Development of layer-by-layer assembled polyamide-imide membranes for oil sands produced water treatment
Bibliographic record
Abstract
The sustainable expansion of steam-assisted gravity drainage, as one of the most popular enhanced oil recovery methods, strongly depends on the proper management of the produced water. The strict environmental regulations have forced the oil sands industry to treat and reuse the produced water for oil extraction. Membrane separation as a single-step water treatment technique has played an important role in removing multiple-sized contaminants from wastewater. However, fouling limits the widespread application of this technology if the membrane is not modified properly to achieve antifouling propensities. Herein, we used the layer-by-layer assembly technique to sequentially coat the hydrophilic poly(diallyl dimethylammonium chloride) and polyacrylic acid on the surface of the polyamide-imide porous membrane to improve its fouling resistance. The effect of the number of bilayers on fouling and permeation properties was examined. The membrane with the highest fouling resistance and reasonable hydrodynamic permeability of 5.2 LMH/psi was achieved by coating four bilayers. This membrane exhibited a low flux decline of 50.2% and a high flux recovery ratio of 100%, while these numbers for the pristine PAI membrane were 75.9% and 97.8% under similar test conditions. The enhanced antifouling characteristics of the modified membranes indicate the viability of these membranes for oil sands produced water treatment with an easy cleaning procedure. The key parameter that contributed to the enhanced fouling resistance of the bilayer-coated membranes was the improved surface hydrophilicity, which manifests through the reduction of water contact angle from 62° ± 3° for the pristine membrane to 52° ± 2° for surface-modified membranes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".