Self‐Assembled Facilitated Transport Membranes with Tunable Carrier Distribution for Ethylene/Ethane Separation
Bibliographic record
Abstract
Abstract Facilitated transport membranes (FTMs) are a forward‐looking technology and have triggered revolutions in many energy‐intensive gas separations. However, the precise manipulation of carrier distribution within FTMs, as well as the visualization of membrane structure at the nanoscale, has never been reported. Herein, FTMs are constructed with tunable carrier distribution by a facile ion/molecule self‐assembly of protic ionic liquid crystal salts (PILSs), polyol, and ethylene‐transport carrier for highly efficient sub‐angstrom scale ethylene/ethane (0.416nm/0.443nm) separation. The elaborate regulation of non‐covalent interactions by optimizing the ion/molecule compositions within membrane confers the bi‐continuous nanostructure of FTMs, resulting in the formation of successive carrier wires and enormous 3D interconnected ethylene transport pathways, which is verified and visualized by molecular dynamics simulations and synchronous small‐ and wide‐angle X‐ray scattering (SWAXS). The as‐designed FTMs manifest simultaneously super‐high selectivity, excellent ethylene permeance, and robust long‐term stability, which exceeds previously reported ethylene/ethane separation membranes. This study clearly draws the first picture of carrier distribution within FTMs, and deep insight into membrane structure will shed light on the design of high‐performance separation membranes for energy‐intensive gas separations.
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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) | 0.001 | 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".