Research Gallery

Material Genome Prediction

The Material Genome Initiative launched in 2011 created a new era for top-down material design and discovery that aims at faster material deployment from research to commercialization. Using polymer nanocomposites as an exemplary case, this project focuses on developing the infrastructure and methodology in representation, evaluation, and prediction of optimal polymer nanocomposite material properties. This synergy combines material structure characterization, processing-structure correlation, physics-based finite element modeling, and a living data resource for polymer nanocomposite material data. Our research targets at the quantitative statistical correlation among the p-s-p domains and examines the underlying principles behind the influences such as interphase and surface chemistry.


Polymer and Polymer Nanocomposites

Nanocomposites are polymers that have been reinforced with small quantities of nano-sized inclusions, defined as particles that have at least one characteristic dimension in the range of 1 to 100 nm. By introducing nano-sized particles, polymer nanocomposites radically differ from conventional polymer composites, and the natural structure and interactions of polymer molecule chains are disturbed on the interface between matrix and fillers, leading to fundamental changes in material properties in this interphase region. Our research thus aims to investigate and understand the mechanism and properties of polymer nanocomposite interphase using computational and experimental approaches in order to design and develop new polymer nanocomposite material systems.


Shape Memory Alloys

Under the appropriate stress and thermal conditions, Shape Memory Alloys (SMAs) exhibit the ability to fully recover large deformations via "superelasticity" or "pseudoelasticity". The main objective of this work is to observe size effects and granular constraints in the elastic and transformation regimes of NiTi based SMAs. Experimental results (using diffraction and imaging techniques) reveal the strain and transformation maps of SMAs, which are compared with similar maps obtained from predictive models.


Advanced Materials Laboratory, Department of Mechanical Engineering
Robert R. McCormick School of Engineering and Applied Science, Northwestern University
2145 Sheridan Road, Evanston, IL 60201 | Phone: (847) 491-7470 | Fax: (847) 491-3915
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