LDRD Seminar Series: ‘Top-Down Design of Large-Area Monolayers of 2-D Materials’
Scientist Subramanian Sankaranarayanan (NST) will discuss his Laboratory-Directed Research and Development (LDRD) sponsored work at the LDRD Seminar Series presentation Tuesday, Feb. 6, 2018. “Top-Down Design of Large-Area Monolayers of 2-D Materials” begins at 12:30 p.m. in the Building 203 Auditorium. All are welcome to attend.
The search for novel two dimensional (2-D) materials beyond graphene has attracted considerable attention due to their exotic physical properties like room temperature quantum hall effect, charge density waves, high temperature superconductivity, superlubricity and high carrier mobility when the thickness is reduced to a monolayer or a few layers. Although these unique electrical, mechanical, optical and thermal properties of 2-D materials have led to prototype devices and applications, the eventual transition into commercially viable technologies would require large area, scalable and controllable growth of monolayers or few layers. Fundamental insights into the growth and synthesis of 2-D materials are therefore urgently needed to enable scalable and rapid formation of monolayer and possibly single crystalline 2-D materials. Current state-of-the-art relies heavily on chemical vapor deposition (CVD), which has recently been optimized to grow large area 2-D materials. The success of CVD is however dependent on availability of precursors and optimized conditions like temperature, pressure and air flow to name a few. Also the CVD grown samples may suffer polycrystallinity and non-uniform thicknesses, which limit the quality of the samples and hence deteriorates its properties. So far the best quality samples of 2-D materials are obtained through mechanical exfoliation of naturally occurring single crystals. The nature of the method produces flakes with lateral dimensions of a few micrometers and randomly distributed thicknesses ranging from monolayers (~0.6nm) to few layers (~50-100nm), where the probabilities of getting monolayer thick layers are quite low and uncontrollable.
Our LDRD work is aimed at developing (experimentally and theoretically) simple, cheap, fast and scalable process for preparing a wide variety of large-area single-crystalline monolayers of 2-D materials, specifically di-chalcogenides. We have introduced an electrochemical exfoliation route that takes advantage of the relative binding energy differences between the substrate-monolayer vs. the interlayer binding in 2-D material to selectively etch off all but the monolayer closest to the substrate. Simultaneously, we are also exploring the growth of large-area 2-D materials via machine learned reactive atomistic simulations. We have also performed detailed X-ray characterization on the 2-D material samples and integrated 3-D imaging with continuum and multimillion atom simulations to understand the effects of synthesis route and underlying substrate on the structure (lattice strain, corrugations, etc.) and thereby the properties/performance of devices utilizing these 2-D materials. Overall, our experimental and theoretical findings have laid the foundation for the first rational route to preparing practical quantities of monolayer, single-crystalline and large-area 2-D materials, facilitating their application in a range of areas from flexible displays, LEDs, touch screens, solar cells, ultracapacitors, batteries and chemical sensors.
Subramanian Sankaranarayanan is a scientist in the Nanoscale Science and Technology (NST) Division and a Fellow of Computational Institute at the University of Chicago. He obtained his Ph.D. in chemical engineering from University of South Florida in 2007. Prior to joining Argonne, Sankaranarayanan was a postdoctoral fellow at the School of Engineering and Applied Sciences at Harvard University from 2007-2010. His research at Argonne focuses on the use of machine learning to bridge the electronic, atomistic and mesoscopic scales. He is pioneering the use of supervised machine learning techniques to develop first principles based force fields for simulating reactive and mesoscopic systems. He is also one of the principal investigators on another strategic effort on machine learned computational tool development for integrated X-ray imaging of ultrafast energy transport across solid-solid and solid-liquid interfaces. His interests span a diverse range of applications from tribology and corrosion to neuromorphic computing and thermal management.