Combinatorial and High-Throughput Screening of Materials ...

ACS Combinatorial Science
The PCA model has been developed to track how critical changes
in dominant spectral regions occur at polymer chemistry blends
that could not be detected by a simple direct observation of the
FTIR spectra associated with each chemistry in the combinatorial
library. Figure 6D shows a scores plot of the developed PCA model
Figure 5. Graphical representation of of PCA with T 2 and Q statistics
for multiple samples in the PCA model.
REVIEW
of the FTIR spectra. Two inflection points in the scores plot that
corresponded to compositions #19 and #53 are clearly seen,
signifying effects of chemistries of #19 and #53 samples on the
molecular structure that influences target properties. 18,98 This
example showed that the merging of informatics techniques with
combinatorial experiments provided a significant “value added”
level of interpretation to the analysis of results from combinatorial
libraries. The use of such data mining techniques is becoming an
integral part of high throughput screening methods in combinatorial
experimentation.
Data analysis comes in many forms, and scientific visualization
is a powerful tool to aid in the interpretation of CHT experiments
and especially in identifying targeted information from large data
sets resulting from these CHT experiments. A representative
example can be provided from the field of mixed-metal oxides
that play an increasingly important role in many areas of
chemistry, physics, and materials science originating from the
opportunities for tailoring of chemical composition, microstructure,
porosity, and surface properties. In principle, the combination
of several metals in an oxide matrix can produce materials
with novel physical and chemical properties that can lead to a
superior performance in technological applications ranging from
catalysis to sensing. 19,99 101 The metals can behave as “isolated
units” that bring their intrinsic properties to the system or their
Figure 6. Integration of informatics with high throughput materials characterization to extract data trends that are not easily visualized from simple
inspection of FTIR spectra and images from combinatorial experiments with polyanhydride copolymers (CPHs). A and B are FTIR spectra, and C is the
corresponding FTIR chemical imaging map for a 10 10 array of CPHs mixtures. (D) The scores plot of the PCA model of the FTIR spectra. Numbers
represent compositions of CPH (e.g., for sample 3, it contains 3 mol % of CPH and 97% of SA (SA= sebacic anhydride). 103
587 dx.doi.org/10.1021/co200007w |ACS Comb. Sci. 2011, 13, 579–633