractorFactorial experimentNutrient

Abstract

fetched live from OpenAlex

Beach-cast brown seaweeds are a rich source of potential bioproducts but are currently either left to degrade on coastlines or harvested and discarded in landfills, becoming an environmental burden and resulting in underutilized biomass. This thesis explores the valorization of beach-cast seaweed using hydrothermal processing. A common brown seaweed, Ascophyllum nodosum, abundant along the North Atlantic seashores, is used to investigate lower temperature hydrothermal processing, subcritical water (SCW) extraction as an effective process to produce bioactive compounds (i.e., alginate, fucoidan, phenolics, and carotenoids) in liquid product while simultaneously producing a valuable solid by-product (hydrochar). A comprehensive literature review identified knowledge gaps in bioactive compound extraction and challenges in scaling SCW processing, guiding the subsequent experimental investigations. The beach-cast A. nodosum was extracted using an Accelerated Solvent Extractor (ASE) under high-pressure SCW conditions (100–200 °C and 100 bar) to evaluate its valorization potential. Experimental design, kinetic studies, and rate modeling identified temperature as the most critical parameter impacting the ASE process. The key bioactive compounds (crude alginate, crude fucoidan, and antioxidant-rich crude extract) and nutrients (minerals and vitamins) exhibited comparable yields and qualities to those reported from fresh seaweeds, demonstrating the feasibility of using beach-cast seaweed as a sustainable feedstock. The scalability of SCW was studied using a 600 mL bench-scale pressurized reactor under low-pressure SCW conditions (100–240 °C, 1–33 bar). A screening factorial design (SFD) and central composite design (CCD) were used to study the effects of final temperature, hold time at that final temperature, water-to-biomass ratio, water pH, and agitation speed. Final temperature and hold time were identified as the most significant parameters impacting yields and qualities of bioactive compounds and hydrochar. A key challenge identified in the SCW scale-up is the variability in heating times across reactors, which complicates process optimization and control. To address this, the severity factor (log Ro) was evaluated as a single parameter (0.59–5.10) to integrate final temperature (100–240 °C), heating time (31–99 min), and hold time (0–24 min) for beach-cast A. nodosum valorization. The results demonstrated that the SCW process could recover crude alginate (15.75 dry wt% at log Rₒ = 1.99), crude fucoidan (39.94 dry wt% at log Rₒ = 2.39), antioxidant-rich crude extract (53.84–55.07 dry wt% at log Rₒ = 3.18–3.22), and hydrochar (29.53 dry wt% at log Rₒ = 3.79) at the maximum yields and/or qualities. Characterization of solid and liquid products revealed potential applications across biofuels, animal feeds, diet supplements, cosmetics, and natural antioxidant ingredients. This thesis establishes a holistic framework for the sustainable valorization of beach-cast seaweeds. By demonstrating the conversion of “waste” A. nodosum into multiple high-value products, the research contributes to regional sustainability, supports zero-waste concepts, and provides practical design tools (i.e., severity factor) for guiding future SCW scale-up and industrial application.

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 score0.003

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)0.0010.000

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 head0.056
GPT teacher head0.349
Teacher spread0.292 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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