Facile Size-Selective Defect Sealing in Large-Area Atomically Thin Graphene Membranes for Sub-Nanometer Scale Separations
Bibliographic record
Abstract
Atomically thin graphene with a high-density of precise subnanometer pores represents the ideal membrane for ionic and molecular separations. However, a single large-nanopore can severely compromise membrane performance and differential etching between pre-existing defects/grain boundaries in graphene and pristine regions presents fundamental limitations. Here, we show for the first time that size-selective interfacial polymerization after high-density nanopore formation in graphene not only seals larger defects (>0.5 nm) and macroscopic tears but also successfully preserves the smaller subnanometer pores. Low-temperature growth followed by mild UV/ozone oxidation allows for facile and scalable formation of high-density (4–5.5 × 1012 cm–2) useful subnanometer pores in the graphene lattice. We demonstrate scalable synthesis of fully functional centimeter-scale nanoporous atomically thin membranes (NATMs) with water (∼0.28 nm) permeance ∼23× higher than commercially available membranes and excellent rejection to salt ions (∼0.66 nm, >97% rejection) as well as small organic molecules (∼0.7–1.5 nm, ∼100% rejection) under forward osmosis.
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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 | 0.001 |
| 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".