Exploring the impact of granular activated carbon on anaerobic digestion: Insights into microbial cross-feeding mechanism
Bibliographic record
Abstract
Microbial metabolite cross-feeding significantly influences microbial communities by modulating microbial interactions within bioreactors, yet our understanding of these processes remains limited. This study operated two laboratory-scale up-flow anaerobic sludge blankets (UASB) for 300 days to explore the effects of granular activated carbon (GAC) addition on cross-feeding. The incorporation of GAC increased methane production from 3.6 ± 0.5–4.5 ± 0.5 g CH 4 -chemical oxygen demand (COD)/d in the UASB when OLR was 2.5 g COD/L reactor/d (p < 0.05, n = 86). Effluents from both UASBs were collected and subsequently fractionated into two relative molecular mass (Mᵣ) groups—greater than 5000 and less than 5000—to investigate active microbial metabolites and cross-feeding mechanisms. This threshold was selected because it reliably distinguishes low-molecular-weight microbial metabolites from larger macromolecules. Notably, the filtrate with a M r below 5000 from the GAC-amended UASB significantly improved methane production by 18 % compared to the control. The > 5000 Mᵣ filtrate also showed an approximate 11 % increase in methane production. The effluent from the non-GAC UASB showed a modest enhancement of 2–6 % compared to the control. These results indicate that GAC-induced biosynthesis and cross-feeding of active metabolites significantly enhance anaerobic digestion efficiency and stability. This study highlights the critical role of microbial metabolites in modulating microbial interactions and boosting biomethane yield. • GAC addition increases methane production by 25 % in UASB reactors. • GAC enhances microbial cross-feeding through biosynthesis of key metabolites. • Small molecular weight metabolites drive improved anaerobic microbial activity. • Novel insights into optimizing biogas production via GAC-induced metabolite synthesis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".