Nanoscale Materials
Our group is interested in the manufacturing and characterization of nanoscale materials. Example material systems may be found in detail below.
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Working with Computer Science collaborators, we are including artificial intelligence (AI) and machine learning (ML) in most of our experimental efforts. Of particular interest within the group is autonomous experimentation controlled by AI. The vision of autonomy is drastically accelerated research, reduced operational costs, and a removal of human bias.
Two-Photon Lithography
Two-photon polymerization (2PP) is a printing technique with a resolution as low as 160 nm. Features smaller than biological cells and even the wavelength of IR light may be fabricated relatively quickly using a Nanoscribe Quantum X system. This technique is used in the group to fabricate microfluidic devices and optical metamaterials. An overarching goal of this work semi-autonomous design and testing of infrared metamaterials to create arbitrary transmission and reflection signatures.
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An example view of 2PP printing of Faurot Field at the University of Missouri can be seen to the right.
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Please check back frequently for updates in this emerging area.
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Example Papers:
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Fernandez, Simon et al. "Towards Autonomous Design of Metamaterial Surfaces via Two Photon Polymerization Printing," SPIE Advanced Optics for Imaging Applications: UV through LWIR X, 13466, (2025): 56-65.​
Carbon Nanotube Forests
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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:
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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.
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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.
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Brown, Josef, et al. "Delamination mechanics of carbon nanotube micropillars." ACS applied materials & interfaces 11.38 (2019): 35221-35227.
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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 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:
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Zakiyyan, Naadaa, et al. "Surface Plasmon Enhanced Fluorescence Temperature Mapping of Aluminum Nanoparticle Heated by Laser." Sensors 21.5 (2021): 1585.
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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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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.
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Check back soon for more details!
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Example Papers:
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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.
Research Sponsors
