Graphene-based nanomaterials for the removal of emerging contaminants of concern from water and their potential adaptation for point-of-use applications
Bibliographic record
Abstract
Considering the plethora of work on the exceptional environmental performance of 2D nanomaterials, there is still a missing link in addressing their practical application in point-of-use (POU) water treatment. By reviewing the exceptional environmental performance of 2D nanomaterials with specific emphasis on graphene and its derivatives, this review aims at inspiring further discussions and research in graphene-based POU water treatment with particular focus on the removal of emerging contaminants of concern (ECCs), which is largely missing in the literature. We outlined the prevalence of ECCs in the environment, their health effects both on humans and marine life, and the potential of efficiently removing them from water using three-dimensional graphene-based macrostructures to ensure ease of adsorbent recovery and reuse compared to nanostructures. Given various successful studies showing superior adsorption capacity of graphene nanosheets, we give an account of the recent developments in graphene-based adsorbents. Moreover, several cost-effective materials which can be easily self-assembled with nanosheets to improve their environmental performance and safety for POU water treatment purposes were highlighted. We highlighted the strategy to overcome challenges of adsorbent regeneration and contaminant degradation; and concluded by noting the need for policy makers to act decisively considering the conservative nature of the water treatment industry, and the potential health risks from ingesting ECCs through drinking water. We further justified the need for the development of advanced POU water treatment devices in the face of the growing challenges regarding ECCs in surface water, and the rising cases of drinking water advisories across the world.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".