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. 

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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 are comprised of densely-packed carbon nanotubes (CNTs) that orient vertically during their synthesis because of shared mechanical interactions. 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. 

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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.

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Example Papers:

• Hajilounezhad, Taher, et al. "Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning." npj Computational Materials 7.1 (2021): 1-11.

• Hines, Ryan, et al. "Growth and Mechanics of Heterogeneous, 3D Carbon Nanotube Forest Microstructures Formed by Sequential Selective-Area Synthesis." ACS applied materials & interfaces 12.15 (2020): 17893-17900.

• Brown, Josef, et al. "Delamination mechanics of carbon nanotube micropillars." ACS applied materials & interfaces 11.38 (2019): 35221-35227.

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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 characterize the reactions at the scale of individual nanoparticles. Our facilities to support nanoparticle reactions are currently growing, so check back for updates. 

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Example Papers:

• Zakiyyan, Naadaa, et al. "Surface Plasmon Enhanced Fluorescence Temperature Mapping of Aluminum Nanoparticle Heated by Laser." Sensors 21.5 (2021): 1585.

• Wang, Anqi, et al. "Stability study of iodinated reduced graphene oxide and its application in self-assembled Al/Bi2 O3 nanothermite composites." Nano Futures 4.4 (2020): 045002.

 

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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.

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Check back soon for more details!

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Example Papers:

• Hajilounezhad, Taher, et al. "Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning." npj Computational Materials 7.1 (2021): 1-11.