Mechanical property measurements of heterogeneous materials by selective nanoindentation: Application to LiMn2O4 cathode

Mechanical properties of composite materials for application as electrodes in batteries have been measured by means of selective statistical nanoindentation. The sample is strongly heterogeneous, as it consists of LiMn2O4 particles, carbon black and PVDF embedded in a soft and compliant epoxy matrix. For comparison, a similar composite sample of SiO2 particles in epoxy was prepared. The difference in terms of elastic modulus between the matrix and the particles is of one order of magnitude. Structural compliance and edge effects induce inconsistent tests which in return cause spurious measurements. A 2-step filtering method has been designed to overcome this problem. This automated method consists in identifying spurious tests and withdraw them from the final statistical analysis. First, nonquadratic load versus displacement curves are filtered out. Then, the Joslin-Oliver analysis is used to filter out tests with an apparent structural compliance. The method greatly improves the noise to signal ratio. After deconvolution, the E-modulus of the silica particles was measured as 69.8GPa (±1.2). It shows the reliability of the method. 105GPa (±7.5) was found for the LiMn2O4 particle E-modulus. After pile-up correction, the real E-modulus of LiMn2O4 particles is estimated to be 13% smaller. The developed method has been demonstrated to be an effective tool to investigate mechanical properties of composites

Sequential piezoresponse force microscopy and the ‘small-data’ problem

The term big-data in the context of materials science not only stands for the volume, but also for the heterogeneous nature of the characterization data-sets. This is a common problem in combinatorial searches in materials science, as well as chemistry. However, these data-sets may well be 'small' in terms of limited step-size of the measurement variables. Due to this limitation, application of higher-order statistics is not effective, and the choice of a suitable unsupervised learning method is restricted to those utilizing lower-order statistics. As an interesting case study, we present here variable magnetic-field Piezoresponse Force Microscopy (PFM) study of composite multiferroics, where due to experimental limitations the magnetic field dependence of piezoresponse is registered with a coarse step-size. An efficient extraction of this dependence, which corresponds to the local magnetoelectric effect, forms the central problem of this work. We evaluate the performance of Principal Component Analysis (PCA) as a simple unsupervised learning technique, by pre-labeling possible patterns in the data using Density Based Clustering (DBSCAN). Based on this combinational analysis, we highlight how PCA using non-central second-moment can be useful in such cases for extracting information about the local material response and the corresponding spatial distribution