Semiconductor-based solar photocatalytic degradation of surrogate naphthenic acids: Insights on degradation mechanism and the effects of pH, water matrix, and compound structure on the degradation kinetics
Bibliographic record
Abstract
Semiconductor-based photocatalysis is emerging as a potent oxidation for degrading Naphthenic acids (NAs) in oil sands process water (OSPW). Here, the impacts of some of the OSPW-relevant parameters and properties were investigated to address some questions about the previously observed varied performance of a given catalyst in different OSPW samples and the differences of some catalysts in OSPW that could not be explained by their intrinsic electrophysical properties. To examine the individual impacts of the selected factors, model NAs were used with ultra-performance liquid chromatography (UPLC) for the analyses. The photocatalytic degradation kinetics was enhanced by the presence of Cl − and SO 4 2− in the water while HCO 3 − dampened the kinetics. The impact of a significant change in water pH appeared to be dependent on the direction of the change relative to the isoelectric points (IEP) of the catalyst. The reactivity of NAs in the photocatalysis was strongly structure-dependent with the alkyl branching and substituent being influential factors. The study of the reaction mechanism indicated that both O 2 − and OH mediated in the reactions. The study provides more understanding of the potentially varied dynamics of the photocatalytic process in treating different streams and can help make better-informed process designs. • Water pH and the catalyst's isoelectric point influenced the photocatalytic degradation of NAs. • CO 3 2− inhibited the photodegradation kinetics of cyclohexane carboxylic acid. • SO 4 2− enhanced the photodegradation of cyclohexane carboxylic acid. • Alkyl branching and the type of substituent on structures influenced their degradation.
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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".