Improving high-solids anaerobic digestion of source-separated organics with nanobubble water supplementation: Significance of microbial community dynamics
Bibliographic record
Abstract
High-solids anaerobic digestion (HSAD) of source-separated organics (SSO) is a key strategy for sustainable waste management and energy recovery, but its intensification through advanced technologies is vital to enhance energy recovery and process stability. This study investigated the impact of nitrogen nanobubble water (N 2 -NBW) supplementation on HSAD of SSO with percolate recirculation. Two bench-scale HSAD reactors were operated under mesophilic conditions, one supplemented with N₂-NBW in the percolate tank, while the control operated without NBW addition. The N₂-NBW-amended reactor achieved ~43 % higher cumulative methane yield than the control along with improved methane content, and reduced hydrogen sulfide (H 2 S) levels. Although total volatile fatty acids (VFAs) levels were similar between systems by day 28, the N 2 -NBW reactor maintained a relatively lower VFA-to-alkalinity (VFA/Alkalinity) ratio (0.33 vs. 0.40) and higher alkalinity (22,540 vs. 18,824 mg CaCO 3 /L), indicating improved buffering capacity. Microbial community analysis revealed an increased abundance of Methanosarcina and vadinCA11 , indicating the development of a more resilient microbial community. These findings demonstrate that N 2 -NBW is a promising intensification strategy for enhancing HSAD efficiency and resilience. • N 2 -NBW was applied to high-solids anaerobic digestion with percolate recirculation. • Methane yield increased by ~43 %. • Improved biogas quality with reduced hydrogen sulfide and increased methane content. • Enhanced enrichment of syntrophic microbial communities.
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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 | 0.001 | 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".