Tailoring Nanoparticle Designs for Cleaning up Oil Sands Process-affected Water
Bibliographic record
Abstract
Oil sands industry in Canada continuously produces enormous volume of toxic and low-quality oil sands process-affected water (OSPW) as a result of bitumen extraction, upgrading, and transportation. Typically, OSPW are treated by several chemical treatment stages that primarily focus on water recycling at high capital and operating costs with high environmental footprints. In this study, naturally sourced nanoparticles were designed for cleaning up OSPW through effective and combined process. Thus, nanoparticles with tunable properties were developed to generate hybrid filter media, nanoflocculants, and oil spill nanoscavengers. For the case of steam assisted gravity drainage (SAGD) produced water, under ambient conditions, hybrid filter media were manufactured via integrating low percentage (< 5 wt%) of iron hydroxide nanoparticles with walnut shell filter media (WS), in which the iron hydroxide nanoparticles elevated the active surface area for simultaneous removal of total organic carbon (TOC) and silica. For the tailing water, titanomagnetite nanoparticles, naturally known as ironsand, were synthesized uner ambient conditions and grafted with hydrophobically modified polyacrylamide with lauryl sulfate, forming novel nanoflocculant that was applied to flocculate the mature fine tailings (MFT). Furthermore, the bare titanomagnetite nanoparticles were employed for removal of crude oil spills, following our modified ASTM protocol. The results showed that the hybrid filtration media (WS-NPs) removed up to 85% of silica and TOC through the batch experiments. In the column tests WS-NPs significantly improved the breakthrough behavior without reaching pressure drop limitations. The column breakthrough behaviors were successfully described by a dimensionless advection-axial dispersion model, that was able to accurately capture the real breakthrough behavior, indicating the possibility of scaling up the filter using a combined unit that removes silica and TOC simultaneously. Also, flocculation of the MFT suspension with applying 3000 ppm of the optimized nanoflocculants, against 20,000 ppm of commercially anionic polyacrylamide, provided 15 times faster initial settling rate (ISR) and half values of supernatant turbidity, capillary suction time (CST), and specific resistance to filtration (SRF). Interestingly, a gram of our inhouse prepared iron sand nanoparticles removed 38g crude oil, showing outstanding performance toward oil spill removal.
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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".