TY - JOUR
T1 - Strategies for Carbon Support Design in Fuel Cells
AU - Li, Donglai
AU - Jin, Haibo
AU - Zhao, Zipeng
N1 - Publisher Copyright:
© 2024 American Chemical Society.
PY - 2024/10/8
Y1 - 2024/10/8
N2 - Proton exchange membrane fuel cell (PEMFC) as a zero-emission power source attracts broad interest. Carbon supports, where catalytic nanoparticles are loaded, are pivotal in optimizing the performance of a PEMFC. In this perspective, we highlight an emerging trend for carbon support design and optimization. First, we summarized the carbon support preparation and modification methods: surface engineering, carbon nanocomposite engineering, and pore engineering. Then we discussed the newly developed characterization and analysis techniques, like three-dimensional electron microscopy, and distribution of relaxation times, which can be employed to obtain the precise structure information and in situ performance evaluation, which can be used as input data set to train the machine learning model. With the aid of the trained machine learning model, the optimization process of structure design and the preparation strategies for carbon support materials can be greatly accelerated as a result. Furthermore, the present challenges associated with the structure and performance analysis were pointed out.
AB - Proton exchange membrane fuel cell (PEMFC) as a zero-emission power source attracts broad interest. Carbon supports, where catalytic nanoparticles are loaded, are pivotal in optimizing the performance of a PEMFC. In this perspective, we highlight an emerging trend for carbon support design and optimization. First, we summarized the carbon support preparation and modification methods: surface engineering, carbon nanocomposite engineering, and pore engineering. Then we discussed the newly developed characterization and analysis techniques, like three-dimensional electron microscopy, and distribution of relaxation times, which can be employed to obtain the precise structure information and in situ performance evaluation, which can be used as input data set to train the machine learning model. With the aid of the trained machine learning model, the optimization process of structure design and the preparation strategies for carbon support materials can be greatly accelerated as a result. Furthermore, the present challenges associated with the structure and performance analysis were pointed out.
UR - http://www.scopus.com/inward/record.url?scp=85205912247&partnerID=8YFLogxK
U2 - 10.1021/acs.chemmater.4c01632
DO - 10.1021/acs.chemmater.4c01632
M3 - Review article
AN - SCOPUS:85205912247
SN - 0897-4756
VL - 36
SP - 9126
EP - 9138
JO - Chemistry of Materials
JF - Chemistry of Materials
IS - 19
ER -