Hydrothermal valorization of beach-cast brown seaweed Ascophyllum nodosum into bioactive compounds and hydrochar using severity factor as a design tool
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it