Electrochemically Fabricated NiW on a Cu Nanowire as a Highly Porous Non-Precious-Metal Cathode Catalyst for a Proton Exchange Membrane Water Electrolyzer

2019 ◽  
Vol 7 (9) ◽  
pp. 8265-8273 ◽  
Author(s):  
Hoyoung Kim ◽  
Hyanjoo Park ◽  
Dong-Kwon Kim ◽  
SeonHwa Oh ◽  
Insoo Choi ◽  
...  
Keyword(s):  
Precious Metal ◽  
Proton Exchange Membrane ◽  
Proton Exchange ◽  
Cathode Catalyst ◽  
Metal Cathode ◽  
Exchange Membrane ◽  
Highly Porous

Ionomer content effect on charge and gas transport in the cathode catalyst layer of proton-exchange membrane fuel cells

2021 ◽  
Vol 490 ◽  
pp. 229531
Author(s):  
Yurii V. Yakovlev ◽  
Yevheniia V. Lobko ◽  
Maryna Vorokhta ◽  
Jaroslava Nováková ◽  
Michal Mazur ◽  
...  
Keyword(s):  
Fuel Cells ◽  
Proton Exchange Membrane ◽  
Gas Transport ◽  
Catalyst Layer ◽  
Proton Exchange ◽  
Cathode Catalyst ◽  
Cathode Catalyst Layer ◽  
Exchange Membrane

Proton-Conducting Polymer Wrapped Cathode Catalyst for Enhancing Triple-Phase Boundaries in Proton Exchange Membrane Fuel Cells

2021 ◽  
Author(s):  
Meenakshi Seshadhri Garapati ◽  
Divya Nechiyil ◽  
Sébastien Joulié ◽  
Revathi R. Bacsa ◽  
Ramaprabhu Sundara ◽  
...  
Keyword(s):  
Fuel Cells ◽  
Conducting Polymer ◽  
Proton Exchange Membrane ◽  
Proton Exchange ◽  
Cathode Catalyst ◽  
Phase Boundaries ◽  
Proton Conducting ◽  
Exchange Membrane

Parametric Analysis of the Cathode Catalyst Layer of Proton Exchange Membrane Fuel Cells Using Artificial Neural Network

2011 ◽  
Author(s):  
N. Khajeh-Hosseini-Dalasm ◽  
S. Ahadian ◽  
K. Fushinobu ◽  
K. Okazaki
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Proton Exchange Membrane ◽  
Structural Parameters ◽  
Catalyst Layer ◽  
Proton Exchange ◽  
Cathode Catalyst ◽  
Cathode Catalyst Layer ◽  
Artificial Neural ◽  
Exchange Membrane

A mathematical model was developed to study the cathode catalyst layer (CL) performance of a proton exchange membrane fuel cell (PEMFC). A number of CL parameters affecting its performance are implemented into the CL agglomerate model. These parameters are: saturation and eight structural parameters, i.e., ionomer film thickness covering the agglomerate, agglomerate radius, platinum and carbon loading, membrane content, gas diffusion layer penetration content and CL thickness. An artificial neural network (ANN) approach along with statistical methods was used for modeling, prediction, and analysis of the CL performance, which is determined by activation over-potential. The ANN was constructed to develop a relationship between the named (input) parameters and activation overpotential. An statistical analysis, namely, analysis of means (ANOM) was performed on the data obtained by the trained ANN and resulted in the main effect of each input parameter, sensitivity factors of structural parameters and their mutual combination.

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