Granular activated carbon enhances microbial activity in anaerobic reactors: Insights from metagenomics and metaproteomics
Bibliographic record
Abstract
Granular activated carbon (GAC) enhances anaerobic digestion (AD) primarily by promoting direct interspecies electron transfer (DIET). However, as most biomass in bioreactors is suspended rather than attached, GAC may also play additional roles in stimulating suspended biomass beyond DIET. In this study, two lab-scale up-flow anaerobic sludge blanket (UASB) reactors were operated for 150 days with propionate-rich synthetic wastewater, one of which was amended with GAC to investigate its broader effects on microbial activity and metabolic function. Results showed that GAC addition significantly improved chemical oxygen demand (COD) removal (92.1 ± 5.0%) and methane yield (70.3 ± 8.2%) compared to the non-GAC reactor (81.0 ± 2.1% and 55.4 ± 5.2%). Metagenomic analysis revealed a shift toward hydrogenotrophic methanogenesis, with an increased abundance of Methanobacterium sp. (31.4%). Metaproteomic profiling and functional gene prediction indicated elevated expression of proteins involved in methanogenesis (e.g., methyl-coenzyme M reductase), energy metabolism (e.g., ATP synthase), and cofactor biosynthesis (e.g., CobS and CobT enzymes). Additionally, batch tests using reactor effluents demonstrated that the GAC-amended system contained active substances capable of stimulating methane production, indicating the release of bioavailable metabolites. These findings suggest that GAC enhances microbial activity not only by facilitating DIET but also by stimulating the biosynthesis of key functional proteins and cofactors. This understanding supports the development of GAC-enhanced anaerobic systems for more stable and efficient reactors in full-scale applications.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".