Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Biological or biochemical
Reexamination Certificate
1999-12-30
2003-10-28
Brusca, John S. (Department: 1631)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Biological or biochemical
C702S027000, C435S004000, C435S006120, C436S501000, C436S518000, C436S536000
Reexamination Certificate
active
06640191
ABSTRACT:
BACKGROUND OF THE INVENTION
The goal of combinatorial materials discovery is to find compositions of matter that maximize a specific material property, such as superconductivity, magnetoresistance, luminescence, ligand specificity, sensor response, or catalytic activity. (See, e.g., McFarland, E. W. & Weinberg, W. H. Combinatorial approaches to materials design. TIBTECH 17, 107-115 (1999); Pirrung, M. C. Spatially addressable combinatorial libraries.
Chem. Rev.
97, 473-488 (1997); Weinberg, W. H., Jandeleit, B., Self, K. & Turner, H. Combinatorial methods in homogeneous and heterogeneous catalysis.
Curr. Opin. Chem. Bio.
3, 104-110 (1998); Xiang, X.-D., Sun, X., Briceño, G., Lou, Y., Wang, K.-A., Chang, H., Wallace-Freedman, W. G., Chang, S.-W. & Schultz, P. G. A combinatorial approach to materials discovery.
Science
268, 1738-1740 (1995); Briceño, G., Chang, H., Sun, X., Schultz, P. G. & Xiang, X.-D. A class of cobalt oxide magnetoresistance materials discovered with combinatorial synthesis.
Science
270, 273-275 (1995); Danielson, E., Golden, J. H., McFarland, E. W., Reaves, C. M., Weinberg, W. H. & Wu, X. D. A combinatorial approach to the discovery and optimization of luminescent materials.
Nature
389, 944-948 (1997); Danielson, E., Devermey, M., Giaquinta, D. M., Golden, J. H., Haushalter, R. C., McFarland, E. W., Poojary, D. M., Reaves, C. M., Weinberg, W. H. & Wu, X. D. A rare-earth phosphor containing one-dimensional chains identified through combinatorial methods.
Science
279, 837-839 (1988); Wang, J., Yoo, Y., Gao, C., Takeuchi, L, Sun, X., Chang, H., Xiang, X.-D. & Schultz, P. G. Identification of a blue photoluminescent composite material from a combinatorial library.
Science
279, 1712-1714 (1998); Burger, M. T. & Still, W. C. Synthetic ionophores, encoded combinatorial libraries of cyclen-based receptors for Cu
2+
and Co
2+
. J. Org. Chem.
60, 7382-7383 (1995); Liu, G. & Ellman, J. A. A general solid-phase synthesis strategy for the preparation of 2-pyrrolidinemethanol ligands.
J. Org. Chem.
60, 7712-7713 (1995); Bilbertson, S. R. & Wang, X. The combinatorial synthesis of chiral phosphine ligands.
Tetrahed. Lett.
37, 6475-6478 (1996); Francis, M. B., Jamison, T. F. & Jacobsen, E. N. Combinatorial libraries of transition-metal complexes, catalysts and materials.
Curr. Opin. Chem. Biol.
2, 422-428 (1998); Dickinson, T. A. & Walt, D. R. Generating sensor diversity through combinatorial polymer synthesis.
Anal. Chem.
69, 3413-3418 (1997); Menger, F. M., Eliseev, A. V. & Migulin, V. A. Phosphatase catalysis developed via combinatorial organic chemistry.
J. Org. Chem.
60, 6666-6667 (1995); Burgess, K., Lim, H.-J., Porte, A. M. & Sulikowski, G. A. New catalysts and conditions for a C—H insertion reaction identified by high throughput catalyst screening.
Angew. Chem. Int. Ed.
357 220-222 (1996); Cole, B. M., Shimizu, K. D., Krueger, C. A., Harrity, J. P. A., Snapper, M. L. Hoveyda, A. H. Discovery of chiral catalysts through ligand diversity: Ti-catalyzed enantioselective addition of TMSCN to meso epoxides.
Angew. Chem. Int. Ed.
35, 1668-1671 (1996); Akporiaye, D. E., Dahl, I. M., Karlsson, A. & Wendelbo, R. Combinatorial approach to the hydrothermal synthesis of zeolites.
Angew. Chem. Int. Ed.
