Hierarchical Digital Twin for Mechanistic Design of Nanofiltration

Objective

Global water scarcity affects over two billion people and is projected to worsen under climate change, making it a critical 21st-century challenge. Addressing this issue aligns with key European priorities, including the European Green Deal, Zero Pollution Action Plan, and Circular Economy Strategy, all of which emphasize pollutant removal and sustainable resource use. Effective solutions require innovative, cost-effective, and environmentally sound water treatment and recovery technologies. Nanofiltration (NF) membranes, with their nanometer-sized pores, have shown exceptional potential in brackish water desalination, toxic metal removal, and lithium recovery from brines and spent batteries. However, designing NF membranes remains challenging due to the trade-off between capturing nanoscale phenomena for mechanistic accuracy and using continuum-scale models for efficient membrane-scale optimization and monitoring. The HiDDeN project addresses the central unresolved question: How can fabrication-driven nanoscale heterogeneity—specifically the geometry and chemistry of pore-throats—be robustly linked to macroscopic membrane performance to enable predictive, rational design? The project proposes a mechanistic-informed digital twin that integrates molecular-level insights into a continuum-scale model, allowing accurate performance prediction and rational membrane design. The project will span three years under a Global Fellowship, with an outgoing phase (Months 1–24) at Yale University under Prof. Haji-Akbari, an expert in molecular-scale simulations, and a return phase (Months 25–36) at the University of Bologna with Prof. Giacinti Baschetti’s team, specialists in continuum-scale modeling and experimental validation of membranes.