WeChat Mini Program
Old Version Features

Performance and Relative Humidity Tolerance: Impact of Ionomer Loading Versus Equivalent Weight

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY(2025)

Foshan Univ

Cited 0|Views5
Abstract
The impact of ionomer loading (IL) and equivalent weight (EW) on proton exchange membrane fuel cell (PEMFC) performance and relative humidity (RH) tolerance is explored. Catalyst layers (CLs) are prepared using ionomers with EWs of 980, 830, or 720 g/mol and a constant loading of 3.50 x10(-7) mol HSO3/cm(2). Ionomer EW significantly influences Pt accessibility as RH is reduced below 60%. Membrane electrode assemblies (MEAs) derived from these CLs are evaluated under different RH conditions (20, 40, 60, 80 and 100% RH), with the 720 EW ionomer achieving >100 mV performance gain vs. the 980 EW ionomer at 20% RH. Electrochemical impedance spectroscopy (EIS) is used to determine an optimal cathode catalyst layer resistance (RCCL) of similar to 300-500 mOhm.cm(2), which remains consistent across MEAs, regardless of differing ionomer EWs and ILs. These finding demonstrate for the first time the inherent advantages of low EW ionomers vs higher EW ionomers when compared at equivalent CL loadings of HSO3 groups.
More
Translated text
Key words
Proton exchange membrane fuel cells,Membrane electrode assembly,Ionomer equivalent weight,Relative humidity tolerance
上传PDF
Bibtex
收藏
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined