Development of novel mixed matrix membranes (MMMs) for oil sands wastewater treatment: A critical review
Bibliographic record
Abstract
Abstract While oil sands production plays a significant role in Canada's economy, the rise in oil sands production leads to increasing water withdrawal, consumption, storage and contamination that threaten the sustainability of water sources, biodiversity, ecosystem and public health. Effective treatment and reuse of oil sands process‐affected water (OSPW) can be a strategic solution for these issues. Membrane technology has emerged as a favourite choice for OSPW treatment with high removal and energy efficiency, small footprint and facile operation, installation and scale‐up. However, challenges also exist for membrane technologies related to fouling that causes a rapid decline in membrane performance. Mixed matrix membranes (MMMs) prepared by mixing superhydrophilic zwitterionic materials and inorganic nanoparticles into host membranes are anticipated as next‐generation membrane designs with significant potential for OSPW treatment by achieving multifunctionalities including fouling resistance, improved water permeability, selectivity and mechanical strength. Reproducibility and feasibility for large‐scale industrial applications remain important research questions for the production of MMMs for OSPW treatment. This study provides new insight on the performance, stability and durability of MMMs, outlooking to the commercialization prospect of MMMs. The research outcomes therefore can provide valuable knowledge for the design and development of high‐quality membranes with the required characteristics for OSPW treatment applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".