Optimization of continuous phase in amino-functionalized metal–organic framework (MIL-53) based co-polyimide mixed matrix membranes for CO2/CH4 separation
Bibliographic record
Abstract
Nano-size non- and amino-functionalized flexible crystalline metal-organic frameworks (MOFs) MIL-53(Al) and NH2-MIL-53(Al), two co-polyimides 6FDA/ODA-DAM (1 : 1 and 1 : 4) and cross-linked co-polyimide 6FDA-ODA-DAM (1 : 1, 2% agent cross-linking APTMDS) were used to prepare mixed matrix membranes (MMMs) whilst membranes consisting of MIL-53(Al) and commercial polyimides (Matrimid 5218 and Ultem 1000) were also fabricated for comparison. Pure gases and blends of CO2 and CH4 permeation tests showed enhanced separation performance of MMMs with relatively high NH2-MIL-53(Al) loadings (CO2 permeability up to 65 Barrer, ideal selectivity up to 36.5). A detailed study of the relation between MMM properties and their morphology as affected by the nature of continuous polyimide phase was performed. The separation factor increased with MOF loading. Furthermore, these materials showed stable CO2/CH4 selectivity at increasing feed pressure, in contrast to the traditional polymer membranes. The separation performances as functions of operation temperature and composition of gas mixture were also studied. Experimental permeation data of MMMs with 6FDA-ODA-DAM (1 : 1, 2% agent cross-linking APTMDS) and up to 35 wt% NH2-MIL-53(Al) loading are in excellent agreement with the modeling predictions by both the Maxwell model and the modified Maxwell model. An approximately ideal morphology was reached when preparing MMMs. A computational optimized process was proposed in order to estimate simultaneously three independent parameters, including gas permeability of dispersed filler, interphase thickness and polymer chain rigidification factors, which would be applied for non-ideal MMM systems.
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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".