SOURCE-LINKED INTELLIGENCE
Smart materials with switchable surface for effective removal of forever chemicals
could catch the fluorinated alkyl tail and the anionic head of PFAS via fluorophilic and electrostatic interactions , respectively, thereby enhancing the selective capture of PFAS by smart hydrogels. Machine learning will guide optimal monomer combinations, accelerate synthesis and optimize performance. Scalable formats (beads, membranes) will be validated in real-water continuous-flow tests with techno-economic analysis. By combining material science, computational modelling, environmental engineering, and sustainability assessment, this project will deliver a selective, regenerable, and cost-effective PFAS treatment technology, supporting EU Green Deal and Zero Pollution goals, and providing a transferable framework for the development of smart adsorbents targeting diverse pollutants. Per- and polyfluoroalkyl substances (PFAS), smart hydrogels, adsorption, regeneration, Thermosensitive polymers, Photothermal polymers
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- recordType
- award
- status
- SIGNED
- region
- EU
- value
- 247553.28
- unit
- EUR
Evidence & attribution
European Commission, CORDIS Horizon Europe project dataset. Metadata adapted.
License: CORDIS reuse policy
First collected: 2026-09-20T04:21:15.460Z. This is not the publication date.