Combinatorial and High-Throughput Screening of Materials ...

ACS Combinatorial Science
REVIEW
Table 1. Examples of Materials Explored Using Combinatorial
and High-Throughput Experimentation Techniques
materials examples ref materials examples ref
superconductor materials 33 zeolites 473
ferroelectric materials 474 polymers 475
magnetoresistive materials 476 metal alloys 477
luminescent materials 478 materials for methanol
fuel cells
479
structural materials 480 materials for solid oxide
fuel cells
hydrogen storage materials 482 materials for solar cells 483
organic light-emitting materials 484 automotive coatings 247
ferromagnetic shape-memory 485 waterborne coatings 486
alloys
thermoelastic shape-memory
alloys
487 vapor barrier coatings 269
heterogeneous catalysts 488 marine coatings 263
homogeneous catalysts 489 fouling-release coatings 490
polymerization catalysts 491 organic dyes 492
electrochemical catalysts 164 polymeric sensing materials 389
electrocatalysts for
493 metal oxide sensing materials 494
hydrogen evolution
fuel cell anode catalysts 495 formulated sensing materials 332
enantioselective catalysts 496 agricultural materials 497
highly automated, have been recently refined to have more
human input, with only an appropriate level of automation.
For the throughput of 50 100 materials formulations per day,
it is acceptable to perform certain aspects of the process
manually. 34,35 While it is attractive to produce multiple smallscale
samples of materials at once using combinatorial tools, it is
important to validate the performance of the combinatorial
system by reproducing materials with good performance in
laboratory scale synthesis and performance testing under conventional
test conditions. In a reliable combinatorial workflow,
relative materials performances correlate well with those reproduced
by traditional scale fabrication and testing. Thus, a
correlation between performance of materials fabricated on the
traditional and combinatorial scales is established using known
materials.
2.1. Experimental Planning. Searching for “a needle in the
haystack” has been popular in the early days of combinatorial
materials science. 4,36,37 It was estimated that 2 86 of chemical
systems needed to be potentially investigated for their new
materials properties (see Figure 2A) however with only the
unary and binary chemical systems investigated so far. 38 Taking
into the account not only the properties of starting materials but
also the needed variable levels of process conditions, rapidly
brings the number of experimental runs in CHT screening of
simple catalytic materials up to several million (see Figure 2B). 39
It was also shown that a theoretical dimensionality of the hyperspace
of independent materials response features could be 10 21
by including the permutations of varying materials, measurement
principles, and modes of operation for each material/measurement
combination in chemical sensors (see Figure 2C). 40
It was realized further that screening of the whole materials
and process parameters space is still too costly and time
prohibitive even with the availability of existing tools. 38 Instead,
designing the CHT experiments to discover relevant descriptors
481
Figure 1. Concepts of the combinatorial materials-development workflow.
(A) Initial concept proposed by Hanak in 1970. 32 (B) Modern
“combinatorial materials cycle”. 19
became more attractive. 37 At present, methods for CHT experiment
planning can be summarized as four general classes that
include (1) traditional statistical design-of-experiments approaches,
such as factorial or fractional factorial designs that
are intended to generate statistically reliable conclusions from a
limited number of experiments; (2) “diversity” methods to cover
a space of interest using various measures to characterize
ensembles of experimental samples; (3) “search” methods to
intelligently navigate through the experiment space in a succession
of experiments; and (4) hierarchical or hybrid methods to
41 45
develop a series of experiments with increasing focus.
2.2. Materials Synthesis. Specific aspects of CHT techniques
for the synthesis, formulation, and preparation of materials are
provided in respective sections 3.1 3.5.
2.3. Materials Characterization. To address quantitation
needs of numerous materials-specific intrinsic and performance
properties, a variety of high-throughput characterization tools are
required for rapid and automated assessment of single or multiple
properties of the large number of samples fabricated together
as a combinatorial array or “library”. 24,46,47 Typical library layouts
can be discrete 27,29,33 and gradient. 28,32,48 52 A specific type of
library layout will depend on the required density of space to be
explored, available library-fabrication capabilities, and capabilities
of high-throughput characterization tools.
As indicated in Technology Roadmap for Combinatorial Methods,
Vision 2020, 53 an integration of analytical equipment with the
combinatorial reactors is of critical importance for characterization
of combinatorial libraries. Thus, significant part of research
efforts in the area of CHT materials science has been dedicated to
the development of methods for in situ quantitative monitoring
of combinatorial reactions in both discovery and optimization
phases. In situ monitoring of combinatorial reactions provides
several attractive options for high-throughput screening. Realtime
observation of the reaction progress in combinatorial
reactors can tremendously speed up the materials discovery
process by providing previously unavailable information about
581 dx.doi.org/10.1021/co200007w |ACS Comb. Sci. 2011, 13, 579–633