At the macro level, techno-economic analysis provides insights into energy solutions' viability and sustainability, pointing to critical parameters and bottlenecks of specific technologies. For example, we examine the techno-economic feasibility of using hydrogen produced from renewable energy as a promising feedstock for energy carriers and chemical commodities manufacturers, shedding light on the potential of using carbon and nitrogen-based derivatives as sustainable alternative hydrogen carriers.
From the micro perspective, producing renewable hydrogen and converting it into valuable products require the design of effective catalysts. The controlled design of phase and morphology in nanostructured materials offers the ability to obtain materials with unique properties tailored for specific catalytic conversion reactions. One case study illustrates the efficacy of electrospun porous ceramic nanofibers with multilevel structures for CO2 hydrogenation to hydrocarbons. We showed the formation morphology mechanism and the interplay between the obtained nanostructures and catalytic performance. In a second case, directing the process parameters of self-assembled block co-polymer enables the creation of thin films with tailored nanostructures. Integrating machine learning models accelerates the exploration of the phase map and assists in classifying key samples.
Zooming into the atomic scale, molecular dynamics (MD) simulations offer detailed insights into material behavior and interactions at the atomic scale, complementing experimental investigation. Specifically, integrating MD simulations with Density Functional Theory (DFT) calculations helped gain a valuable understanding of the nucleation mechanism and pathways to control the morphology of ligand-protected metal nanoclusters. Furthermore, employing machine learning algorithms alongside MD simulations provided important information about polymorphic phase transitions of molecular crystals.
This multi-scale approach provides a holistic framework for navigating the complexities landscape of materials phase map, facilitating the development of tailored materials to meet diverse process needs, and provide mechanistic insight through this interdisciplinary synergy.
Research Interests
As a research associate at the Center for Functional Nanomaterials (CFN) at Brookhaven National Laboratory, my primary focus is pioneering advancements in membrane synthesis and hybrid materials through block co-polymer self-assembly and utilizing innovative autonomous experimental methodologies to create thin film materials with tailored nanoarchitectures. As a postdoctoral associate at Yale University, I investigated crystallization and polymorphism of molecular crystals using molecular dynamics simulations and machine learning algorithms. My PhD research focused on understanding the interplay between morphology and properties of electrospun ceramic nanofibers. During my work, I utilize multi-solution electrospinning systems to create raw materials to produce complex nanostructured ceramics after thermal treatment. Leveraging my materials science and computational chemistry expertise, I aim to contribute enhance the interdisciplinary of these field to accelerate science findings.
During my PhD in energy engineering, I was dedicated to designing new materials for energy-related applications. Nanostructured metal oxides are promising for various applications such as catalysis and batteries. Engineering the morphology of these materials controls their chemical and physical properties and performance in the designated application. However, the production of complex nanoarchitectures is challenging. Therefore, investigating efficient routes and protocols to achieve desired structures and properties was a significant part of my work.
Multilevel structured electrospun nanofibers having complex surface or inner structures are promising morphologies. These structures can have a high surface area to volume ratio and accessible surface, while their large-scale production is feasible. Metal oxide electrospun nanofibers are produced from a composite precursor of a polymer, metal-organic complex, and solvents. After electrospinning, the as-spun sample is introduced into thermal treatment to obtain the final morphology and phase. Different surface and inner multilevel structures can be achieved by controlling the precursor composition and the heating profile during thermal treatment. These different multilevel structures led to different material properties and performance as catalysts for CO2 hydrogenation to hydrocarbons. Nanobelts, a complex inner architecture, showed high conversion and selectivity toward light olefins due to their stable structure under reaction conditions and accessible active sites. The surface complex structure of lamellar-like nanofibers showed better selectivity to heavy hydrocarbons. Thus, these findings demonstrate multilevel morphology's effect on the nanostructured material's catalytic performance.
Computational tools can accelerate material design and screening by predicting properties and performance. Simulations using density functional theory and molecular dynamics are useful for predicting the specific properties of proposed materials and assisting in the discovery of new materials. As a postdoctoral researcher, I develop effective computational protocols utilizing machine-learning models to investigate polymorphism and crystallization in molecular solids.
As a research associate at CFN, I am focus on developing and implementing cutting-edge techniques, particularly utilizing a groundbreaking instrument integrating precise multi-solution spraying and in situ ellipsometry. Also, I using machine learning models to drive autonomous experimentation and optimize synthesis protocols in real time, aiming to achieve unprecedented efficiency and precision.
I am dedicated to fostering a collaborative and interdisciplinary research environment throughout my work. I am actively engage with researchers possessing diverse scientific expertise, facilitating knowledge exchange and cross-disciplinary collaboration. Additionally, I am committed to disseminating my research findings through high-impact publications and presentations, thereby contributing to the advancement of the scientific community.