Professor Kim Yoon-Seok of the Department of Material Engineering, Development of the Evaluation Method for Fuel Cell
- 공과대학
- Hit5213
- 2020-06-12
A research team led by Professor Kim Yoon-seok of the Department of New Material Engineering, Development of the Evaluation Method for the Distribution of Fuel Cell Components Using Atomic Force Microscope (AFM)
- Expected improvement of fuel cell performance using distribution evaluation method
Recently, attention has been focused on renewable energy due to environmental pollution problems, and research on polymer electrolytes* fuel cells that can produce electricity when injected with fuel is active. Electrodes, which are the main components of high molecular electrolyte fuel cells, are composed of ion-conducting high molecules, catalyst** and various additives that help chemical reactions, and the distribution and composition of components are closely related to the performance of fuel cells.
* Electrolyte: A substance that melts into a solvent, such as water, and ionizes it to flow current.
** Catalyst: substance that causes chemical reactions to occur faster without changing oneself
Catalysts and additives are several to tens of nanometers (10-9 m) in size when small, and are generally used to evaluate these distributions. However, there were technical limitations to actual application, as components could be compromised during the assessment and only a small area could be assessed in a vacuum environment.
To overcome these limitations, the research team led by Professor Kim Yoon-seok (first author Seol Dae-hee, co-author Jeong Soon-ho) of the Department of New Material Engineering developed a methodology that can evaluate the distribution of fuel cell electrodes using Atomic Force Microscope* and Machine Learning algorithms with the MEA Design Team of Hyundai Motor and Dr. Jang Jae-hyuk of the Korea Basic Science Support Institute.
* Atomic force microscope: Microscope that measures the surface shape of an object to be evaluated using a tiny needle called a probe and the force acting between the object to be evaluated, using it to measure the properties of a substance, such as an electric current.
Based on the fact that the current characteristics of each component may be different, the research team measured the current using an atomic force microscope, and was able to evaluate the distribution of each component by applying this measured current value to the underlying machine learning algorithm.
Professor Kim Yoon-seok, who led the study, said, "This study will help us to improve the performance of fuel cells as it can evaluate which type of distribution is effective in improving the performance of fuel cells. It is expected to help us apply eco-friendly fuel cells such as cars in the future."
Researcher Chung Soon-ho said, "The application of basic machine learning algorithms has made it easier to assess the distribution of each component. "If we apply more systematic machine learning algorithms in the future, we can evaluate distribution more accurately and effectively."
This study was conducted with the support of Hyundai Motor Company and the Korea Research Foundation (2017R1A2B2003342), the intensive research institute (2019R1A1A03033215), and the Korea Institute of Energy Technology Evaluation (20173010032080).
The results of the study were published online on May 20 (Wednesday) in the issue of ACS Applied Materials&Interfaces (IF 8.456), an international journal of relevant fields.
※ Data Mining of Heterogenous Electrical Production in the Electrode Components of Fuel Cells