Water permeation through single sub-micron pores in single layer graphene measured by a micro-particle image velocimetry technique
Bibliographic record
Abstract
Graphene holds potential as a high permeance membrane material for separation applications owing to its single atom thickness. Transport rates through graphene pores ultimately determine membrane performance and are an area of focus of design efforts. In this regard, single pore flow rate measurements are desirable because they are not influenced by material defects present in large-area samples and are unaffected by modeling assumptions used in simulations. However, measuring liquid flow rates through single graphene pores is challenging. In this paper, we establish a micro-particle image velocimetry technique to measure flow rates through single pores or small permeable areas by comparing the velocity decay rate downstream of the pore to analytical predictions for the flow field. The method is validated on silicon nitride micropores by comparison with microfluidic sensor measurements and then applied to measure water permeation rates through single sub-micron graphene pores, below the detection limit of the sensor. A 200 nm diameter pore is measured to have a pore permeation coefficient of 1.5×10−19 m3 s−1 Pa−1, and 500 nm pores are measured to have pore permeation coefficients of 7.0×10−19 and 14×10−19 m3 s−1 Pa−1. These values are less than half those predicted by continuum theory, but of the same order of magnitude. The results provide measured permeances of experimentally realized flows through single sub-micron graphene pores and a reliable technique for measuring the liquid permeance of micrometer-scale membrane areas.
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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 | 0.001 |
| 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".