Hydrothermal Co-Liquefaction of Drift Macroalgal Biomass and Single Use Plastic Wastes: Optimizing Aqueous Phase Valorization for Enhanced Energy Recovery
Bibliographic record
Abstract
Hydrothermal co-liquefaction of drift macroalgal biomass and single use plastics presents a technically viable route for biofuel production, simultaneously addressing marine biomass overgrowth and plastic waste accumulation. Optimized process parameters (340 °C, 75 min, 1:1 feedstock ratio) yielded 41.2% bio crude. Catalytic enhancement of HTL with diatomaceous earth (DE) increased bio crude yield to 47.83%, while nanoporous zinc oxide (ZnO) produced a comparable yield of 48.1%. Furthermore, various aqueous phase valorization (APV) strategies, such as hydrothermal gasification (HTG) and photocatalytic reforming (PCR), were evaluated, with HTG yielding the highest hydrogen production (62.5%) and PCR producing 42.2% hydrogen. Additionally, aqueous phase (AP) recirculation significantly improved the bio crude yield, reaching 51.6% in ZnO-assisted HTL (6 mL/g) and 51.3% in DE-assisted HTL (10 mL/g), while DE HTL+AP achieved the highest carbon (64.37%) and energy recovery (77.00%), demonstrating the effectiveness of aqueous phase (AP) valorization in improving overall energy recovery and resource utilization efficiency. Each aqueous phase valorization (APV) process was assessed individually based on its quantifiable energy output. The findings identified ZnO HTL+HTG as the most efficient strategy, achieving an NER of 1.83, marking an 87.6% improvement over the base HTL process and demonstrating its superior potential for maximizing energy efficiency and bio crude production.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".