Impact of polymeric membrane filtration of oil sands process water on organic compounds quantification
Bibliographic record
Abstract
The interaction between organic fractions in oil sands process-affected water (OSPW) and three polymeric membranes with varying hydrophilicity (nylon, polyvinylidene fluoride and polytetrafluoroethylene) at different pHs was studied to evaluate the impact of filtration on the quantification of acid-extractable fraction (AEF) and naphthenic acids (NAs). Four functional groups predominated in OSPW (amine, phosphoryl, carboxyl and hydroxyl) as indicated by the linear programming method. The nylon membranes were the most hydrophilic and exhibited the lowest AEF removal at pH of 8.7. However, the adsorption of AEF on the membranes increased as the pH of OSPW decreased due to hydrophobic interactions between the membrane surfaces and the protonated molecules. The use of ultra pressure liquid chromatography-high resolution mass spectrometry (UPLC/HRMS) showed insignificant adsorption of NAs on the tested membranes at pH 8.7. However, 26±2.4% adsorption of NAs was observed at pH 5.3 following the protonation of NAs species. For the nylon membrane, excessive carboxylic acids in the commercial NAs caused the formation of negatively charged assisted hydrogen bonds, resulting in increased adsorption at pH 8.2 (25%) as compared to OSPW (0%). The use of membranes for filtration of soluble compounds from complex oily wastewaters before quantification analysis of AEF and NAs should be examined prior to application.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".