Engineering aerobic granular sludge for enhanced curdlan biosynthesis: the impact of organic loading rate, carbon-to‑nitrogen ratio, and feeding strategy
Bibliographic record
Abstract
This study reports, for the first time, the recovery of curdlan from aerobic granular sludge (AGS) wastewater systems. Nine bench-scale experiments were conducted to evaluate the effects of organic loading rate (OLR), carbon-to‑nitrogen ratio (C/N), and feeding strategy on curdlan biosynthesis during wastewater treatment. At OLR = 2.1 kg COD/m 3 ∙d with C/ N = 10 and 30 min feeding/30 min stationary phase, curdlan yield reached 74 ± 6 mg/g biomass, while at OLR = 2.1 kg COD/m 3 ∙d with C/ N = 30 and 10 min feeding/50 min stationary phase, curdlan yield was 69 ± 11 mg/g biomass. Univariate statistical analysis showed that there was a statistically significant association between curdlan production and OLR, and between curdlan production and feeding strategy at α = 0.05. Although synthetic wastewater was used for controlled bench-scale experiments, the complex composition of municipal wastewater may further affect microbial dynamics and curdlan biosynthesis. These findings highlight AGS as a promising platform for integrating curdlan recovery into wastewater biorefinery. • Curdlan was recovered from AGS systems: 69 ± 11 mg/g and 74 ± 6 mg/g peak yields. • OLR and feeding strategy significantly influenced curdlan production (α = 0.05). • Optimal conditions: OLR = 2.1 kg COD/m 3 ∙d, C/ N = 10, & 0.5 h feeding/0.5 h resting. • Curdlan recovered was identified by aniline blue staining, FT-IR, and NMR. • Curdlan recovery from AGS shows promise for wastewater biorefinery applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".