Embedding Molecular Amine Functionalized Polydopamine Submicroparticles into Polymeric Membrane for Carbon Capture
Bibliographic record
Abstract
In this study, a facile and novel facilitated transport mixed matrix membrane (FT-MMM) was fabricated by incorporating molecular amine functionalized polydopamine (PDA) into Pebax MH 1657. Polydopamine submicroparticles were chemically modified by tetraethylenepentamine (TEPA). The catechol groups on PDA can react with amino groups through the Michael addition reaction and Schiff base reaction. Grafting molecular amines onto PDA submicroparticles can not only improve interfacial compatibility between the fillers and a polymeric matrix but also enhance the facilitated transport of CO 2 in the membrane because of the abundant CO 2 -philic groups. The effects of modified PDA submicroparticle content on the membrane permselectivity were investigated, and it was found that FT-MMM with a loading of 5% PDA submicroparticles exhibited the optimum CO 2 /CH 4 separation property under humid conditions. The selectivity of Pebax-PDA/TEPA(5) membrane was 27.5, about 1.5-fold higher than that of the pristine Pebax membrane under identical operating conditions, while the membrane permeability remained comparable. Furthermore, the effects of operating pressure and temperature on the membrane separation performance were evaluated as well.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".