Research team
Expertise
I am a computational condensed matter and material science expert. I have more than 15 years of extensive experience in the characterization of electronic, optical, thermal, thermoelectric, piezoelectric, ferroelectric, energy storage, and superconducting properties of materials (in particular two-dimensional) with methods based on mostly Density Functional Theory and Classical/First-Principles Molecular Dynamics Simulations. The projects regarding the search for novel two-dimensional crystals to be used in energy generation/conversion, sensor, and ion-battery applications particularly attract my research interest. I have developed more than 12 research projects (as a PI). I have also contributed to a long-term project on the growth and characterization of novel two-dimensional materials such as MoS2, MoSe2, WS2, and Mo2C. Therefore, I have gained extensive experience in Chemical Vapor Deposition growth of materials and their layer-by-layer characterization by RAMAN and PL tools. Currently, I am also working on the development of Machine Learnin-based potential for Materials Simulation applications. I have experience in computational tool development (Fortran, C++, Python) and the operation of simulation tools on HPC systems. Please see my Google Scholar Page:https://scholar.google.com/citations?user=orEY3LwAAAAJ&hl=en
Enhanced Exciton Lifetime in (computationally designed) 2D Heterostructures of Transition Metal Dichalcogenides.
Abstract
Two-dimensional transition metal dichalcogenides (TMDs) hetero-layers have proven promising for future valleytronic, spintronic, and quantum computing applications owing to their ability to host real space-separated excitons with strong binding energy and long radiative lifetime. However, the full physical understanding of the energetics, excitation mechanisms, and dynamics of those special quasiparticles has not been reached yet. Moreover, the optimum heterolayer structures among the many alternatives for hosting the long-lived interlayer excitons have not been determined either experimentally or theoretically. Heterolayer TMD engineering is highly practical in that sense, as interlayer distance, stacking, the type of constituent TMD, and the electric field, all thoroughly affect the emergent excitonic properties. Moreover, its "two-faced" Janus TMD (JTMD) structures (high-quality fabrication has recently been achieved) possessing an intrinsic out-of-plane electric dipole find additional combinational opportunities towards even more versatile excitonic optimization. Therefore, we propose a highly accurate ab initio computational framework to investigate the direct and indirect exciton spectrum of TMD/JTMD and TMD/TMD/JTMD (TMD = MoS2, MoSe2, WS2, WSe2; JTMD = MoSSe, WSSe) 2D heterostructures. We plan to computationally characterize exciton dynamics including phonon-mediated exciton excitation rates and identify the most promising heterolayer crystals, with unprecedented exciton lifetimes for applications in advanced optoelectronics and emergent quantum technology. The proposed computational framework will also contribute to current full ab initio modeling developments and pave the way for the fundamental understanding of interlayer excitons physics, along with creation and annihilation mechanisms.Researcher(s)
- Promoter: Sevik Cem
Research team(s)
Project type(s)
- Research Project
Photo-thermo-structural characterisation of mono- and bimetallic Au and Ag nanoparticles.
Abstract
Fabrication and design of metallic nanoparticles (NPs) has tremendously advanced over the last decades, enabling a variety of their applications. Many of the latter are based on heat delivery - utilizing plasmonic properties of such NPs, where exposure to light activates conducting electrons at the surface and heats the particle, with consequently transferred heat to the (biological, chemical, medical) environment the NP is embedded in. What is often disregarded is that NPs structurally change under such photo-thermal excitations. It is therefore of prime importance to understand the stability and behavior of metallic NPs at elevated and distributed temperature, and devise strategies for their optimized performance under desired conditions. That is the core objective of the present project, focusing on mono- and bimetallic Au and Ag NPs. To achieve this goal, it is first necessary to determine the atomistic structure of the NPs, for which one must go beyond the computationally expensive density-functional theory (DFT) calculations. For that, we will employ machine learning for training the Au and Ag interatomic potentials based on DFT data, towards incrementally sped up yet accurate relaxation of the NP shape and structure. The subsequent iterative coupling of the obtained morphology with spatially varying optical and thermal response is a cutting-edge development, that will enable us to predictively tailor the NPs under heating and light exposure, for any intended purpose.Researcher(s)
- Promoter: Milosevic Milorad
- Co-promoter: Sevik Cem
- Fellow: Vlahovic Jovana
Research team(s)
Project type(s)
- Research Project