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A Decade of Hi-Payoff, Hi-Throughput Combinatorial Research

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25.05.2009

In its first decade of work, a research effort at the National Institute of Standards and Technology (NIST) to develop novel and improved “combinatorial” techniques for polymer research—an effort that became the NIST Combinatorial Methods Center (NCMC)—realized economic benefits of at least $8.55 for every dollar invested by NIST and its industry partners, according to a new economic analysis.

The new study,* conducted for NIST by RTI International, estimates that from inception (1998) through 2007, the investment in the NCMC has yielded a social rate of return of about 161 percent (also known as the “internal rate of return” in corporate finance; a minimum acceptable IRR for a government research project is about 50 percent). RTI’s evaluation also found that the NCMC accelerated industry’s adoption of combinatorial methods by an average of 2.3 years.

RTI surveyed and interviewed polymer scientists at NCMC member institutions, as well as from the much larger body of research universities and chemical companies who benefited from the center’s research and outreach. The study found that the NCMC has had a significant impact on the development and use of combinatorial research methods for polymers and other organic materials, both in the development of novel techniques and data and the diffusion of research results to the larger polymers research community.

Started as a NIST pilot project in 1998 and formally established in 2002, the NCMC was conceived as a community effort—supported by NIST and industry membership fees—to develop methods for discovering and optimizing complex materials such as multicomponent polymer coatings and films, adhesives, personal care products and structural plastics. Indeed, the RTI report lauded the novel “open source” consortium model that NIST developed for the center as a main reason for its success and impact. The program has since branched out into nanostructured materials, organic electronics, and biomaterials.

The big idea behind combinatorial and high-throughput research is to replace traditional, piecemeal approaches to testing new compounds with methods that can synthesize and test large numbers of possible combinations simultaneously and systematically. NIST in particular pioneered the idea of “continuous gradient libraries,” polymer test specimens whose properties change gradually and regularly from one side to the other so that the behavior of a huge number of possible mixtures can be evaluated at the same time. (For example, see http://www.nist.gov/public_affairs/techbeat/tb2006_0608.htm#designer “Designer Gradients Speed Surface Science Experiments,” NIST Tech Beat, June 8, 2006, and http://www.nist.gov/public_affairs/techbeat/tb2007_0510.htm#wet “Wetter Report: New Approach to Testing Surface Adhesion,” NIST Tech Beat, May 10, 2007.)

The NCMC has contributed advances in three major platform infratechnologies for creating combinatorial “libraries” for materials research—gradient thin films, and discrete libraries created by robotic dispensing systems and microfluidics systems—as well as several new high-throughput measurement methods and information technologies to manage the vast amount of data produced by combinatorial analysis. The center's research program is matched with an outreach effort to disseminate research results through open workshops and training programs.

The RTI report, Retrospective Economic Impact Assessment of the NIST Combinatorial Methods Center, is available online at www.nist.gov/director/prog-ofc/report09-1.pdf. The Web home page of the NIST Combinatorial Methods Center is at http://polymers.nist.gov/combi/index.html.

* A.C. O’Connor, H.J. Walls, D.W. Wood and A.N. Link. Retrospective Economic Impact Assessment of the NIST Combinatorial Methods Center. Planning Report 09-1 prepared by RTI International, Research Triangle Park, N. C. for the National Institute of Standards and Technology, April 2009.

Michael Baum | Quelle: Newswise Science News
Weitere Informationen: www.nist.gov

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