Enhancing anammox process with granular activated carbon: A study on Microbial Extracellular Secretions (MESs)
Bibliographic record
Abstract
Granular activated carbon (GAC), a porous carbon-based material, provides increased attachment space for functional microorganisms and enhances nitrogen removal by facilitating extracellular electron transfer in the anammox process. This study investigates the effects of GAC on the biosynthesis of microbial extracellular secretions (MESs) and explores the roles of these secretions in anammox activities. Four lab-scale reactors were operated: two downstream UASB reactors (D1 and D2) receiving effluents from the upstream UASB reactors (U1: no-GAC, U2: yes-GAC). Our results indicate that MESs were enhanced with the addition of GAC. The effluent from U2 exhibited a 59.62 % higher amino acid content than that from U1. These secretions contributed to an increase in the nitrogen loading rate (NLR) in the downstream reactors. Specifically, NLR in D1 increased from 130.5 to 142.7 g N/m3/day, and in D2, it escalated from 137.5 to 202.8 g N/m3/day, likely through acting as cross-feeding substrates or vital nutrients. D2 also showed increased anammox bacterial activity, enriched Ca. Brocadia population and hao gene abundance. Furthermore, this study revealed that D2 sludge has significantly higher extracellular polymeric substances (EPS) (48.71 mg/g VSS) and a larger average granule size (1.201 ± 0.119 mm) compared to D1 sludge. Overall, GAC-stimulated MESs may have contributed to the enhanced performance of the anammox process.
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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 | 0.001 |
| 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 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".