Graphene Oxide Membranes for Water Isotope Filtration: Insight at the Nano- and Microscale
Bibliographic record
Abstract
Recent experimental studies have revealed the selective permeation properties of lamellar graphene oxide (GO) membranes as applied to filtration of water isotopes. In this work, we explore the molecular structures and diffusive dynamics of water isotopes in GO membrane nanochannels by employing ReaxFF reactive molecular dynamics simulations. The significance of isotope effects and their role in the interactions between light/heavy water and the functional groups of GO membranes are identified, and it is found that isotope separation is driven by a combination of phase change from liquid to vapor and kinetic fractionation due to the difference in isotope diffusivity. Our phenomenological model reveals that pervaporation mode and monolayer surface diffusion of water result in efficient isotope separation, while liquid-phase pressure-driven permeation is not an effective mass transport mode for isotope filtration. These observations suggest that there is great promise for GO membranes as a means for isotope filtration, expanding the application space of GO membranes beyond the established scope of filtration by size-exclusion and preferential adsorption mechanisms.
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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) | 0.001 | 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".