Tohoku University researchers have developed a method to predict the performance of new catalysts for fuel cells, potentially accelerating the development of more efficient clean energy solutions. Researchers at Tohoku University have developed a reliable tool to predict the performance of a new and promising type of catalyst. Their findings will accelerate the development of effective catalysts for both alkaline and acidic environments, thus saving time and effort in future attempts to create better fuel cells.

Details of their research were recently published in the journal. Chemical Science.

Fuel cell technology is often touted as a promising clean energy solution; However, problems with the efficiency of the catalyst prevent its widespread use.

Molecular metal-nitrogen-carbon (MNC) catalysts have excellent structural properties and excellent electrocatalytic performance, especially for the oxygen reduction reaction (ORR) in fuel cells. They offer a cost-effective alternative to platinum-based catalysts.

Unique properties of MNC catalysts

One of these types of MNC catalysts is metal-doped azaphthalocyanine (AzPc). They have unique structural features characterized by long functional groups. When these catalysts are placed on a carbon substrate, they take on three-dimensional shapes, similar to a dancer placed on stage. This change in shape affects how well they perform for ORR at different pH levels.

However, translating these useful structural features into improved performance is a challenge that requires extensive modeling, validation, and resource-intensive experiments.

“To overcome this, we used computer simulations to study how the performance of oxygen reduction reactions of a carbon-supported Fe-AzPCS catalyst varies at different pH levels and looked at how electric fields interact with pH and the surrounding functional group,” Hao said. Li is an associate professor at Tohoku University Advanced Materials Research Institute (WPI-AIMR) and corresponding author of the paper.

Analyzing the efficiency of Fe-AzPcs in ORR, Li and colleagues assembled large molecular structures with complex long-chain arrangements, or “dance patterns” of more than 650 atoms. More importantly, experimental data showed that the microkinetic patterning associated with the pH field closely matched the observed ORR efficiency.

“Our findings suggest that assessing the charge transfer that occurs in the iron region, where an iron atom typically loses about 1.3 electrons, may be a useful method to identify suitable functional groups in the environment for ORR,” adds Li. “Essentially, we established a direct benchmark assay for a microkinetic model to identify efficient MNC catalysts for ORR under different pH conditions.”