Optimized electrode structure for performance and mechanical stability in a direct formate fuel cell using cation ionomer

2020 ◽  
Vol 4 (4) ◽  
pp. 1899-1907 ◽  
Author(s):  
Hongsun Hwang ◽  
Sujik Hong ◽  
Jin Won Kim ◽  
Jaeyoung Lee

In DFFC systems, there are different optimal points depending on the content of cation ionomer with regard to the cell performance and mechanical cell stability during the cell operation.

2013 ◽  
Vol 31 ◽  
pp. 120-124 ◽  
Author(s):  
So Young Lee ◽  
Dong Won Shin ◽  
Chenyi Wang ◽  
Kang Hyuck Lee ◽  
Michael D. Guiver ◽  
...  

2021 ◽  
Vol 4 (3) ◽  
pp. 2307-2317
Author(s):  
Aki Kobayashi ◽  
Takahiro Fujii ◽  
Chie Harada ◽  
Eiichi Yasumoto ◽  
Kenyu Takeda ◽  
...  

2021 ◽  
Vol 11 (14) ◽  
pp. 6348
Author(s):  
Zijun Yang ◽  
Bowen Wang ◽  
Xia Sheng ◽  
Yupeng Wang ◽  
Qiang Ren ◽  
...  

The dead-ended anode (DEA) and anode recirculation operations are commonly used to improve the hydrogen utilization of automotive proton exchange membrane (PEM) fuel cells. The cell performance will decline over time due to the nitrogen crossover and liquid water accumulation in the anode. Highly efficient prediction of the short-term degradation behaviors of the PEM fuel cell has great significance. In this paper, we propose a data-driven degradation prediction method based on multivariate polynomial regression (MPR) and artificial neural network (ANN). This method first predicts the initial value of cell performance, and then the cell performance variations over time are predicted to describe the degradation behaviors of the PEM fuel cell. Two cases of degradation data, the PEM fuel cell in the DEA and anode recirculation modes, are employed to train the model and demonstrate the validation of the proposed method. The results show that the mean relative errors predicted by the proposed method are much smaller than those by only using the ANN or MPR. The predictive performance of the two-hidden-layer ANN is significantly better than that of the one-hidden-layer ANN. The performance curves predicted by using the sigmoid activation function are smoother and more realistic than that by using rectified linear unit (ReLU) activation function.


RSC Advances ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 7-14
Author(s):  
Cheng Cheng Wang ◽  
Mortaza Gholizadeh ◽  
Bingxue Hou ◽  
Xincan Fan

Strontium segregation in a La0.6Sr0.4Co0.2Fe0.8O3−δ (LSCF) electrode reacts with Cr and S in a solid oxide fuel cell (SOFC), which can cause cell performance deterioration.


Polymers ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1000
Author(s):  
Guoxiao Xu ◽  
Juan Zou ◽  
Zhu Guo ◽  
Jing Li ◽  
Liying Ma ◽  
...  

Although sulfonic acid (SA)-based proton-exchange membranes (PEMs) dominate fuel cell applications at low temperature, while sulfonation on polymers would strongly decay the mechanical stability limit the applicable at elevated temperatures due to the strong dependence of proton conduction of SA on water. For the purpose of bifunctionally improving mechanical property and high-temperature performance, Nafion membrane, which is a commercial SA-based PEM, is composited with fabricated silica nanofibers with a three-dimensional network structure via electrospinning by considering the excellent water retention capacity of silica. The proton conductivity of the silica nanofiber–Nafion composite membrane at 110 °C is therefore almost doubled compared with that of a pristine Nafion membrane, while the mechanical stability of the composite Nafion membrane is enhanced by 44%. As a result, the fuel cell performance of the silica nanofiber-Nafion composite membrane measured at high temperature and low humidity is improved by 38%.


Sign in / Sign up

Export Citation Format

Share Document