Bioinspired nano-ordered liquid membrane for precise molecular separation
Bibliographic record
Abstract
Abstract Cellular membranes provide ideal archetypes for molecule or ion separations with sub-angstrom scale precision, which are featured with both extremely high permeability and selectivity due to the well-defined membrane protein channels. However, the development of bioinspired membranes with artificial channels for sub-angstrom scale ethylene/ethane (0.416 nm / 0.443 nm) separation remains an uncharted territory and a significant challenge. Herein, a bioinspired nano-ordered liquid membrane is constructed by a facile ion/molecule self-assembly strategy for highly efficient ethylene/ethane separation, which mimics the structure of cellular membrane elegantly and possesses plenty of three-dimensional (3D) nanochannels. The elaborate regulation of non-covalent interactions by optimizing the ion/molecule compositions within membrane confers the nano-ordered liquid structure with interpenetrating and bi-continuous apolar domains and polar domains, which results in the formation of regular carrier wires and enormous 3D interconnected ethylene transport nanochannels. By virtue of these 3D nanochannels, the bioinspired nano-ordered liquid membrane manifests simultaneously super-high selectivity, excellent permeance and long-term stability, which exceeds previously reported ethylene/ethane separation membranes. This methodology in this work for construction of bioinspired membrane with tunable 3D nanochannels through ion/molecule self-assembly will enlighten the design and development of high-performance separation membranes for angstrom/sub-angstrom scale ion or molecule separations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".