Trace concentrations of graphene oxide and magnetic graphene oxide rescue anaerobic municipal sludge digesters under stress
Bibliographic record
Abstract
• Long-term assessment of GO/MGO nanosheets on semi-continuous flow sludge AD tested. • GO/MGO nanosheets improved digester stability and microbial syntropy through DIET. • MGO improvement on AD stability was concentration dependent, linked to soluble iron. • At high organic loading, GO/MGO amended AD survived, but control (no GO/MGO) failed. • Semi-continuous AD results aligned with BMP assays validating BMP as screening tool. This study evaluated long-term performance of graphene oxide (GO) and magnetic GO (MGO) nanosheets in semi-continuous-flow anaerobic digestion (AD) of municipal sludge over 230 days. At organic loading rates (OLRs) of 2.6 and 3.4 g chemical oxygen demand (COD) fed /L/day, 20 and 200 mg/L of GO and MGO did not affect AD performance. However, at an OLR of 5.2 g COD fed /L/day, where control digesters failed, 20 and 200 mg/L of GO and MGO sustained biogas yields at 190 mL/g COD fed /day, similar to yields at lower OLRs (2.6 and 3.4 g COD fed /L/day). This performance persisted after the daily nanosheet replenishment stopped. Improvements were due to enhanced conductivity and microbial syntropy. The results showed a strong correlation with previous biochemical methane potential (BMP) assays, positioning BMPs as a predictive tool for continuous-flow AD performance. Overall, this study demonstrated potential of GO/MGO nanosheets to improve the stability and efficiency of AD systems under stress.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".