Characterizing Transport Properties of Surface Charged Nanofiltration Membranes via Model-Based Data Analytics.

This study presents an integrated framework for characterizing the transport properties of surface-charged nanofiltration (NF) membranes through dynamic diafiltration experiments and model-based data analytics. By incorporating startup dynamics, time corrections, and the influence of water flux on solute permeation, the framework effectively captures key transport properties governing membrane performance. A comparison of lag and overflow experimental modes highlights the superior precision of the lag startup mode, improving parameter estimates by 8%, 138%, and 83% as quantified by A-, D-, and E-optimality, respectively. Diafiltration experiments in the diluting regime further enhance model discrimination, enabling the identification of dominant transport mechanisms. This work marks a step toward developing self-driving laboratories (SDLs) that leverage model-based design of experiments (MBDoE) to expedite the characterization and development of high-performance NF membranes. The framework provides critical insights into transport phenomena, enabling the inverse design of membranes tailored to the demands of modern separation systems.

Characterizing Transport Properties of Surface Charged Nanofiltration Membranes via Model-Based Data Analytics. Journal Articles