People

MS in Materials Science, Stevens Institute of Technology, 2024 to Present

BS in Chemical Engineering, Stevens Institute of Technology, 2024

My research utilizes density functional theory to characterize the nanoscale surface behavior of Ni-based bimetallic catalysts in order to determine interplay between the surface chemical potentials of hydrogen and oxygen with the working catalyst surface structure. Through this, the equilibrium surface coverages of H*, O*, and OH* can be predicted on the working catalytic surface for various temperatures and pressures. This allows for the rapid identification of advantageous bimetallic catalysts for the hydrogen economy.

MS in Chemical Engineering, Stevens Institute of Technology, 2024 to Present 

BE in Chemical Engineering, Stevens Institute of Technology, 2024    

My research focuses on catalytic development for the hydrogen oxidation reaction. Focused on creating a machine learning model that predicts catalyst performance of bimetallic PtM and NiM surfaces. The model will be designed with the aim of being applicable for both bimetallic and trimetallic catalysts systems. This work will expedite the procedure for catalyst discovery and development.  

PhD in Chemical Engineering, Stevens Institute of Technology, 2021 to Present

MS in Chemical Engineering, Stevens Institute of Technology, 2024

BS in Chemical Engineering, Obafemi Awolowo University, 2018

Biofuels are the perfect blend of carbon-based, renewable, abundant, carbon-neutral byproduct substitutes for traditional fossil fuels. My research focuses on designing bimetallic catalysts that work best for the catalytic hydrodeoxygenation of lignocellulosic bio-oils in the upgrading process to reduce the problematic oxygen content and push these biofuels towards commercial usage. 

PhD in Chemical Engineering, Stevens Institute of Technology, 2021 to Present

MS in Chemical Engineering, University of Rochester, 2020

BS in Chemistry, Wuhan University, 2018

My research topics involve both non-oxidative and oxidative ethane dehydrogenation for selective ethene production in metal-based catalytic systems from theoretical perspectives including DFT, MKM, and kMC simulations. In my spare time, I like to do photography, seek great food, and participate in outdoor recreation.

MS in Computer Science, Stevens Institute of Technology, 2023 to Present

MS in Environmental Engineering, South China University of Technology, 2021

BS in Environmental Engineering, Zhengzhou University of Aeronautics, 2016

My research project focuses on machine learning enhanced multi-scale modeling, which combines several multi-scale modeling techniques (e.g. density functional theory, molecular dynamics) with machine learning to enable the rapid and rational design of bio-based carbons for heavy metal extraction from wastewater. My work is expected to enable quantification of the nanoscale properties that determine the structure and performance of bio-based carbons for heavy metal adsorption.

BS in Mechanical Engineering, Stevens Institute of Technology, 2023 to present

My research centers around machine-learned interatomic potential for oxygen on PtW bimetallic systems. This focus includes various factors such as adsorbate placement, adsorbate coverage, and metal composition (i.e. Pt:W ratio). I apply multiscale modeling techniques including density functional theory, molecular dynamics, and machine learning to aid in my research. Overall, I hope to further the understanding of the active structure of heterogenous catalysts with applications in biofuels production.

BS in Chemical Engineering, Stevens Institute of Technology, 2022 to present

The objective of my research is to identify efficient catalysts for Oxidative Dehydrogenation of Ethane (EODH) for producing ethylene. Ethylene is a valuable chemical in the petrochemical industry with current practices having a significantly negative impact on our environment. I am focusing on doped NiO catalysts. The research involves simulating different doped NiO catalysts and calculating formation and adsorption energies to determine the stability of the doped NiO structures, providing insights into nanoscale structure of the catalyst surface during EODH.

BS in Chemical Engineering, Stevens Institute of Technology, 2022 to Present 

My research hopes to tackle the issue of water pollutants, specifically heavy metals such as lead and copper, which continue to threaten the future of human sustainability on Earth. Therefore, my work focuses on identifying bio-based carbon groups determined to absorb these heavy metals effectively. This research will allow for future designs of environmentally safe biochar technology that can remove the presence of these heavy metal pollutants in our waterways. 

BS in Chemical Engineering, Stevens Institute of Technology, 2022 to Present 

My research aims to develop a machine-learned interatomic potential (ML-IAP) for O-Rh-Pd catalytic systems and benchmark its accuracy against theoretical and experimental data. Additionally, I will conduct molecular dynamics simulations to study O*-induced RhPd nanoparticle reconstruction. These simulations will test the effects of nanoparticle size, initial O* coverage, Rh composition, and temperature on the reconstruction process, providing insights into optimizing catalytic performance.

BS in Chemical Engineering, Stevens Institute of Technology, 2021 to Present

My research addresses the need to replace the standard, but scarce, noble metal catalysts used in water splitting with more sustainable catalysts. This is done by investigating Ni-based mixed metal oxide catalysts as a potential solution using density functional theory, machine learning, and molecular dynamics. This work will contribute towards green hydrogen production, a promising sustainable and renewable energy source to reduce greenhouse gas emissions.

BS in Computer Science, Stevens Institute of Technology, 2022 to Present 

My research focuses on creating a web-based graphical user interface (GUI) that will enable undergraduate students to explore and apply engineering design to understand complex chemistry topics related to sustainable energy production through catalysis. This GUI will allow students to input a range of parameters (i.e. reaction environment, catalyst composition) and output real-time visualizations of catalyst performance and cost.