Chemists and physicists at SFU are conducting interdisciplinary research that will help determine the architecture of nanoparticles using X-ray absorption spectroscopy data. This approach allows us to speed up the process of processing data and finding the best catalysts for low-temperature fuel cells.
Scientists from the Southern Federal University conducted a joint interdisciplinary scientific project dedicated to studying the architecture of bimetallic nanoparticles as part of electrocatalysts for low-temperature fuel cells. The essence of the research is to automate the process of processing experimental data through the use of machine learning algorithms.
The work was attended by employees of the Department of Theoretical and Computational Physics of the Faculty of Physics of SFU, Professor, Doctor of Physical and Mathematical Sciences. Leon Avakyan , Doctor of Physical and Mathematical Sciences, Professor Lusegen Bugaev, and employees of the laboratory “Nanostructured materials for electrochemical energy” of the Faculty of Chemistry of Southern Federal University, Doctor of Chemical Sciences, chief researcher Vladimir Guterman , leading researchers Sergey Belenov and Anastasia Alekseenko .
“As physicists, we are engaged in the processing and in-depth analysis of experimental data in materials science. Since we ourselves do not synthesize new materials, cooperation with the group of the Faculty of Chemistry of SFU is vital for us. They are the ones who synthesize new materials with outstanding and unusual properties,” Leon Avakyan, professor of the Department of Theoretical and Computational Physics of the Faculty of Physics of SFU.
“Interdisciplinary research allows us to obtain a new quality of work, since without modern research methods and approaches to data processing from colleagues from the Faculty of Physics, it is impossible to characterize in detail the architecture of nanoparticles obtained at the Faculty of Chemistry,” added Anastasia Alekseenko, leading researcher at the Faculty of Chemistry of SFU.
The machine learning algorithms used by the scientists revealed a significant sensitivity of the theoretical metal radial distribution functions to the architecture of bimetallic nanoparticles. According to the scientists, these results can be used to determine the architecture of nanoparticles using X-ray absorption spectroscopy data. The results of this research will automate the process of determining the structure of complex nanoparticles, which will accelerate the search for highly efficient catalysts for low-temperature fuel cells.
“The results were obtained based on previously performed EXAFS measurements at the BESSY II Synchrotron (Berlin, Germany). The functions of the radial distribution of atoms in bimetallic nanoparticles were obtained both theoretically, using the molecular dynamics method, and experimentally, from an analysis of the fine structure of X-ray absorption spectra (EXAFS) at the Pt L3 and Cu K edges,” noted the leading researcher at the Faculty of Chemistry SFU Sergey Belenov.
The work was supported by the Russian Science Foundation grant No. 20–79–10211.
The results of joint research were published in the international high-ranking journal Computational Materials Science.