Nanoscale Materials

Many properties of materials change when they are confined to nanoscale dimensions.  Our research group is interested in the processing and application of nanoscale materials to achieve new engineering properties. Doing so requires novel processing, characterization, and simulation techniques. We develop new processing methods and characterization tools to exploit the exciting properties of nanoscale materials. Our group is highly collaborative and have interdisciplinary projects across campus and with numerous other Universities and National Labs. 

We have long-standing efforts in the areas of carbon nanotube forests and nanoenergetic materials. Work in these areas is expected to persist. We are also active in areas including localized nanoscale surface functionalization, machine learning and artificial intelligence, and tailored thermal transport. These areas will appear on this page upon publication.

Carbon Nanotube Forests

Carbon Nanotube Forests are comprised of densely-packed carbon nanotubes (CNTs) that orient vertically because of shared mechanical interactions during synthesis. Our goal is to understand these interactions using advanced simulation, machine learning, and in-situ synthesis techniques. If we can understand and control the self-assembly forces, we can control the resultant properties of the CNT forest. 

We are also interested in front-end and back-end processing techniques that can extend the application of CNT forests. These techniques include the use of scanning electron microscopy (SEM)-based patterning using chemical additives, nanoscale machining of CNT forests, and synthesis methods to produce 3D microsturctures.

Nanoenergetic Materials

Nanoeneretic materials are comprised of solid-state fuel and oxide nanoparticles. Upon combustion, the nanoparticles rapidly release significant energy and can reach temperatures exceeding 3000K. In collaboration with the Gangopadhyay group at the University of Missouri, we explore innovative assembly methods to pack nanoparticles at a high density, introduce reactive dopants, and characterize the reactions at the scale of individual nanoparticles. 


The group specializes in using 2D materials to facilitate high-density packing of fuel and oxidizer nanoparticles and in the use of advanced microscopy to react and characterize individual nanoparticles.

Machine Learning / Artificial Intelligence

New digital tools are being developed to increase the rate and quality of materials research. Our team is currently partnering with several research groups to introduce machine learning, deep learning, and artificial intelligence into the development of various nanoscale material systems. This new area is becoming pervasive throughout the research group. We are currently using machine learning techniques to classify CNT forests and to predict their physical properties based on images of their structural morphology.

Check back soon for more details!

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© 2020 Matthew Maschmann