Photocatalytic Degradation of Recalcitrant Organics in Oil Sands Process Water Using Facile Synthesized ZnO and BaO@ZnO Nanoparticles: Impact of Oxide Coupling on Degradation Kinetics and Toxicity Reduction
Bibliographic record
Abstract
With increasing interest in the effective treatment of oil sands process water (OSPW), advanced oxidation processes (AOPs), particularly semiconductor-based photocatalysis, are gaining attention as promising, cost-effective, and efficient remediation techniques. In this study, zinc oxide (ZnO) and barium oxide-modified ZnO (BaO@ZnO) nanomaterials were synthesized via chemical precipitation, and the Ba-to-Zn molar ratio was optimized to enhance photocatalytic activity. This study investigates the solar-activated photocatalytic degradation of recalcitrant organic compounds in oil sands process water (OSPW) using zinc oxide (ZnO) and composite BaO@ZnO nanoparticles synthesized via chemical precipitation. The optimal composite ratio (2% mol Ba/Zn) was identified to enhance photocatalytic performance without altering ZnO’s crystal structure, significantly reducing electron–hole recombination. Under simulated solar radiation, 2%BaO@ZnO achieved ∼97% degradation of classical naphthenic acids (O 2 –NAs), outperforming pristine ZnO (∼90%) and demonstrating superior degradation of aromatic fluorophores. Electron paramagnetic resonance and scavenger studies confirmed that • OH, h +, and 1 O 2 were the primary reactive species. Acute toxicity, bioaccumulation potential, and genotoxicity of OSPW were substantially reduced post-treatment. These results highlight BaO@ZnO as a highly efficient, stable, and solar-responsive photocatalyst for sustainable remediation and reuse of OSPW.
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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 | 0.000 |
| 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".