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April 24, 2026Chinese Science Bulletin (Chinese Version)Open Access

Electrical conductivity of porous layers in reversible fuel cells based on transfer learning

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Authors

YRYiquan RuiBWBowen WangQDQing Du

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Overview

This research demonstrates high-precision predictions of electrical conductivity in porous layers using advanced machine learning methods, indicating improved efficiency in fuel cell technology.

Key Points

  • The aim is to enhance the efficiency and accuracy of predicting electrical conductivity in porous layers of reversible fuel cells using machine learning techniques.
  • Random reconstruction to build microscopic geometric models of carbon paper and titanium felt.
  • Development of four machine learning models using gradient boosting, support vector regression, kernel ridge regression, and deep neural networks.
  • Bayesian optimization for adaptive tuning of model hyperparameters and application of transfer learning for small sample prediction.
  • The Bayesian-optimized model predicts carbon paper electrical conductivity with an R-square of 0.9981.
  • The transfer learning model achieves an R2 of 0.9979 for titanium felt conductivity predictions.
  • SHAP analysis reveals that porosity non-linearly influences the conductivity of both materials.

Cite This Study

Rui et al. (2026) studied this question.

synapsesocial.com/papers/69eb0899553a5433e34b3765https://doi.org/10.1360/csb-2026-0141
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