Substantially Improving CO<sub>2</sub> Permeability and CO<sub>2</sub>/CH<sub>4</sub> Selectivity of Matrimid Using Functionalized-Ti<sub>3</sub>C<sub>2</sub>T<sub><i>x</i></sub>
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Mixed-matrix membranes (MMMs) with favorable interfacial interactions between dispersed and continuous phases offer a promising approach to overcome the traditional trade-off between permeability and selectivity in membrane-based gas separation. In this study, we developed free-standing MMMs by embedding pristine and surface-modified Ti 3 C 2 T x MXenes into Matrimid 5218 polymer for efficient CO 2 /CH 4 separation. Two-dimensional Ti 3 C 2 T x with adjustable surface terminations provided control over these critical interfacial interactions. Characterization (Raman spectroscopy, XPS, DSC, FTIR) indicated the formation of hydrogen bonds between the termination groups on Ti 3 C 2 T x and the carbonyl groups of Matrimid, promoting enhanced compatibility and dispersion of MXenes within the polymer matrix. The resulting MMMs with 5 wt % Ti 3 C 2 T x showed a 67% increase in CO 2 permeability and an 84% enhancement in CO 2 /CH 4 selectivity compared to pristine Matrimid membranes. Surface modification of Ti 3 C 2 T x using [3-(2-aminoethylamino)propyl]trimethoxysilane (AEAPTMS) further enhanced compatibility, leading to MMMs with 140% higher CO 2 permeability and 130% greater CO 2 /CH 4 selectivity. Molecular simulations suggested that AEAPTMS functionalization improved interfacial interactions with Matrimid chains, increasing the affinity of MXenes toward CO 2 molecules. Additionally, the elongation of gas pathways, polymer chain disruption, and the presence of interlayer nanogalleries contributed positively to the enhanced separation performance. This work provides insights into tailoring nanomaterial–polymer interfaces to address the challenges of gas separation, paving the way for environmentally friendly CO 2 separation technologies.
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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 | 0.001 |
| 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".