37, 609-611 (1998); Reddington, E., Sapienza, A., Gurau, B., Viswanathan, R., Sarangapani, S., Smotkin, E. S. & Mallouk, T. E. Combinatorial electrochemistry: A highly parallel, optical screening method for discovery of better electrocatalysts.
Science
280, 1735-1737 (1998); and Cong, P., Doolen, R. D., Fan, Q., Giaquinta, D. M., Guan, S., McFarland, E. W., Poojary, D. M., Self, K., Turber, H. W. & Weinberg, W. H. High-throughput synthesis and screening of combinatorial heterogeous catalyst libraries.
Angew. Chem. Int. Ed.
38., 484-488 (1999)].
A variety of materials have been optimized or developed to date by combinatorial methods. Perhaps the first experiment to gather great attention was the demonstration that inorganic oxide high-T
c
superconductors could be identified by combinatorial methods (Xiang et al., supra). By searching several 128-member libraries of different inorganic oxide systems, the known compositions of superconducting BiSrCaCuO and YBaCuO were identified. Since then, many demonstrations of finding known materials and discoveries of new materials have appeared. Known compositions of magnetoresistant materials have been identified in libraries of various cobalt oxides (Briceño et al., supra). Blue and red phosphors have been identified from large libraries of 25,000 different inorganic oxides [Danielson et al. (1998), supra; Danielson et al. (1988), supra; and Wang et al., supra]. Polymer-based sensors for various organic vapors have been identified by combinatorial methods (Dickinson et al., supra). Catalysts for the oxidation of CO to CO
2
have been identified by searching ternary compounds of Pd, Pt, and Rh or Rh, Pd, and Cu (Pirrung, supra, and Cole et al., supra). Phase diagrams of zeolitic materials have been mapped out by a combinatorial “multiautoclave” (Akporiaye et al., supra). Novel enantioselective catalysts have been found by searching libraries of transition metal-peptide complexes (Cole et al., supra). Novel phosphatase catalysts were found by searching libraries of carboxylic acid-functionalized polyallylamime polymers (Menger et al., supra). New catalysts and conditions for C—H insertion have been found by screening of ligand-transition metal systems (Burgess et al., supra). A new catalyst for the conversion of methanol in a direct methanol fuel cell was identified by searching the quaternary composition space of Pt, Ir, Os, and Ru (Reddington et al., supra). Finally, a novel thin-film high dielectric compound that may be used in future generations of DRAM chips was identified by searching through over 30 multicomponent, ternary oxide systems [van Dover, R. B., Schneemeyer, L. F. & Fleming, R. M. Discovery of a useful thin-film dielectric using a composition-spread approach.
Nature
392, 162-164 (1998)].
Present approaches to combinatorial library design and screening invariably amount to a grid search in composition space, followed by a “steepest-ascent” maximization of the figure of merit. Such optimization procedures, however, are inefficient methods at finding optima in high-dimensional spaces or when the figure of merit is not a smooth function of the variables. Indeed, the use of a grid search is what has limited essentially all current combinatorial chemistry experiments to quaternary compounds, i.e., to searching a space with three variables. What is needed is an automated, yet more efficient, procedure for searching composition space.
SUMMARY OF THE INVENTION
The present invention uses Monte Carlo methods to provide just such a powerful new procedure for searching a multi-dimensional space of variables in combinatorial chemistry. Moreover, the effectiveness of the new protocols is validated on the Random Phase Volume Model, showing the new methods to be superior to those in current practice.
The present invention provides a method for generating a combinatorial library, which begins by preparing a first set of current samples using a grid search or a random selection method. Preferably, each initial sample has at least one composition or non-composition variable selected by a random method. In one preferred Monte Carlo method, the first set of current samples is prepared by choosing the variables of each current sample at random from allowed values. Alternatively, the first set of current samples could be prepared by choosing the variables of each current sample via a quasi-random, low discrepancy sequence.
The next step is preparing a new set of proposed samples by changing the variables of each current sample by a Monte Carlo selection method. A preferred method for changing the variables is a random displacement protocol, which randomly changes at least one of the composition and non-composition variables of a randomly chosen current sample a small amount. The
Deem Michael W.
Falcioni Marco
Brusca John S.
Fulbright & Jaworski L.L.P.
The Regents of the University of California
Zhou Shubo
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