Discovering metal-organic framework/polymer mixed-matrix membranes via machine learning for CO2 separation
Feride Neva Yüngül, Seda Keskin · 2026
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Description
Feride Neva Yüngül and Seda Keskin used machine learning to hunt gas-sorting membranes that beat the usual limits. Published in 2026, the study paired tiny-scale mock-ups with learning models to forecast gas flow through 104,196 mixed-matrix membranes. Plastic-only membranes must trade flow for choosiness, and testing every metal-organic frame mix by mock-up costs too much.
Mock-ups supplied gas uptake and spread data for 3,982 made frameworks and 4,701 dreamed-up ones. Three learning models then forecast flow for four gases and were checked against both mock-ups and lab measures. Many mixes beat the old upper bounds with very high carbon dioxide flow, and framework shape and chemistry steered results most. Readers get a fast route to top films for natural gas cleaning, flue gas work, and hydrogen capture.
Catalog record
| CREATOR | Feride Neva Yüngül, Seda Keskin |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 49 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |