Removal of Naphthenic Acids from OSPW Using Pore-Widened Activated Carbons: An FT-ICR-MS Study
Bibliographic record
Abstract
This study focuses on the remediation of oil sands process-affected water (OSPW) using activated carbons (AC), specifically examining how thermal cycling affects AC’s ability to remove naphthenic acids (NA). The research aims to determine the effectiveness of single- and triple-cycled ACs in reducing contaminants from the OSPW using two analytical methods: total organic carbon (TOC) analysis and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). TOC analysis shows that triple-cycled ACs remove 71% of naphthenic acids from the OSPW, compared to 33% for single-cycled ACs, highlighting the enhanced performance of thermally cycled ACs. FT-ICR-MS is used to identify and classify the complex NA species present in the OSPW, revealing that Ox and OxSy classes dominate the NA composition. This method underscores the importance of controlling the porosity and surface functionality of ACs for effective remediation. Further analysis of double-bond equivalent (DBE) versus carbon number plots for the O2, O3, and O4 classes shows that species with higher DBE values are more effectively removed by single-cycled ACs. However, triple-cycled ACs demonstrate a broader range of DBE removal (DBE of 2–9), indicating their superior ability to eliminate highly conjugated naphthenic acids. These findings emphasize the critical role of optimizing AC pore size to enhance mesoporosity and improve the uptake of complex organic contaminants from the OSPW. The study highlights the potential of thermally tailored ACs as a viable strategy for the effective remediation of the OSPW.
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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) | 0.001 | 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".