ANALYTICAL SCIENCES LEADING MATERIALS DEVELOPMENT

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1 JPACSM 8 ANALYTICAL SCIENCES LEADING MATERIALS DEVELOPMENT A.J. Signorelli and S.A. Curran Analytical Sciences, Specialty Materials Honeywell International K.O. MacFadden Advanced Materials and Devices, Honeywell Laboratories Honeywell International Inc. Morristown, NJ ABSTRACT Over the next several years, the distinction between materials development and materials analysis will become increasingly indistinct. Combinatorial materials development is becoming a major driver for new analytical tools, requiring incorporation of advanced data mining, capable measurement systems, and micro sampling techniques. Consequently, the role of the analytical scientist is evolving from simple service provider to full partner in new materials development. INTRODUCTION Analytical sciences has undergone radical changes in recent years, and the pace of change is accelerating. This is particularly evident in materials development. From our perspective, the role of analytical sciences in materials development has evolved from that of a simple service provider to one of full partnership in materials development. This evolution will continue and, in many cases, the analytical component will be leading the development process. EVOLUTION OF ANALYTICAL SCIENCES Clearly, analytical sciences plays an integral role in current materials development efforts. This role includes physical and compositional characterization of structural, functional, and smart materials. Let us briefly outline some characteristics and typical examples of these different classes of material. First, we have structural materials such as monolithic ceramics, or complex composites such as the pyrolitic carbon and carbon fiber composite shown in Figure 1. Such composites are used in aircraft braking systems. Analytical sciences play a critical role in defining the performance characteristics mechanical strength, wear, frictional behavior, and energy dissipation characteristics (e.g., noise and vibration). Second, we have functional materials such as catalysts, thin films as corrosion barriers, high temperature coatings, tie layers used to bond differing layers of polymers, and so on. The Triad fiber shown in Figure 2 is especially suited for filtration, and as a high surface area support, functioning in the same manner as oxide supports used in catalysis. Again, analytical sciences is critical in defining performance characteristics such as filtration efficiency, degradation during aging, etc. Finally, we have smart materials. Materials that are designed to receive a stimulus, transmit or process the stimulus, and produce a useful response. A smart material may even tell you that it is working (i.e. a self-validating sensor). A very simple example of a smart material may be a seat belt. Typically woven from a high modulus polymers such as poly(ethylene terephthalate) (PET), a seat belt is made to absorb energy during a sudden stop. If you look at a force-displacement curve you will observe that the force rises very steeply. If you want to absorb as much energy as possible during a sudden-stop one could build elastic segments into the polymer chain to increase the displacement just enough to absorb more energy and taking the stress off the rib cage and chest. Indeed, you may incorporate a piezochromic block to let you know that the belt is functioning properly. Figure 1. Carbon/carbon composite.

2 JPACSM 9 Figure 2. Triad fibers. Development of all of these types of materials is supported to one degree or another by the analytical sciences. The various materials types rely on analytical tools to characterize their behavior, or how they measure up against the expected or desired performance. The analytical scientist may be called upon to develop a new measurement system, or resolve problems associated with materials development in the laboratory, product scale-up, or manufacturing. Needless to say, continued analysis is used to support the materials product once it is launched into production. In addition, considerable resources, from a materials/analytical scientist's point of view, are usually allocated at improving a product or making incremental improvement in product performance. To a large degree what is described above is the traditional way in which we view analytical support in materials development. As a service provider, analytical scientists are constantly challenged to provide data that answers the question, How much? The major drivers for analytical development are highlighted by the holy triangle: tell me what is there and how much and do it fast, cheap and of course, right. But if the analytical sciences are to play a real role in helping to lead materials development, we need to see the holy triangle only as a base (Figure 3) that is used to support or drive the use of tools such as chemometrics, data mining, or the use of knowledge discovery databases, micro-sampling, high throughput experimentation and so on. Together these tools will drive the formulation of predictive models. Indeed, there are a growing number of instances where the analytical scientist has already become a partner in materials development. If the analyst can provide predictive models that can correlate structure to desired performance, we have the analytical sciences leading materials development. To some degree it may be argued that ceramists and metallurgists have successfully blended analytical measurement systems with their particular disciplines so that the line separating materials development and analytical sciences is completely blurred. To move from service provider to true partner or to leading materials development in other areas will demand a paradigm shift. There is no doubt that analytical sciences has led the industry in improving manufacturing productivity with on-line monitoring and data reduction technologies. At the present time analytical sciences is helping to lead the efforts in combinatorial materials efforts. The next really big technical challenge is measuring critical performance characteristics of new materials, produced in increasingly smaller amounts, and correlating that information with macro performance. Business requirements will drive us to become partners and leaders in materials development if we are to meet this technical challenge. Prediction Micro sampling Chemometrics Data Mining Cheap Cheap Right Fast Fast Right Figure 3. Analytical sciences evolving role in materials development.

