| AVS 55th International Symposium & Exhibition | |
| Applied Surface Science | Wednesday Sessions |
| Session AS-WeM |
| Session: | Advanced Data Analysis for Surface Characterization |
| Presenter: | K. Artyushkova, The University of New Mexico |
| Authors: | K. Artyushkova, The University of New Mexico J.E. Fulghum, The University of New Mexico P. Atanassov, The University of New Mexico |
| Correspondent: | Click to Email |
This talk will discuss new approaches in accelerating material development and design by building models describing structure-to-property relationship based on X-ray Photoelectron data. The science of designing of heterogeneous materials has benefited from an understanding of the chemical, surface and materials phenomena at the nanoscale. Among the more complex nano-structured functional materials that will be discussed in this talk are electrocatalysts and biocatalysts. Understanding the structure of catalysts, and linking this structure to performance is essential for identification of the active catalytic sites, for optimization of catalyst performance, and elucidation of failure mechanisms. XPS is one of the most widely utilized surface spectroscopic techniques for analysis of catalyst structure. The ability to discriminate between different surface oxidation states and chemical environments is one of the primary advantages of the use of XPS in the characterization of catalyst structures. It is critical that the XPS spectra are interpreted and quantified with a high confidence level, as this information will be a central link between structure and performance. Although, the majority of XPS analyses of catalysts are focused on identifying the oxidation state and overall speciation, the ambiguity in peak assignment from overlapping peak components in XPS spectra is still a significant problem. Multivariate statistical methods of data analysis (MVA) are of critical importance in developing unambiguous methods of XPS data interpretation. Correlation of XPS structural data to any other property, such as derived from BET porosity, microscopic images and performance characteristics, represents a multivariate problem. Initially, Principal Component Analysis and Correlation maps will be used to study qualitative correlations between amounts of chemical species detected by XPS and variety of relevant for particular system macroscopic properties such as surface area, pore size distribution, electrochemical performance, corrosion rate, etc. In order to learn about relationship between several independent variables and a dependent variable and to determine the magnitude of those relationships, a variety of Regression Models are widely used. Multiple Linear Regression along with Genetic Algorithm for Variable selection will be discussed in attempt to build a predictive model between XPS, macroscopic parameters and performance characteristics.