Biochar and Granular Activated Carbon Mitigate Polystyrene Nanoplastics Inhibition in Dark Biohydrogen Fermentation of Sludge
Bibliographic record
Abstract
Nano/microplastics (NPs/MPs) are commonly found in sewage sludge, which leads to their unavoidable introduction into anaerobic bioreactors used for the fermentation or digestion of sludge in bioenergy recovery processes. This results in oxidative stress on the microbiome, ultimately hindering energy recovery. This study investigates the efficacy of biochar (BC) and granular activated carbon (GAC) in enhancing the dark hydrogen fermentation of primary sludge while mitigating the inhibitory effects of polystyrene nanoplastics (PsNPs). Comprehensive analyses included volatile fatty acid production, microbial community, toxicity, reactive oxygen species (ROS) generation, and sludge dewaterability. For the sludge without PsNPs, the highest enhancement (22.4% over the control) in biohydrogen production was obtained for 5 g/L BC. However, GAC performed better than BC by achieving the highest recovery (64.3%) of biohydrogen production by reducing ROS and toxicity from PsNPs. The abundance of Firmicutes in BC- and GAC-amended reactors was linked to higher biohydrogen yields. Also, BC and GAC significantly reduced the prolonged capillary suction times observed in the PsNPs-containing reactors, demonstrating their effectiveness in enhancing the sludge dewaterability. These findings demonstrate the potential of carbonaceous additives, such as BC and GAC, to deliver multiple benefits, including boosting biohydrogen production and mitigating the inhibitory effects of PsNPs.
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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".