Enhancing xanthan biosynthesis in aerobic granular sludge for resource recovery: The role of organic loading rate, carbon-to-nitrogen ratio, and feeding strategy
Bibliographic record
Abstract
Recovering high-value biopolymers from wastewater offers a sustainable strategy for both pollution control and resource generation. This study is the first to examine xanthan biosynthesis and recovery in aerobic granular sludge (AGS) wastewater treatment systems, providing an alternative to conventional carbohydrate-rich fermentation, which is energy-intensive, feedstock-dependent, and costly. Valorizing xanthan from wastewater supports circular economy principles and integrated water–resource management. The effects of organic loading rate (OLR), carbon-to-nitrogen ratio (C/N), and feeding strategy on xanthan yield were assessed in nine experimental runs treating synthetic wastewater. AGS performance remained stable under all conditions, with excellent settling (5-min sludge volume index < 40 mL/g) and high COD removal (95 ± 5%). Ammonia-nitrogen and phosphorus removals averaged 73 ± 23%, and 72 ± 18%, respectively. Maximum xanthan yields occurred at OLR = 2.1 kg COD/m³∙d and C/N = 10 (41 ± 7 mg/g biomass, run 3) and at OLR = 2.1 kg COD/m³∙d and C/N = 20 (35 ± 10 mg/g biomass, run 1). Pearson correlation analysis showed a strong positive relationship between OLR and xanthan yield (r = 0.831, p = 0.006), and a moderate, non-significant negative correlation with C/N (r = –0.512, p = 0.158). Feeding strategy showed minimal influence (r = 0.042, p = 0.915). Fourier transform infrared and proton nuclear magnetic resonance spectroscopies confirmed structural similarity between recovered and commercial xanthan gum. These results demonstrate that AGS can be engineered to recover xanthan while maintaining high treatment performance, advancing sustainable wastewater management, biopolymer production, and circular economy objectives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".