1. Multifunctional Polymer Matrix Nanocomposites
Traditional polymer matrix composites with nanofillers (0D, 1D, and 2D) have evolved into a new class of polymer nanocomposites with superior mechanical and physical properties. These nanocomposites can be tailored for specific functional applications in aerospace, automotive, microelectronics, energy, and packaging industries.
2. Structural Composites for Extreme Environment
Structural fiber composites are increasingly used in hostile environments like space exploration, land transportation, and deep-sea navigation. However, the damage and degradation of their mechanical and physical properties in these environments have not been well investigated. These research areas challenge the extreme applications of fiber-reinforced and hybrid fiber/nanoparticle composites.
3. Additive Manufacturing of Advanced Composites
3D additive manufacturing of fiber- and nanoparticle-reinforced polymer composites with defined mechanical and functional properties is popular. Machine learning and field-assisted methods improve process design in some studies. However, printed product quality is often defective due to unoptimized printing parameters. We aim to model the 3D printing process (e.g., FDM) of a selected polymer/continuous fiber or polymer/nanoparticle material system, understand how processing parameters control composite quality, and improve final material performance through property-processing relationships.
4. Data-Driven Design of New Polymer Nanocomposites
Polymer nanocomposites are promising materials for multifunctional applications, but predicting their properties from their constituents is challenging due to their complex nature. A new design paradigm is needed to efficiently predict nanocomposite properties and establish the structure-property relationship for accelerated design. This involves investigating the design and discovery of new or improved polymer nanocomposites with specific property functions (e.g., fracture toughness, wear rate, thermal conductivity, fire retardancy) using available datasets, machine learning techniques, and multiscale computational models.
