Impact of petroleum versus bio-based nano/microplastics on fermentative biohydrogen production from sludge
Bibliographic record
Abstract
Biohydrogen production via dark fermentation offers a promising route for waste-to-bioenergy. The impact of emerging contaminants like microplastics (MPs) and nanoplastics (NPs) in the waste on fermentative hydrogen production has not been thoroughly examined. Notably, a systematic comparison between petroleum-based and bio-based MPs/NPs in the hydrogen fermentation process has not yet been explored. We investigated the effects of petroleum-derived polyethylene MPs, polyvinyl chloride MPs, polystyrene NPs, and bio-based polyhydroxy butyrate and polylactic acid MPs, at low and high concentrations, on hydrogen production from primary sludge. Inhibition of hydrogen production ranged from 8.2% to 82.4%, with high concentrations of petro-based MPs/NPs causing more significant inhibition. Bio-based MPs exhibited lower inhibition compared to petro-based MPs/NPs. PsNPs at 0.3 mg/L exhibited the highest inhibition, accompanied by the highest increase (77.3%) in reactive oxygen species compared to the control. High levels of MPs/NPs increased extracellular polymeric substance production, indicating a protective response to toxicity. These findings highlight the importance of studying how emerging MPs/NPs pollutants in wastewater sludge impact fermentative hydrogen production and sludge properties. • Impact of micro/nanoplastics (MPs/NPs) on dark hydrogen fermentation is studied. • Effects of petroleum-based and bio-based MPs/NPs studied at varying concentrations. • Petro-MPs showed increased inhibition at high concentrations; bio-MPs did not. • Polystyrene NPs at a high level showed the most inhibitory effect. • Reactive oxygen species formation in fermentation is the key inhibitory mechanism.
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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".