Porous graphene-based membranes for water purification from metal ions at low differential pressures
Bibliographic record
Abstract
A new generation of membranes for water purification based on weakly oxidized and nanoporous few-layer graphene is here introduced. These membranes dramatically decrease the high energy requirements of water purification by reverse osmosis. They combine the advantages of porous and non-oxidized single-layer graphene, offering energy-efficient water filtration at relatively low differential pressures, and highly oxidized graphene oxide, exhibiting high performance in terms of impurity adsorption. In the reported fabrication process, leaks between juxtaposed few-layer graphene flakes are sealed by thermally annealed colloidal silica, in a treatment that precedes the opening of (sub)nanometre-size pores in graphene. This process, explored for the first time in this work, results in nanoporous graphene flakes that are water-tight at the edges without occluding the (sub)nanopores. With this method, removal of impurities from water occurs through a combination of size-based pore rejection and pore-edge adsorption. Thinness of graphene flakes allows these membranes to achieve water purification from metal ions in concentrations of few parts-per-million at differential pressures as low as 30 kPa, outperforming existing graphene or graphene oxide purification systems with comparable flow rates.
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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 | 0.000 |
| 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 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".