Hydrothermal valorization of beach-cast brown seaweed Ascophyllum nodosum into bioactive compounds and hydrochar using severity factor as a design tool
Bibliographic record
Abstract
Beach-cast brown seaweed ( Ascophyllum nodosum ) is an abundant but underutilized biomass, often discarded as waste from coastal management. This study presents a hydrothermal processing (HTP) strategy under mild subcritical water conditions (100-240 °C and 1–33 bar) to valorize A. nodosum into liquid bioactive compounds and solid hydrochar. A key challenge in HTP scale-up is the variability in heating times across reactors, which complicates process optimization and control. To address this, the severity factor (log R o = 0.59–5.10) was evaluated as an integrated design parameter to combine final temperature (100-240 °C), heating time (31–99 min), and hold time (0–24 min) for waste brown seaweed valorization. This approach allows recovery of crude alginate (15.75 dry wt% at log R o = 1.99), crude fucoidan (39.94 dry wt% at log R o = 2.39), antioxidant-rich crude extract (53.84–55.07 dry wt% at log R o = 3.18–3.22), and hydrochar (29.53 dry wt% at log R o = 3.79) at the maximum yields and/or qualities. The crude extract obtained at log R o = 3.18–3.22 was enriched in saccharides, phenolics, and carotenoids, and concomitant antioxidant activities, demonstrating potential use as natural antioxidant ingredients. Hydrochar produced at log R o = 3.79 showed enhanced fuel properties (HHV = 21.9 MJ/kg, carbon content of 55.3 %, and energy yield of 40.6 %), suggesting its potential as a solid biofuel. This work demonstrates a scalable and sustainable valorization strategy for transforming coastal biomass waste into a broad spectrum of value-added products within a circular economy framework.
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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) | 0.001 | 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".