Bibliographic record
Abstract
Water pollution poses a significant environmental challenge that has garnered considerable attention, primarily due to its role in depleting vital resources. The separation of oil from water presents a complex issue for industries, particularly when large quantities of stable oil/water emulsions are released. This article reviews recent advancements, particularly over the past seven years, in the membrane separation of oil-water mixtures utilizing nanofiber membranes through filtration and absorption methods. Among the various techniques for fabricating membranes, electrospinning has emerged as a favored approach due to its ease of mass production and the potential for integrating other functional materials at the nanoscale. This method has gained significant interest in developing innovative nanofibrous membranes characterized by selective wettability, optimized pore structures, and high specific surface areas. Electrospinning is recognized as the most versatile technique for producing nanofibers embedded with diverse active agents. Several strategies have been explored, including the electrospinning of polymer blends rather than single polymeric materials, surface modifications through coating or grafting, and the incorporation of nanofillers to create mixed matrix membranes. Notably, numerous efforts have been made to manipulate surface hydrophobicity and oleophobicity by designing hierarchical and Janus structures. The future of electrospinning technology appears promising for the design and fabrication of next-generation materials for oil-water separation.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".