OH GROUP

  • Research Field----
      • [ Enhanced Electrochemical Materials via Nanointerface and Defect Engineering ]

        Our nanocrystal thin-film engineering research employs a bottom-up growth approach that shows great promise for various electrochemical device applications. We focus on precise engineering of nanointerfaces—such as surface facets, grain boundaries, and phase boundaries—as well as control over defects like strain and dislocations, and optimization of pore structures. These engineered features significantly improve the performance of the devices we are developing. Our research focuses on creating electrochemical materials that are essential for a wide range of environmental and energy applications.


        Nanocrystal Thin-Film Engineering Approach:

        1. 1.  Self-Assembly and Shell Growth: After nanocrystal self-assembly, we use colloidal epitaxy or physical deposition techniques to engineer nanointerfaces (e.g., surface facets, grain boundaries, phase boundaries), control defects (e.g., strain and dislocations), and optimize pore structures.
        2. 2.  Crystallite Orientation & Grain Boundaries: We control 3D crystallite orientation and optimize grain boundary formation, ensuring improved structural organization for various applications.


        Electrochemical Materials Applications:

        • 3.  Electrolyzers for Carbon dioxide reduction and nitrate reduction, helping in the sustainable conversion of waste gases into valuable chemicals.
        • 4.  Plasma Catalysis, improving reaction pathways for chemical transformations.
        • 5.  Gas Sensors, which we enhance for higher sensitivity and selectivity by engineering nanointerfaces and defect structures.


        Advanced TEM characterizations:

        6.  3D Atomic Structures: Mapping the detailed atomic arrangement of nanocrystals post-shell growth.
        7.  Correlative Analysis of Chemical and Electronic States: We employ multimodal characterizations, combining TEM with optical and X-ray spectroscopy, for comprehensive insights into chemical and electronic states.
        8.  TEM Analysis with Machine Learning: Using machine learning to analyze nano-to-mesoscale mechanics and improve understanding of structure-property relationships.
        9.  Dynamic Processes: Using in-situ TEM (liquid, gas, EC, heating) to study nanocrystal growth, transport phenomena like ionic diffusion, and structural reorganization under operation conditions.