质子交换膜燃料电池
材料科学
极化(电化学)
限制电流
蒙特卡罗方法
浓差极化
膜
化学工程
热力学
分析化学(期刊)
电极
电化学
统计
色谱法
化学
生物化学
物理
工程类
物理化学
数学
作者
Lunyang Liu,Tingli Liu,Fang Ding,Huan Zhang,Jifu Zheng,Yunqi Li
标识
DOI:10.1021/acsami.1c20289
摘要
The polarization curve is the most important profile to evaluate the performance of proton-exchange membrane fuel cells (PEMFCs). To explore the important thermodynamic parameters and their correlation with the composition, fabrication, and operational settings, a comprehensive data set consisting of 446 polarization curves from 191 perfluorosulfonate and 255 sulfonated hydrocarbon-based PEMs is collected. Then, a Markov chain Monte Carlo simulation within the Bayesian frame provides higher than 93% confidence to extract six important thermodynamic parameters including open-circuit potential, the transfer coefficient, the current loss, the reference exchange current density, the internal resistance, and the limiting current density. An extreme gradient boosting algorithm affords a mean determinative coefficient of 0.89 to predict the whole polarization curve and a confidence of 94% to predict the peak power density (PPD). Both approaches to explore the polarization curve for PEMFCs show good robustness in the blind test. Overall, the PPD is positively correlated with the ion-exchange capacity of the polymer, operational temperature, and humidity and is negatively affected by internal resistance, membrane thickness, and the loading of the catalyst. The flow rate of fuels can effectively enhance them, while the increase of catalyst loading or fuel concentration shows deleterious impacts.
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