A5034081562- Electrocatalysts for Energy Conversion
- Machine Learning in Materials Science
- Advanced Photocatalysis Techniques
- Advancements in Battery Materials
- Electrochemical Analysis and Applications
- nanoparticles nucleation surface interactions
- Fuel Cells and Related Materials
- Quantum Dots Synthesis And Properties
- Advanced battery technologies research
- Advanced Electron Microscopy Techniques and Applications
- Metal-Organic Frameworks: Synthesis and Applications
- Covalent Organic Framework Applications
- Advanced Nanomaterials in Catalysis
- Advanced Battery Materials and Technologies
- Catalytic Processes in Materials Science
- 2D Materials and Applications
- Nanocluster Synthesis and Applications
- Copper-based nanomaterials and applications
- Radioactive element chemistry and processing
- MXene and MAX Phase Materials
- Fault Detection and Control Systems
- Gas Sensing Nanomaterials and Sensors
- Perovskite Materials and Applications
- Graphene and Nanomaterials Applications
- Advanced biosensing and bioanalysis techniques
Yonsei University
2016-2025
Massachusetts Institute of Technology
2023-2024
Government of the Republic of Korea
2016-2021
Seoul Institute
2018-2021
Cu is considered as the most promising catalyst for electrochemical carbon dioxide reduction reaction (CO2RR) to produce C2+ hydrocarbons, but achieving high product selectivity and efficiency with long-term stability remains one of great challenges. Herein, we report a strategy realize CO2RR allowing stable catalytic properties by utilizing benefits oxygen-plasma-assisted nitrogen doping on CuO. It exhibited that defects such oxygen vacancies grain boundaries suitable are generated N2...
Seeing subtle nanoparticle differences A challenge in the fabrication of nanoparticles is that even for particles uniform size, there will still be a distribution atomic arrangements and surface capping ligands from one particle to next. Using liquid-cell transmission electron microscopy, Kim et al. reconstructed structure individual nanocrystals synthesized batch while they were solution. comparison multiple showed structural heterogeneity between interior outer shell nanoparticles, as well...
The modulating of the geometric and electronic structures metal-N-C atomic catalysts for improving their performance in catalyzing oxygen reduction reactions (ORRs) is highly desirable yet challenging. We herein report a delicate "encapsulation-substitution" strategy synthesis paired metal sites N-doped carbon. With regulation d-orbital energy level, significant increment electroreduction activity was demonstrated Ru-Co diatomic catalyst (DAC) compared with other (Ru-Fe Ru-Ni) single-atomic...
Abstract Green hydrogen production is crucial for a sustainable future, but current catalysts the oxygen evolution reaction (OER) suffer from slow kinetics, despite many efforts to produce optimal designs, particularly through calculation of descriptors activity. In this study, we develop dataset density functional theory calculations bulk and surface perovskite oxides, adsorption energies OER intermediates, which includes compositions up quaternary facets (555). We demonstrate that per-site...
An elegant machine-learning-based algorithm was applied to study the thermo-electrochemical properties of ternary nanocatalysts for oxygen reduction reaction (ORR).
Create Li-ion multichannels and achieve excellent ionic conductivity by doping cost-effective Fe 2+ in halospinel.
Colloidal nanocrystals inherently undergo structural changes during chemical reactions. The robust structure-property relationships, originating from their nanoscale dimensions, underscore the significance of comprehending dynamic behavior in reactive media. Moreover, complexity and heterogeneity inherent atomic structures require tracking transitions individual at three-dimensional (3D) resolution. In this study, we introduce method time-resolved Brownian tomography to investigate temporal...
Achieving both robust extrapolation and physical interpretability in machine learning interatomic potentials (ML-IPs) for atomistic simulation remains a significant challenge, particularly data-scarce areas such as chemical reactions or complex, multicomponent materials at extreme conditions. Here, we present pairwise-decomposed physics-informed neural network (P2Net) that parametrizes an analytical bond-order potential (BOP) layer to decouple the energy contributions of atomic pairs. By...
We study removal of gas-phase organic methyl iodide (CH3I) from an ambient environment via adsorption onto triethylenediamine (TEDA) impregnated activated carbon (AC). First principles density functional theory (DFT) calculations and ab-initio molecular dynamics (AIMD) simulations were extensively utilized to understand the underlying mechanism for chemical reaction CH3I on surface. Our results suggest that energy shows substantial heterogeneity depending site, porosity AC, humidity. It is...
Using first-principles density functional theory calculations and ab initio molecular dynamic simulations, we propose Li10-xSnP2S12-xClx as a highly solid electrolyte for Li ion battery. The underlying mechanisms of excellent conductivity electrochemical stability are 2-fold: (i) complete replacement expensive Ge4+ with relatively cheaper Sn4+ species (ii) partial substitution Cl– S2– to form body-centered cubic anionic framework. rationally controlled doping levels the halide Cl atoms play...
Revealing the underlying mechanism of distinct optoelectronic properties affected by Cl-doping in 2D tin hybrid perovskite.
A unique anion frameowrk for halide solid electrolyte is investigated to promote fast ionic diffusion and secure the electrochemical stability.
Machine-learning (ML) techniques have drawn an ever-increasing focus as they enable high-throughput screening and multiscale prediction of material properties. Especially, ML force fields (FFs) quantum mechanical accuracy are expected to play a central role for the purpose. The construction ML-FFs polymers is, however, still in its infancy due formidable configurational space composing atoms. Here, we demonstrate effective development using kernel functions Gaussian process organic polymer,...
Two-dimensional (2D) catalysts often show extraordinary activity at low mass loading since almost all their atoms are exposed to electrolyte. Palladium (Pd) holds great promise for catalyzing oxygen reduction reaction (ORR) but 2D Pd-based ORR catalyst has rarely been reported. Herein, ternary palladium phosphoronitride (Pd3P2Nx) is synthesized, the first time, catalysis. The synthesis guided by a rational design using first-principles density functional theory calculations, and then...