Martha Grover

Martha GroverMartha Grover
Associate Professor, School of Chemical and Biomolecular Engineering
Georgia Institute of Technology, Atlanta, GA

Martha Grover is an Associate Professor in the School of Chemical & Biomolecular Engineering at Georgia Tech. She earned her B.S. in Mechanical Engineering from the University of Illinois, Urbana-Champaign, and her M.S. and Ph.D. in Mechanical Engineering from Caltech. She joined Georgia Tech as an Assistant Professor in 2003, receiving an NSF CAREER award in 2004. In 2011 she received the Outstanding Young Researcher Award from the Computing and Systems Technology Division of AIChE. Her research program is dedicated to understanding and engineering the self-assembly of atoms and small molecules to create larger scale structures and complex functionality. Her approach draws on process systems engineering, combining modeling and experiments in applications dominated by kinetics, including crystal growth, polymer reactions, and colloidal assembly. She is a member of the Institute for Bioengineering and Bioscience, the NSF/NASA Center for Chemical Evolution, and the Georgia Tech Center for Organic Photonics and Electronics.




Martha A. Grover1* and J. C. Lu2
1School of Chemical & Biomolecular Engineering, 311 Ferst Dr. NW, Atlanta, GA 30332, Georgia Institute of Technology
2School of Industrial & Systems Engineering, 755 Ferst Dr. NW, Atlanta, GA 30332, Georgia Institute of Technology

Material structure is highly dependent upon the process used to make it, and material properties are highly dependent upon the structure. When designing a new material and the process to manufacture it, it is uncommon to have an accurate predictive model available that describes this process-structure-property relationship. As a result, statistical design of experiments is often used for process optimization, with little consideration of physical knowledge. Model-based experimental design provides a unifying platform to incorporate all sources of knowledge, including experimental data, physics-based models, and expert opinion. However, existing methods do not  necessarily take into account the objectives of the process engineer, so new formulations of this optimization problem are needed. Illustrations of the experimental design approach in nanoparticle deposition will be given [1,2].

[1] “Optimization of a carbon dioxide-assisted nanoparticle deposition process using sequential experimental design with adaptive design space,” M. J. Casciato, S. Kim, J. C. Lu, D. W. Hess, and M. A. Grover, Industrial & Engineering Chemistry Research, 51(11) 4363-4370 (2012).

[2] “An initial experimental design methodology incorporating expert conjecture, prior data, and engineering models for deposition of
iridium nanoparticles in supercritical carbon dioxide,” M. J. Casciato, J. T. Vastola, J. C. Lu, D. W. Hess, and M. A. Grover, Industrial &
Engineering Chemistry Research
, 52(28), 9645-9653 (2013).