Atmospheric Pressure Photoionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Characterization of Oil Sand Process-Affected Water in Constructed Wetland Treatment
Bibliographic record
Abstract
The remediation of oil sand process-affected water (OSPW) generated during the bitumen extraction in the oil sand region of Canada is an area of ongoing research interest. One of the primary remediation challenges is the removal of residual complex organic compounds present in the OSPW. In the present study, the molecular constitution of OSPW from aerated and nonaerated wetland treatments were characterized in constructed wetland treatment systems. Negative-ion and positive-ion atmospheric pressure photoionization (APPI) Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) was used to provide extensive molecular-level analysis of the samples. Multiple aerated and nonaerated treatment wetland systems were characterized in terms of naphthenic acids (NAs), oxy-NAs, heteroatom NAs class, and double bond equivalent (DBE) versus carbon number to evaluate their molecular composition and variability. A broad range of heteroatom compound classes with variable relative abundances were identified. The DBE versus carbon number analysis revealed different levels of transformation of the compound classes, an indicator of NA fraction compound susceptibility to transformation. The selectivity and the extent of transformation of the compound classes were a function of the wetland design. The complementarity in the heteroatom classes detected in negative-ion and positive-ion APPI FT-ICR-MS highlight the need for multiple ionization methods for more complete coverage of the distribution of components in OSPW. The detailed molecular-level information can be useful for prediction of the fate and associated toxicity of the species and also treatment efficiencies of the wetland systems.
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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".