3 JPACSM 10 The business requirement is, of course, top and bottom line growth. If new materials are expected to contribute at least 10% to top line growth, and if bottom line revenue is driven by productivity increases (e.g., reduced cost of poor quality, capacity increases, and so on), analytical technology and data mining will have to help lead the way. We expect that the blurring of the line between development and analysis that is seen in ceramics and metallurgy will become the norm rather than the exception. The technology that will effectively team or merge analytical sciences with materials development is the shift toward a combinatorial approach to materials discovery. And again, the shift toward a combinatorial approach to materials discovery is one that is dictated by business needs and the competitive environment. Our view of the future evolution of characterization technology is given in Figure 4. Clearly, with respect to productivity enhancements, process analytical chemistry has and continues to make significant bottom line contributions where it has been routinely implemented in, or at, the manufacturing site. Process analytical chemistry, coupled with a suitable data extraction and reduction scheme, and ultimately coupled with a control strategy has had a considerable impact on the business bottom line. Significant bottom line contributions to revenue are realized through process control that does not rely on process stream snap shots that are four or more hours old because we are awaiting a laboratory measurement. This progression is predicated on rapid development of capable measurement systems. Six Sigma tools have already shown that this is possible, and we expect further utilization of these tools in the future. If we are to move from incremental enhancements to already existing materials to the discovery of new materials however, we need an entirely new set of tools. High throughput experimentation and detectors will be necessary because in combinatorial materials science one creates or synthesizes hundreds or thousands of compounds or materials by reacting a set of components in all possible combinations at once. Rapid screening for one or more performance characteristics is critical if one is to test synthesis strategies in a timely fashion. Combinatorial approaches to materials development are relatively new. While techniques for preparation compositional gradients have been known for many years 1, the convergence of computational power, improved analytical instrumentation, and a strong business drive for new materials has fueled the recent growth in combinatorial material development. Prior to the 1990s several companies recognized the need to increase the rate of new material synthesis. High throughput methods for pharmaceutical compounds are now well established. However, the field of combinatorial materials discovery lags behind the combinatorial efforts that have been underway for at least 15 years in the pharmaceutical industry. The recent reports on combinatorial approaches to materials discovery began to appear in the literature around ,3. Since that time, combinatorial approaches for materials have become more frequent. While combinatorial materials development is still in its early stages, a number of approaches have been developed for preparing material libraries. These have been reviewed in a number of articles 4-8. Sputtering and physical masking has proven quite successful for generating catalyst libraries where the resulting systems had spatial variations in composition and thickness. Solution methods have also been developed to take advantage of the automation approaches employed by the pharmaceutical industry. If we are to succeed, we must borrow or build on what the pharmaceutical industry has done automation, hardware-like robotics and reactors, and data mining techniques that use increasingly sophisticated algorithms to screen or cluster library components for desired activity or property. Several industry leaders are already pursuing this technology for a range of new materials (Table I) Real time non destructive on-line monitoring & control High throughput assay methods Methods to predict properties from structure High throughput detectors Change speed of measurement by two orders of magnitude Figure 4. Evolution of characterization technology.

