Insights into the Effects of CeO2 Nanoparticles on Medium-Chain Carboxylates Production from Waste Activated Sludge
Bibliographic record
Abstract
The synthesis of medium-chain carboxylates (MCCs) from waste-activated sludge (WAS) upgrading has received considerable attention. However, limited research has been conducted on the effects of CeO2 nanoparticles (NPs) on this process. This study showed that 1 mg/g−TS of CeO2 NPs improved the solubilization of WAS, resulting in higher production of MCCs. At 5 mg/g−TS, CeO2 NPs weakly inhibited 3 biological steps. Despite this, there was an enhancement in WAS solubilization, thus the overall production of MCCs was similar to the control. However, doses of CeO2 NPs ranging from 25–100 mg/g−TS were unable to offset biological inhibition, leading to a decrease in MCC production. The toxic mechanisms involved were not the generation of reactive oxygen species or Ce ions from CeO2 NPs to anaerobic sludge, but instead the decline of extracellular polymeric substance (EPS) and destruction of the cell membrane through physical penetration. Microbial community analysis confirmed that 1 mg/g−TS of CeO2 NPs increased the relative abundance of key bacteria involved in the anaerobic fermentation of WAS. The MCC microbe Clostridium sensu stricto was enriched in the control group, while the relative abundance of this genus was significantly reduced with 100 mg/g−TS CeO2 NPs.
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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".