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Quantifying bubble-induced diffusion resistance through real-time SAM-assisted YOLO high density bubble detection algorithm
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AI
DOI：10.1016/j.cej.2025.162422.png)
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Abstract
En 中文
The chemistry and localized structure of materials greatly affect their efficiency in gas evolution reactions leading to cell resistance loses. This work focuses on the role of morphological changes in the nature of the bubble distribution by a comparison of two different surface structures: polished Ni and various porous Ni surfaces fabricated by dynamic hydrogen bubble templating. This study introduces a refined method for counting bubbles in gas evolution reactions, focusing on high-density, overlapping bubble flows. By incorporating the Segment Anything Model (SAM), we significantly reduced the training cost of the You Only Look Once algorithm, maintaining accuracy while improving efficiency. Surface characteristics strongly influenced the bubble distribution, with superaerophobic surfaces exhibiting smaller Sauter Mean Diameter (D32) values than polished ones. A polynomial extrapolation indicated that D32s were roughly one third of the thickness of the bubble layer at higher current densities. Building on the established connection between bubble layer thickness and diffusion resistance through electrochemical impedance spectroscopy (EIS), this study establishes a direct relationship between D32 and diffusion resistance. We have also demonstrated that the characteristics of the bubble layer are independent of the electrochemical surface area (ECSA) beneath the surface. This combined approach uses EIS for broader current coverage and imaging for real-time data, providing a powerful framework for bubble management and performance monitoring in gas evolution applications.
Keywords:
Machine Learning
Electrolysis
Bubble
Hydrogen Evolution Reaction
Bubble Resistance