SOURCE-LINKED INTELLIGENCE

Sense-Capture-Destroy-Regenerate: A multi-stimuli molecular strategy for autonomous removal of emerging micropollutants from wastewater

CORDIS · observation · Publication date unknown

elf) and external (e.g., light, pH) stimuli, how time-dependent architectural changes influence pollutant capture, mass transfer, and degradation, STELLAR integrates three synergistic advances: (i) a machine learning/density functional theory (ML/DFT) discovery loop to rationally design and link electronic structure and architecture to adaptive functionality; (ii) decision making materials that switch between adsorption and photocatalysis in response to environmental cues and (iii) reconfigurable 4D-printed morphing modules that enhance performance and enable regeneration through multi-stimuli-induced conformational changes. Validation of these concepts in realistic wastewater matrices will demonstrate selective removal, regeneration, and sustained process adaptability, while fundamental relationships between stimuli, structure and the system function will be uncovered via mechanistic studies. Beyond water treatment STELLAR will open entirely new avenues in catalysis, sensing, and intensified processes. STELLAR will thus lay the scientific foundations for a new generation of environm

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recordType

award

status

SIGNED

region

EU

value

2499687

unit

EUR