Bioinspired Graphene Oxide Membranes with Dual Transport Mechanisms for Precise Molecular Separation
Bibliographic record
Abstract
Abstract The implementation of membrane technology to replace or combine with energy‐intensive cryogenic distillation for precise separation of ethylene/ethane mixture proves an extremely important yet highly challenging task. Inspired by the hierarchical structure and facilitated gas transport of biological membranes, a highly selective ethylene/ethane separation membrane is explored through the fixation of a silver ion carrier and the impregnation of ionic liquid within 2D nanochannels of graphene oxide laminate, where plenty of ethylene‐permeating in‐plane nano‐wrinkles and ethylene‐facilitated plane‐to‐plane nanochannels are constructed. By virtue of synergistic effects of molecular sieving and carrier‐facilitated transport, an unprecedented combination of high ethylene permeance (72.5 GPU) and superhigh ethylene/ethane selectivity (215) is achieved, out‐performing currently reported advanced membranes. Moreover, molecular dynamics simulations verify a favorable membrane nanostructure for fast and selective transport of ethylene molecules. This bioinspired approach with dual transport mechanisms may open novel avenues to the design of high‐performance membranes for precise molecular separation.
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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 | 0.000 |
| 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 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".