High-Performance Carboxylated Polymers of Intrinsic Microporosity (PIMs) with Tunable Gas Transport Properties
Bibliographic record
Abstract
Carboxylated polymers of intrinsic microporosity (carboxylated PIMs) are reported as potential high-performance materials for membrane-based gas separation. Carboxylated PIM membranes were prepared by in situ hydrolysis of the nitrile groups of PIM-1 films. Structural characterization was performed by Fourier transform infrared spectroscopy (FTIR) and proton nuclear magnetic resonance ( 1 H NMR). The degree of hydrolysis was determined by carbon elemental analysis. The thermal properties were evaluated by differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). Compared with PIM-1, carboxylated PIMs with different degrees of hydrolysis have similar thermal and mechanical properties but show higher selectivity for gas pairs such as O 2 /N 2, CO 2 /N 2, He/N 2, and H 2 /N 2 with a corresponding decrease in permeability. Selectivity coupled to high permeability combines to exceed the Robeson upper-bound line for the O 2 /N 2 gas pair. This work demonstrates that significant improvements in gas separation properties may be obtained through postmodification of nitrile-based PIM membranes. The present work improves the understanding of the relationship of structure/permeation properties and also extends the PIM spectrum beyond those reported previously. In addition, the incorporation of carboxylic acid sites has the potential for further modification reactions such as grafting and cross-linking.
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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".