4 JPACSM 11 Table I Combinatorial Materials Synthesis and Screen Approaches for Solid State Inorganic Materials Materials Application Library Generation Performance Screening Analytical Screening Dielectric Materials Thin Film Vapor Deposition Composition spread Shadow masks Capacitance probe Scanning tip microwave microscope X-ray Diffraction Surface Enhanced Raman Luminescent Materials Photolithography CCD camera with exitation Atomic Force Microscopy Fuel Cell Catalysis Solution Phase Deposition Inkjet delivery Controlled rate deposition Proton release / fluorescence detection Selective Oxidation / Reduction Catalysis Robotic dispensing Infrared thermography Cameras with focal plane array detectors of exothermicity Photoionization with Microelectrode array detection Scanning mass spectroscopy Multi autoclaves 0.5 ml multitube blocks 2 microliter spots on silicon wafer X-ray Microdiffraction Zeolites Polymerization Multipolymerization reactors High speed, high temperature GPC At this point, combinatorial materials development is still in its infancy, but we are confident that it will expand rapidly. If analytical scientists are to succeed in the future we must develop the tools to lead this expansion. For a combinatorial approach to materials development whether we are thinking in terms of new materials or in terms of new processes like those associated with blending, extrusion and so on we will require the application of multidimensional analyses. For example, hyphenated approaches to analysis using separations and spectroscopy or thermal analysis, separations and mass spectrometry. Ideally we would like to see matrix independent chemical analysis, and separation-free analysis. Since the sample ensemble is very large, speed is critical. Faster is the mantra. For example, BASF has reported that it will be looking at 40,000 catalyst compositions per year. If we assume one hour per analysis, that would account for 40,000 man hours or 23.5 man years of analytical effort. In short, we need high throughput screening techniques to provide some sort of figure of merit to flag hits. The issue of scaling is an important one, particularly when dealing with new chemistry. For example, how large should a particular materials library be? If we are dealing with catalysts, how well can we model small catalytic reactors and use the results to predict the design criteria and process robustness for a scaled up reactor? We include here the question of how to best synthesize and process the desired catalyst formulation. Similarly, consider the relationship between processing and performance for polymers. We need to keep before us the interrelatedness of morphology and polymer processing. For example, in a flat-film extrusion process, casting roll temperature and line tensioning play a very important role in determining the resulting mechanical, permeability characteristics, etc., of the final cast film product. Analytical scientists are ideally suited to answering these questions, but not as service providers. That is to say, we must not wait until our customers require these techniques, but in fact must have the necessary measurement techniques on the shelf ready to go long before they are needed. To do this effectively, the analytical sciences must be a full partner in the development of long range technology-product roadmaps or, at the very least, be involved participants in the materials development cycle.

5 JPACSM 12 This evolution will demand both software and hardware development. If we do not develop appropriate software, we will drown in data. If a combinatorial approach to materials development is to succeed, we need algorithms that can interrogate large data fields and provide information related to performance clustering and so on in order to narrow the compositional space that one needs to explore in order to produce the optimal composition for a specific performance criteria. Fabrication techniques and materials used for the production of micro total analytical systems and microreactors is an area that deserves as much attention as data mining. We also need to develop micro scale hardware to make the appropriate measurements. Keep in mind that we have not eliminated the holy triangle right, fast, and cheap. We still need those features. The drive for faster and cheaper demands that we look for economy in scale. Over the years we have produced the micro total analytical system which in effect mimics the silicon chip. That is we have a lab on a chip so to speak, that includes sample manipulation and detection on a chip sized device. This approach has the potential to revolutionize how we characterize materials. As analytical scientists we need to figure out how to best use it for material development. Our expectations for sensor development are shown in Figure 5. Over the next several years analytical measurement systems must lead the rapid growth of combinatorial approaches to materials development and materials processing. This will have to drive the fabrication of rugged, miniaturized collection and sampling systems and micro-fabricated instrument systems. These miniaturized systems will not only perform separation and detection of species, but also make the need for sample preparation or pretreatment off-chip unnecessary. Finally, we will have come full circle. The drive to use combinatorial methods for materials development will drive development of micro sensors. Those sensors may themselves be smart materials. Consequently, the line between analysis and material development will not be blurry; it will be nonexistent. For example, the development of an electronic nose utilized combinatorial identification of sensors and subsequent use of these sensors with multivariate data analysis 9. In effect, there is no differentiation between the development of the device and the analytical characterization of the materials. CONCLUSION In conclusion, this is an exciting time for those of us in the analytical sciences. The targets and drivers are clear. We must move to predictive models and extraction of information from vast amounts of data. We still need to do it right, fast, and cheap and we need to do it with rapidly decreasing sample sizes and exponentially increasing numbers of samples. That is clearly the challenge. Our job now is to figure out how to get there Miniaturized optical Non contact harsh environment Micro fabricated instrument Rugged Miniaturized collection & sampling Improved approaches to imbedded chemometrics Figure 5. Predicated sensor development roadmap.

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