Carbohydrate-Modified Microgels as a System for Extracting Naphthenic Acids from Tailings Pond Water
Bibliographic record
Abstract
Northern Alberta houses massive tailings ponds, that store aqueous waste as a result of the processes employed to recover bitumen from the oil sand deposits. The aqueous waste, or tailings pond water (TPW), houses numerous toxic chemicals including naphthenic acids (NAs) - a complex group of naturally occurring hydrophobic organic acids that can have adverse and even irreversible effects on their surrounding environment. The Lowary group has shown that methyl mannose polysaccharides (MMPs) have a high binding affinity for NAs, while the Serpe group has demonstrated that poly(N-isopropylacrylamide) (pNIPAM)-based micro-particles have a high binding affinity for organic molecules in general. Our research group has been developing a unique class of microgels for the removal of NAs from TPW by utilizing pNIPAM-based porous micro-particles and incorporating unmethylated and methylated derivatives of long-chained saccharide(s)-amines. Several pNIPAM-based microgels coupled or polymerized with a series of carbohydrates have been developed and their effectiveness to treat TPW was monitored using Microtox bioassay toxicity tests and Fourier-transform infrared spectroscopy (FT-IR). According to the Microtox data the carbohydrate-modified microgels have only a marginal effect treating medium fine tailings (MFT) and the top recyclable tailings water layer. However, FT-IR analysis shows that few carbohydrate-modified poly(N-isopropylacrylamide)-co-acrylic acid (pNIPAM-co-AAc) microgels lower NAs concentration in MFT: pNIPAM-co-50 % AAc-di-mann-octylamine shows the best performance by decreasing the NAs concentration within a similar range as the standard sorbent: powdered activated carbon (PAC). Overall, PAC shows the best performance treating MFT according to both Microtox bioassay and FT-IR analysis.
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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".