Carbon-based polymer nanocomposite membranes for oily wastewater treatment
Bibliographic record
Abstract
Abstract Increasing oil contaminants in water is one of the major environmental concerns due to negative impacts on human health and aquatic and terrestrial ecosystems. The objective of this review paper is to highlight recent advances in the application carbon-based polymer nanocomposite membranes for oily wastewater treatment. Carbon-based nanomaterials, including graphene and graphene-oxide (GO), carbon nanotubes (CNTs), and carbon nanofibers (CNFs), have gained tremendous attention due to their unique physicochemical properties, such as excellent chemical and mechanical stability, electrical conductivity, reinforcement capability, and their antifouling properties. This review encompasses innovative carbon-based membranes for effective oil–water separation and provides a critical comparison of these membranes regarding the permeation flux, wettability, and flux recovery. The current challenges for the successful development of carbon-based nanocomposite membranes and opportunities for future research are also discussed.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".