Molecular Sieving Across Centimeter-Scale Single-Layer Nanoporous Graphene Membranes
Bibliographic record
Abstract
Molecular sieving across atomically thin nanoporous graphene is predicted to enable superior gas separation performance compared to conventional membranes. Although molecular sieving has been demonstrated across a few pores in microscale graphene membranes, leakage through nonselective defects presents a major challenge toward realizing selective membranes with high densities of pores over macroscopic areas. Guided by multiscale gas transport modeling of nanoporous graphene membranes, we designed the porous support beneath the graphene to isolate small defects and minimize leakage through larger defects. Ion bombardment followed by oxygen plasma etching was used to produce subnanometer pores in graphene at a density of ∼10 11 cm –2 . Gas permeance measurements demonstrate selectivity that exceeds the Knudsen effusion ratio and scales with the kinetic diameter of the gas molecules, providing evidence of molecular sieving across centimeter-scale nanoporous graphene. The extracted nanoporous graphene performance is comparable to or exceeds the Robeson limit for polymeric gas separation membranes, confirming the potential of nanoporous graphene membranes for gas separations.
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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 | 0.000 | 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 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".