Research

Advances in health, machine intelligence, and space explorations rely on a new type of materials – intelligent matter that can adapt their morphology and properties to changing tasks and environments. Our research combines the fundamental studies of such materials with application-driven engineering efforts through theory, computation, and experiments. The following sections highlight some of the recent research focuses.

Programmable Materials

4d display-research

Selected Publications

X. Ni, H. Luan, J. Kim, S. Rogge, Y. Bai, J. W. Kwak, S. Liu, D. S. Yang, S. Li, S. Li, Z. Li, Y. Zhang, C. Wu, X. Ni, Y. Huang, H. Wang, and J. A. Rogers, “Soft shape-programmable surfaces by fast electromagnetic actuation of liquid metal networks.” Nature Communications 13, 5576 (2022) [doi]

Y. Bai, H. Wang, Y. Xue, Y. Pan, J. Kim, X. Ni, T. Liu, Y. Yang, M. Han, Y. Huang, J. A. Rogers, and X. Ni, “A dynamically reprogrammable metasurface with self-evolving shape morphing.” Nature 609, 701 – 708 (2022) [doi]


Hierarchically Engineered Composites

Selected publications

X. Ni, L. H. Acauan, and B. L. Wardle, “Hierarchical nanoengineered composites enabled by buckled aligned carbon-nanotube arrays.” Extreme Mechanics Letter 39, 100773 (2020) [doi]

X. Ni, C. Furtado, N. K. Fritz, R. Kopp, P. P. Camanho, and B. L. Wardle, “Interlaminar to intralaminar Mode I and II crack bifurcation due to aligned carbon nanotube reinforcement in aerospace-grade advanced composites.” Composites Science and Technology 190, 108014 (2020) [doi]


4D Characterization

Selected publications

X. Ni, R. Kopp, E. Kalfon-Cohen, C. Furtado, A. Arteiro, G. Borstnar, M. N. Mavrogordato, L. Helfen, I. Sinclair, S. M. Spearing, P. P. Camanho, and B. L. Wardle, “In situ synchrotron tomography study of nanoscale interlaminar reinforcement and ply thickness effects on damage progression in composite laminates.” Composites Part B: Engineering 217, 108623 (2021) [doi]

X. Ni, N. K. Fritz, and B. L. Wardle, “In situ testing using synchrotron radiation computed tomography in materials research.” MRS Advances 4, 2831 – 2841 (2019) [doi]


Machine Learning

Selected publications

R. Kopp, J. M. Joseph, X. Ni, N. Roy, and B. L. Wardle. “Deep Learning Unlocks X‐ray Microtomography Segmentation of Multiclass Microdamage in Heterogeneous Materials.” Advanced Materials, 2107817 (2022) [doi]