B.Sc. Thesis 

Title: Ab initio Calculations of Electronic, Magnetic, Optical, and Photocatalytic Properties of Strained 1T–ZrSe2

Advisor: Ahmed Zubair, Ph.D. (Associate Professor, Dept. of EEE, BUET)

Abstract:

In this thesis, I delve into the fascinating realm of two-dimensional (2D) materials, focusing on the transition metal dichalcogenide 1T–ZrSe2. Utilizing Density Functional Theory (DFT), I explore how strain influences the electronic, magnetic, optical, and photocatalytic properties of this material. By systematically applying varying degrees of strain, I uncover the potential for tuning these properties to enhance the performance of nanoscale devices.

Research Highlights:

Electronic Properties: Investigation of the band structure and density of states to understand how strain modulates the electronic behavior of 1T–ZrSe2.

Magnetic Properties: Analysis of magnetic moments and spin configurations induced by strain, providing insights into potential spintronic applications.

Optical Properties: Study of the absorption spectra and dielectric function to evaluate the impact of strain on the material's optical responses.

Photocatalytic Properties: Evaluation of the photocatalytic efficiency through changes in the band edge positions and reaction kinetics under different strain conditions.

 
Methodology:

The research employs state-of-the-art DFT calculations to model the strained 1T–ZrSe2. Using this computational approach, the study meticulously examines the correlation between applied strain and the resultant changes in various material properties. These findings are critical for designing and optimizing 2D materials for advanced technological applications.

Significance:

The results of this thesis contribute to the broader understanding of strain engineering in 2D materials, opening new avenues for the development of high-performance electronic, magnetic, optical, and photocatalytic devices. By elucidating the strain-induced property deviations, this work paves the way for future innovations in nanoscale device simulation and fabrication.

You can read my thesis (published) here: https://doi.org/10.1016/j.mtcomm.2023.107271

Academic Projects

Induced Magnetism in Monolayer Transition Metal Dichalcogenides (TMDs)

Advisor: Ahmed Zubair, Ph.D. (Associate Professor, Dept. of EEE, BUET)

In this project, I delved into the magnetic properties of several transition metal dichalcogenides (MX2) using first-principle calculations based on density functional theory (DFT). Transition metal dichalcogenides are a class of materials known for their diverse electronic properties. By investigating the induced magnetism in monolayers of these materials, I aimed to uncover new magnetic behaviors and potential applications in spintronics. The results provide a deeper understanding of how transition metals and chalcogens interact at the atomic level to influence magnetic properties.

Ab Initio Study of Nb Intercalated 2-H MX2 System

Advisor: Ahmed Zubair, Ph.D. (Associate Professor, Dept. of EEE, BUET)

In this study, I introduced Niobium (Nb) atoms into the middle of bilayer 2-H MX2 to explore its novel geometrical, electronic, and optical properties. Using advanced computational techniques, I compared the intercalated system with pristine 2-H MX2. The intercalation of Nb was found to significantly alter the material's properties, opening up new avenues for its application in electronic and optoelectronic devices. This project highlights the potential of material engineering at the atomic scale to tailor properties for specific applications.

Detecting Chest Diseases from Chest X-ray Using Deep Learning

Advisor: Mohammad Ariful Haque, Ph.D. (Professor, Dept. of EEE, BUET)

In this project, I developed a novel deep-learning framework for the multi-label classification of thoracic diseases in chest X-ray images. By effectively exploiting pathological regions that contain critical diagnostic information, the framework aims to enhance the accuracy and efficiency of chest X-ray screening. This work not only showcases the power of artificial intelligence in healthcare but also emphasizes the importance of accurate and automated diagnostic tools in medical imaging.

GCD and LCM Processor Using Verilog

Advisor: ABM Harun-ur Rashid, Ph.D. (Professor, Dept. of EEE, BUET)

I designed a processor using Verilog that can determine the greatest common divisor (GCD) and least common multiple (LCM) of multiple decimal inputs. This project involved creating a digital system capable of performing these mathematical computations efficiently. The processor design emphasizes the practical applications of digital logic design and demonstrates how hardware description languages like Verilog can be used to solve complex computational problems.

Real-time 3D Object Measurement with Sorting

Advisor: Pran Kanai Saha, Ph.D. (Professor, Dept. of EEE, BUET)

In this innovative project, I developed a system for real-time 3D measurement of various objects. Utilizing images and videos as inputs, the system accurately measures different shapes and sizes of objects in real time. The project also included a sorting mechanism based on the measurements, showcasing the integration of image processing and real-time computation. This work is a testament to the capabilities of real-time systems in practical applications such as quality control and automation.