2D Material Nanofiltration Membranes: From Fundamental Understandings to Rational Design
Bibliographic record
Abstract
Since the discovery of 2D materials, 2D material nanofiltration (NF) membranes have attracted great attention and are being developed with a tremendously fast pace, due to their energy efficiency and cost effectiveness for water purification. The most attractive aspect for 2D material NF membranes is that, anomalous water and ion permeation phenomena have been constantly observed because of the presence of the severely confined nanocapillaries (<2 nm) in the membrane, leading to its great potential in achieving superior overall performance, e.g., high water flux, high rejection rates of ions, and high resistance to swelling. Hence, fundamental understandings of such water and ion transport behaviors are of great significance for the continuous development of 2D material NF membranes. In this work, the microscopic understandings developed up to date on 2D material NF membranes regarding the abnormal transport phenomena are reviewed, including ultrafast water and ion permeation rates with the magnitude several orders higher than that predicted by conventional diffusion behavior, ion dehydration, ionic Coulomb blockade, ion-ion correlations, etc. The state-of-the-art structural designs for 2D material NF membranes are also reviewed. Discussion and future perspectives are provided highlighting the rational design of 2D material membrane structures in the future.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".