Solar-driven CeO2 catalysis for treating synthetic and real oil sands process water: Insights into kinetics, by-products, and toxicological response
Bibliographic record
Abstract
This study evaluates for the first time the use of solar-activated cerium oxide (CeO 2 ) for the degradation of synthetic and natural naphthenic acids (NAs), persistent and toxic contaminants frequently detected in oil sands process water (OSPW). The solar/CeO 2 system was applied to degrade a single model NA, a mixture of model NAs with different chemical structures, and natural NAs in real OSPW. Under simulated solar light, more than 96 % of 5-phenylvaleric acid (PVA; 20 mg/L), used as a representative model NA, was removed using 0.025 g/L of CeO 2 within 180 min. Similarly, high degradation was achieved for a mixture of six model NAs (3 mg/L each, carbon numbers 6 to 11), with all compounds showing over 90 % removal. The findings suggest that NAs with higher carbon numbers showed greater reactivity compared to lower carbon NAs. In real OSPW, more than 90 % of classical NAs were removed using 1.0 g/L of CeO 2 in 480 min, demonstrating the potential of the system in complex water matrices. PVA degradation was proposed to occur through oxidation, hydroxylation, and chain cleavage, with • OH species playing a key role. Normalized degradation efficiency (NDE) and normalized performance index (NPI) were introduced. Notably, the immunotoxic and cytotoxic effects of untreated OSPW were significantly reduced after treatment, showing the potential for the safe future reuse of this industrially impacted matrix. • CeO 2 was applied for the degradation of synthetic and natural naphthenic acids (NAs). • Solar-activated CeO 2 achieved >96 % degradation of model NAs and > 90 % in real OSPW. • • OH, O 2 •– and h + were the main reactive species in NAs degradation, with • OH playing the dominant role. • A significant reduction in OSPW-induced immunotoxicity was observed after treatment. • Bioavailable hydrocarbons linked to toxicity in real OSPW were reduced by 87 %.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".