Direct development of microalgae-bacterial granular sludge system by seeding pre-made microalgae-dewatered sludge granules: Performance and mechanism analysis
Bibliographic record
Abstract
Microalgae-bacterial granular sludge (MBGS) process has great potential in achieving carbon neutrality and energy neutrality, but rapidly cultivating MBGS remains challenging. To address this challenge, this study proposes a new strategy to develop MBGS systems using pre-made granules from microalgae and dewatered sludge. The results indicate that using pre-made microalgae-dewatered sludge granules (M-DSG) as inoculants can directly develop MBGS system, with M-DSG maintaining a relatively stable granular structure, and ultimately achieving pollutant removal efficiencies of 94.0% for chemical oxygen demand (COD), 99.7% for ammonium nitrogen (NH 4 + -N), and 86.0% for total inorganic nitrogen (TIN). Extracellular polymeric substances (EPS) play a dominant role in maintaining the structure of granules, while filamentous bacteria/algae provide additional reinforcement. The adhesion of microalgae to granules possibly relies on polysaccharides in tightly bound extracellular polymeric substances (TB-EPS) and proteins in loosely bound extracellular polymeric substances (LB-EPS). Microbial community analysis reveals that the target algae ( Chlorella ) remain the primary algae, and heterotrophic nitrifying bacteria (HNB) and denitrifying bacteria are enriched.
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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 | 0.000 | 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".