Biochar and Granular Activated Carbon Mitigate Polystyrene Nanoplastics Inhibition in Dark Biohydrogen Fermentation of Sludge
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it