The Beckman Institute Graduate Student Seminar Series presents the work of outstanding graduate students working in Beckman research groups. The seminars begin at Noon in Beckman Institute Room 1005 and are open to the public. Lunch will be served.
Force-Driven Chemistry: How to Achieve Mechanochemical Activation in Bulk Linear Polymers
Traditionally, chemical reactions are driven by thermal, chemical, or electrical potential. By linking force-sensitive chemical species (mechanophores) into polymer backbones, mechanical deformation can be used drive a chemical reaction. Mechanophores have been developed with potential as damage sensing, self-healing, and self-reinforcing materials. This work is intended to determine the solid-state conditions which promote mechanochemical reactions in various polymer and mechanophore systems. The predominant mechanophore studied, spiropyran (SP), reacts under force to a merocyanine (MC) form, accompanied by the emergence of a fluorescence signal. A combined mechanical and optical set-up was used to measure SP activation in situ. By testing with a variety of loading conditions and polymer mechanical properties, we shed light on the role of stress, strain and polymer mobility in mechanochemical reactions. Current work is focused on applying these findings to achieve activation of emerging mechanophore chemistries.
Biography: Brett Beiermann received his undergraduate degree in Materials Science and Engineering in 2008 from the University of Illinois at Urbana-Champaign. He is currently a fourth-year graduate student working with Professor Nancy Sottos' research group in Materials Science and Engineering and has contributed multiple patent disclosures and journal publications related to self-healing materials.
Low-Power Switching of Phase-Change Materials with Carbon Nanotube Electrodes
Phase change materials (PCMs) are promising candidates for future data storage and reconfigurable electronics; however high programming currents have so far presented a challenge to realize ultra-low power operation. In this work, we enable control of PCM bits using single-wall carbon nanotubes (CNT), which represent the ultimate nanoscale electrodes; this reduces programming currents to the 1-10 μA range, up to two orders of magnitude below present state-of-the-art. I first created nanogaps (20 to 300 nm) in the middle of CNTs via electrical breakdown. Then I sputter-deposited a 10-nm film of amorphous GST to cover the device and fill the nanogaps (Fig. 1). This forms PCM devices with CNT electrodes and extremely small bit volumes, of the order of just a few hundreds of cubic nanometers. The CNT electrodes are very effective in addressing nanometer scale PCM bits, and thus the programming current and energy are scaled down significantly. This is confirmed by electrical characterization showing amorphous-to-crystalline switching at ~1 μA and ~3 V (Fig. 1). Reversible switching is obtained using pulsed voltages, with crystalline-to-amorphous transitions at <5 μA and <100 fJ per bit, approximately two orders of magnitude lower than existing state-of-the-art PCM devices.
Biography: Feng Xiong received his undergraduate degree in Electrical Engineering from the National University of Singapore, where he graduated with First Class Honors in 2008. He has completed his Masters in Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign and is currently working with Prof. Eric Pop on Ultra-low power phase change memory with carbon nanotube interconnects. He has received several awards during his tenure at UIUC, including the Beckman Institute Graduate Fellowship.
Uncertainty Quantification in Microsystems
Microfabrication technologies are highly prone to process variations which cause the performance of a device to significantly deviate from the specifications that it was designed for. This makes it imperative to take these variations or uncertainties in account when designing robust microsystems. In this talk, we look at various numerical tools for representing uncertainties and quantifying their effect on specific design metrics. These tools augment traditional design methods by placing them in a larger framework to simulate the effect of variations in device parameters with speed and accuracy. This process involves using experimental data to generate stochastic models, which are then efficiently propagated through the system. The final statistics of the output metrics provide an estimate of the amount of variation expected in the actual device. We consider examples of simple micromechanical switches that integrate electrostatic and electrothermal actuation. Stochastic models are estimated from measurements of device parameters like stiffness and thermal conductivity as well as the inter-electrode gap. These data-driven estimates are then applied to numerical models of the devices and statistics are computed for the output displacement of the actuators. We show how the results predicted by the models accurately reproduce experimentally observed values.
Biography: Aravind Alwan is a graduate student in Mechanical Engineering at the University of Illinois, Urbana-Champaign. He received his B. Tech degree in Engineering Physics at the Indian Institute of Technology, Delhi and his MS in Mechanical Engineering from UIUC. He is currently pursuing his doctoral studies with a focus on uncertainty quantification in microsystems. His research interests include numerical simulation of microsystems, stochastic modeling and developing propagation methods to perform uncertainty quantification in these systems. He is a recipient of the CSE Fellowship awarded by the Department of Computational Science and Engineering, UIUC and the Summer Undergraduate Research Award from IIT Delhi.