Center for Functional Nanomaterials Seminar

This presentation exhibits research on understanding the synthesis-structure-property relationship of transition metal nitride and oxide thin films. The nitride thin films, TixM1-xN (M = Mo, V, Hf), deposited by plasma enhanced atomic layer deposition (PEALD) were studied for tribological application. The effect of PEALD conditions including deposition temperature, substrate preparation method, applied substrate bias, and the cation ratio, on the structural, physical, chemical, and tribological properties were explored in this study. A few notable findings from the study includes deposition of adherent films by cleaning substrates using UV ozone, application of RF substrate bias causing densification and compressive residual stress in the film both of which contributes to higher wear resistance. Moreover, tuning plasma composition and duration can deposit desired stoichiometric nitride phase with FCC rock salt structure that is up to 98% dense. In this study we observed wear rates as low as 3×10-8 mm3/Nm, which is comparable to the diamonds, diamond-like carbon, and diamond-like nanocomposites. Amorphous MoOx and HfO2 thin films were studied as well, for hole selective layers in photovoltaics and as gate dielectric respectively. The lack of long-range ordering in these amorphous films makes it difficult to probe the structure by crystallography. However, the short-range order of amorphous materials can be probed using X-ray absorption spectroscopy (XAS), and pair distribution function (PDF) by X-ray total scattering. Melt-quench simulation by molecular dynamics (MD) was used to compute a starting amorphous structure which was fitted against the experimental XAS and PDF by reverse Mote Carlo (RMC) simulation to get a realistic structural model. Electronic properties of were predicted using density function theory (DFT) from the refined structure and verified by comparing to the experimental data. This study is proof of concept for the experiment-theory symbiosis approach for amorphous materials. Correlations between atomic scale structural properties, electronic properties, and synthesis/processing conditions learned from such studies can be mined through the application of artificial neural networks (ANNs) providing the building blocks for data-driven materials design. Bio-sketch: Md Istiaque Chowdhury is a PhD candidate at the Department of Materials Science and Engineering, Lehigh University. He also received his MS (leading to PhD) in Materials Science and Engineering from Lehigh University. Prior to graduate education, he worked as a production engineer at Walton Hi-tech Industries PLC, Gazipur Bangladesh. He received his BS in Materials and Metallurgical Engineering from Bangladesh University of Engineering & Technology.