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Related Experiment Video

Updated: May 15, 2026

Three-Dimensionally Printed Microfluidic Cross-flow System for Ultrafiltration/Nanofiltration Membrane Performance Testing
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Three-Dimensionally Printed Microfluidic Cross-flow System for Ultrafiltration/Nanofiltration Membrane Performance Testing

Published on: February 13, 2016

Machine learning-driven multi-objective optimization of electrospun nanofibrous membranes design for membrane

Xiaolu Li1, Kai Qi Yan2, Xinxin Wei1

  • 1School of Energy and Environment, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong SAR, China.

Water Research
|May 13, 2026
PubMed
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Machine learning optimizes electrospun membranes for membrane distillation (MD). This data-guided approach accelerates the design of membranes with desired properties, overcoming limitations of traditional methods.

Area of Science:

  • Materials Science and Engineering
  • Chemical Engineering
  • Separation Technologies

Background:

  • Electrospinning fabricates tunable membranes for membrane distillation (MD).
  • Optimizing electrospun membrane properties requires complex control of solution and processing parameters.
  • Conventional trial-and-error methods are inefficient for navigating the vast parameter space.

Purpose of the Study:

  • To develop a machine learning (ML) workflow for predicting and optimizing electrospun membrane properties.
  • To enable data-guided selection of polymer systems and process conditions for MD applications.
  • To establish a general framework for the forward prediction and inverse design of electrospun membranes.

Main Methods:

  • Dataset construction, ML model training, and tuning.
Keywords:
ElectrospinningMachine learningMembrane distillationMulti-objective optimization

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Last Updated: May 15, 2026

Three-Dimensionally Printed Microfluidic Cross-flow System for Ultrafiltration/Nanofiltration Membrane Performance Testing
10:19

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Published on: February 13, 2016

Electrophoretic Crystallization of Ultrathin High-performance Metal-organic Framework Membranes
07:45

Electrophoretic Crystallization of Ultrathin High-performance Metal-organic Framework Membranes

Published on: August 16, 2018

  • Feature-contribution analysis to understand parameter influences.
  • Integration of ML model with Bayesian optimization for inverse design.

    • Main Results:

    • ML workflow accurately predicts key membrane attributes (thickness, contact angle, fiber diameter, morphology).
    • Feature analysis quantifies the impact of polymer descriptors and operating parameters.
    • Membranes prepared using ML-guided conditions exhibited predicted properties.

    Conclusions:

    • A holistic ML framework facilitates rapid screening and optimization of electrospun membranes for MD.
    • The developed workflow enables efficient inverse design, accelerating material discovery.
    • This approach provides a general and extensible solution for designing advanced separation membranes.