(241b) Characterization Based Chemical Product Design for the Nanoscale

Solvason, C. C., Auburn University
Chemmangattuvalappil, N., Auburn University
Eden, M., Auburn University

The National Research Council (NRC) has recently recognized the importance of developing integrated formulated product design tools. In response, it created the Committee on Integrated Computational Materials Engineering (CICME) which developed and published a road map for what it termed a ?Grand Challenge? [1]. In the report, the committee noted that in order to alleviate strain put on U.S. manufacturers from the swiftly changing and increasingly global marketplace, integrated design closely coupling computational models with manufacturing processes would be required. The term ?integrated' recognizes that the properties of products are controlled by a multitude of separate and often competing mechanisms that operate over a wide range of length and time scales. It is the linkage of the scales that remains the ?Grand Challenge' [1]. In response to this call from CICME, the research community has shifted focus from developing only the physical form, function, and aesthetics of assembled products to the design of chemically formulated products [2]. Chemically formulated products are products designed at the molecular level to deliver a specific desired attribute that may exist at multiples scales such as the quantum-, nano-, meso-, macro-, and mega-scales. Examples of formulated products include pharmaceuticals [3], proteins [4], organic semi-conductors [5], nano-structured materials [6] and many more product types.

Using today's technology, designing these optimized molecules for a specific end-use has never more attainable. Recent achievements owe much of their successes to increased computational speeds and the advent of novel algorithms that improved the transfer of information between the formulated scales [6]. One type of promising new algorithms utilize the reverse problem formulation which has been shown to significantly reduce the complexity and required computational time of product design problems. For example, Eden et. al. [7] demonstrated the ability of the reverse problem formulation to decouple product design problems from constitutive equations so that molecules can be designed prior to verification at the various scales, effectively reducing the computational complexity. Furthermore, Eljack et al. [8] extended the reverse problem formulation to include information from processes operating at the macro-scale. Additionally, Satyanarayana et al. [9] recently demonstrated the use of the reverse problem formulation combined with grid technology for polymer design with long range order approaching the meso-scale. While each of these applications of the reverse problem formulation has incorporated elements of multi-scale product design, the integration of the reverse problem formulation into a multi-scale framework that includes the nano-scale has yet to be achieved.

The main objective for this research is to extend the reverse problem formulation algorithm to include aspects of each of the length scales, including the nano-scale, thereby creating a framework where product synthesis, design, and optimization can be achieved with significantly reduced computational time. The research plan to achieve the objective entails the following: (1) the development of a centralized framework, (2) the development of an algorithm that chooses the appropriate properties to bridge each of the length scales, (3) the successful implementation of a property clustering algorithm to load the property descriptions into the framework. Independent validation steps will also be built in to the method and verified using published experimental results because of the ?profound importance of experimental results to calibrate and validate computational methods and fill gaps in the theoretical understanding? [1]. Due to the size of this project, proof-of-concept case studies will initially focus on experimentally derived parameters derived from characterization techniques. Information from the molecular scale on short range order, such as group structure, conformation, and stereoregularity, will be combined with information from the nano-scale on long range order, such as the size, shape, and aspect ratio of the particle and the molecular weight, structure, and polarity of the polymer matrix. In particular, functional groups will be designed for attachment to single walled carbon nano-tubes in order to improve dispersion, which has been shown to significantly improve the mechanical, electrical, and optical properties of nano-composite products.


[1] Committee on Integrated Computational Materials Engineering, National Research Council (2008). Integrated Computational Materials Engineering: A Transformational Discipline for Improved Competitiveness and National Security. The National Academies Press, USA.

[2] Hill, M. (2004) Product and Process Design for Structured Products. AIChE Journal, 50(8), pp. 1656-1661. [3] Solvason C.C., Chemmangattuvalappil N.G., Eden M.R. (2009) Decomposition Techniques for Multi-Scale Structured Product Design: Molecular Synthesis. Computer Aided Chemical Engineering (In Press).

[4] Floudas C.A., Fung H.K., McAllister S.R., Monningmann, M., Rajgaria R. (2006) Advances in protein structure prediction and de novo protein design: A review. Chemical Engineering Science, 61(3), pp.966-988.

[5] Farhang A.R and Deeter T. (1999) Theoretical characterization of phenylene-based oligomers, polymers, and dendrimers. Synthetic Metals, 100(1), pp. 141-162.

[6] Fermeglia M. and Pricl S. (2009) Multiscale molecular modeling in nanostructured material design and process system engineering. Computers & Chemical Engineering (In Press).

[7] Eden M.R., Jergensen S.B., Gani R., and El-Halwagi M.M. (2003) Reverse problem formulation based techniques for process and product synthesis and design. Computer Aided Chemical Engineering, 15(1), pp. 451-456. [8] Eljack F.T., Eden M.R., Vasiliki K., Kazantzi Q., and El-Hawagi M.M. (2007) Simultaneous process and molecular design - A property based approach. AIChE Journal, 53(5), pp. 1232-1239.

[9] Satyanarayana K.C., Abildskov J., and Gani, R. (2009) Computer-aided polymer design using group contribution plus property models. Computers & Chemical Engineering, 33, pp. 1004-1013.