Charles Compson

Characteristic Thickness for a Dense La0.8Sr0.2MnO3 Electrode

Dense La0.8Sr0.2MnO3 LSM electrodes were patterned by photolithography and fabricated via pulsed-laser deposition on Y2O3-stabalized ZrO2 YSZ electrolytes. Impedance analysis shows that the interfacial polarization resistance decreases significantly as electrode thickness drops below a critical value, beyond which the top surface of the LSM becomes active for oxygen reduction. However, when the LSM electrodes become too thin, the in-plane sheet resistance of the LSM starts to limit the utilization of the electrodes along their length. Quantification of the characteristic thickness is important not only to intelligent design of practical mixed-conducting electrodes but also to electrode design for fundamental studies.

Nanostructured and functionally graded cathodes for intermediate-temperature SOFCs

Nanostructured composite cathodes graded in both composition and microstructure have been successfully fabricated for the first time using a combustion chemical vapor deposition (CCVD) process. The functionally graded structures of these cathodes dramatically increase the rates of electrode reactions, enhance the transport of oxygen molecules to the active reaction sites, and significantly improve the compatibility between the electrodes and other cell components. As a result, extremely low interfacial polarization resistances and high power densities have been achieved at operating temperatures of 600–850°C, suggesting that the CCVD process has great potential for cost-effective fabrication of nanostructured fuel cell electrodes.

Data analytics using canonical correlation analysis and Monte Carlo simulation

A canonical correlation analysis is a generic parametric model used in the statistical analysis of data involving interrelated or interdependent input and output variables. It is especially useful in data analytics as a dimensional reduction strategy that simplifies a complex, multidimensional parameter space by identifying a relatively few combinations of variables that are maximally correlated. One shortcoming of the canonical correlation analysis, however, is that it provides only a linear combination of variables that maximizes these correlations. With this in mind, we describe here a versatile, Monte-Carlo based methodology that is useful in identifying non-linear functions of the variables that lead to strong input/output correlations. We demonstrate that our approach leads to a substantial enhancement of correlations, as illustrated by two experimental applications of substantial interest to the materials science community, namely: (1) determining the interdependence of processing and microstructural variables associated with doped polycrystalline aluminas, and (2) relating microstructural decriptors to the electrical and optoelectronic properties of thin-film solar cells based on CuInSe2 absorbers. Finally, we describe how this approach facilitates experimental planning and process control.Data analytics: Non-linear model for establishing correlationsA method for quantifying non-linear relationships provides insight into the connections between microstructure and properties of materials. Canonical correlation analysis is a common technique used to quantify the relationship between two sets of variables but it is often difficult to apply when the relationships are non-linear. An international team of researchers led by Jeffrey Rickman from Lehigh University now present a Monte-Carlo-based extension of canonical correlation analysis that can be applied to scenarios where non-linear variable dependencies are likely. They demonstrate this approach by establishing correlations between the variables responsible for abnormal grain growth in a ceramic oxide, as well as the variables that are most important in connecting the microstructure to the electrical and optoelectronic properties of certain solar cells, showing the range of materials systems that this approach could be used for.