Liu Xinhong, Estrada Laurianne, Ouimet Jonathan A, McClure Matthew, Latulippe David R, Phillip William A, Dowling Alexander W
Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, United States.
Department of Chemical Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada.
Ind Eng Chem Res. 2025 Jun 3;64(24):12111-12130. doi: 10.1021/acs.iecr.4c04763. eCollection 2025 Jun 18.
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.
本研究提出了一个综合框架，用于通过动态渗滤实验和基于模型的数据分析来表征表面带电纳滤（NF）膜的传输特性。通过纳入启动动力学、时间校正以及水通量对溶质渗透的影响，该框架有效地捕捉了控制膜性能的关键传输特性。滞后和溢流实验模式的比较突出了滞后启动模式的卓越精度，分别通过A-、D-和E-最优性量化，参数估计提高了8%、138%和83%。稀释状态下的渗滤实验进一步增强了模型判别能力，能够识别主要的传输机制。这项工作朝着开发自动驾驶实验室（SDL）迈出了一步，该实验室利用基于模型的实验设计（MBDoE）来加速高性能NF膜的表征和开发。该框架为传输现象提供了关键见解，能够根据现代分离系统的需求进行膜的逆向设计。
