Inhibition study of model compounds from sludge-derived hydrothermal liquefaction aqueous product on anaerobic digestion
Bibliographic record
Abstract
This study systematically evaluated the inhibitory effects of model compounds from sludge-derived hydrothermal liquefaction aqueous phase (HTLaq) on anaerobic digestion (AD) at both mesophilic and thermophilic temperatures using a total of 1008 anaerobic toxicity assays (ATA). Twenty representative compounds of suspected inhibitors, including nitrogen-containing heterocyclics like pyridines, pyrrolidinones, and pyrazines, as well as phenols and ketones, were tested at varying dosages (25, 50, 100, 200, 400, and 800 mg/L) to assess their impact on volatile fatty acids (VFA) generation and consumption, methane production, substrate utilization, and inhibitory compound degradation. Results demonstrated that thermophilic AD is generally more susceptible to inhibition than mesophilic AD, both in terms of acute and chronic toxicity. Compounds such as 3-methylcyclopentanone, indole, pyridine, 2-ethylpyridine, 3-aminopyridine, phenol, and 2-aminophenol were identified as the most toxic to both mesophilic and thermophilic AD systems. Indole and 2-aminophenol uniquely inhibited both methanogenesis and acetogenesis, while the others only affected methanogenesis. Despite their partial degradation, several compounds, including pyrazines, pyridines, ketones, and indole, exhibited recalcitrant behaviour under both mesophilic and thermophilic conditions, with the exception of δ-valerolactam. These findings contribute to a deeper understanding of HTLaq toxicity in AD and inform strategies for optimizing biogas production from sludge-derived HTLaq waste streams.
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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) | 0.000 | 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 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".