Graphene-based materials and technologies for the treatment of PFAS in water: A review of recent developments
Bibliographic record
Abstract
Per and polyfluoroalkyl substances (PFAS) are anthropogenic chemicals used in various commercial and industrial applications. As an emerging global concern due to their ubiquity and toxicity, PFAS are the focus of ongoing environmental research. Although production is partially limited by regulations, PFAS are released in water, soil, and air worldwide. Considering their deleterious impacts on wildlife and humans, developing strategies to capture and remove PFAS is crucial. Graphene materials may be advantageously applied to PFAS remediation. A survey of graphene-based materials and technologies used to treat PFAS-contaminated water is presented in this review. First, the general concept of PFAS and their related environmental and health problems are outlined. Then, the features and structures of graphene-containing materials, including graphene quantum dots, graphene oxide (GO), reduced-GO, carbon nanotubes, and graphene nanoplatelets, are described. Finally, prevailing PFAS treatment techniques, i.e., adsorption, advanced oxidation processes, membrane separation, electrochemical separation, and hybrid applications, are described along with the mechanisms involved. Currently, PFAS cannot be effectively treated to the very low regulatory guidelines (less than one part per billion for certain compounds) using any current methods because of incomplete removal, impractical applications, or operating costs. Barriers remain, including adsorbent regeneration, membrane fouling, system scale up, and toxic by-product generation. Integrating graphene-based materials, especially graphene nanoplatelets, into treatment may address these problems if PFAS can be removed completely without secondary contamination. Further research is required to achieve effective PFAS removal. However, health and environmental risks remain associated with PFAS and graphene-based materials, which must be addressed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".