Functionalized graphene nanosheets as filtration membranes for theremoval of Cd2+ from wastewater
Bibliographic record
Abstract
Toxic metals such as Pb, Cu, Cd, Zn, Ag, and Hg can be seen in industrial wastewater. The existence of these heavy metals in environment can cause liver and kidney disease, and thus is extremely harmful. Filtration-based membranes, such as reverse osmosis and nanofiltration membranes, can help to remove heavy metals from contaminated water by physical separation. The membrane acts as a barrier that allows only water molecules to pass through, while blocking pollutants, e.g., heavy metal ions. The process can effectively reduce the concentration of heavy metals in water, making it safer for consumption and other uses. In this study, the performance of nonporous graphene (NPG), which is functionalized by hydrogen (NPG-H) and hydroxyl (NPG-OH) as a nanostructure membrane, has been investigated by molecular dynamics techniques. These membranes had two different pore sizes, for which the radius is 5 and 10 , respectively, leading to 4 different types of membranes. An external pressure was applied to the system to mimic pressure-driven filtrations. It was found that with a small pore (5 ), both NPG-H and NPG-OH totally rejected Cd 2+ and Cl -1 , while with a large pore (10 ), Cd 2+ gradually passed through NPG-H and NPG-OH membranes. In terms of water molecules filtered, membranes with large pores have a larger permeability compared to those with small pores. For the effects of functional groups, compared to NPG-OH, NPG-H can allow more water molecules to permeate the pore, regardless of pore sizes. On the other hand, the rejection rates of functional groups for Cd 2+ and Cl -1 show dependences on pore sizes. The underlying mechanisms for these observations will be explored. The results obtained shed lights on the effects of pore sizes and functional groups on water permeability and Cd 2+ rejection rate for graphene-based filtration membranes.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".