A5101632034- Machine Learning in Materials Science
- Advanced Photocatalysis Techniques
- 2D Materials and Applications
- Electrocatalysts for Energy Conversion
- MXene and MAX Phase Materials
- X-ray Diffraction in Crystallography
- EFL/ESL Teaching and Learning
- CO2 Reduction Techniques and Catalysts
- Engineering Applied Research
- Chalcogenide Semiconductor Thin Films
- Educational Research and Pedagogy
- Education and Learning Interventions
- Hybrid Renewable Energy Systems
- Second Language Learning and Teaching
- Historical Studies and Socio-cultural Analysis
- Educational Technology and Assessment
- Catalytic Processes in Materials Science
- Educational Systems and Policies
- Electronic and Structural Properties of Oxides
- Hydrogen Storage and Materials
- Gas Sensing Nanomaterials and Sensors
- Vietnamese History and Culture Studies
- Quantum Dots Synthesis And Properties
- Forecasting Techniques and Applications
- Diverse Historical and Scientific Studies
Korea University
2025
Korea Institute of Science and Technology
2024
Samsung (South Korea)
2023-2024
Hyundai Motors (South Korea)
2024
Seoul National University
2018-2023
California State University, San Marcos
2017-2018
Conventional studies on photocatalytic oxidation reaction have focused primarily hydroxyl radical (•OH) as a major reactive oxygen species (ROS). This perspective may not be suitable for gaseous HCHO photodegradation due to the short lifetime and diffusion length of •OH. Identification divergent ROS understanding their characteristics gas-phase are further required. Here, we report BiOI/TiO2 p-n junction photocatalyst, which uses mobile singlet (1O2) main photooxidation. The introduction...
Transition metal dichalcogenides (TMDs) have attracted enormous attention in diverse research fields. Especially, gas sensors are considered a promising application exploiting TMDs. However, the studies confined to only major TMDs such as MoS2 and WS2. Particularly, chemoresistive sensing properties of two-dimensional (2D) NbS2 never been explored. For first time, we report room temperature NO2 characteristics 2D nanosheets mechanisms using first-principles calculations based on density...
The catalytic activity for the hydrogen evolution reaction (HER) at anion vacancy of 40 2D transition-metal dichalcogenides (TMDs) is investigated using adsorption free energy (Δ GH) as descriptor. While vacancy-free basal planes are mostly inactive, makes bonding stronger than clean planes, promoting HER performance many TMDs. We find that ZrSe2 and ZrTe2 have similar Δ GH Pt, best catalyst, low density. depends significantly on density, which could be exploited a tuning parameter. At...
Two-dimensional Re dichalcogenide nanostructures are promising electrocatalysts for the hydrogen evolution reaction (HER). Herein, we report adatom doping of various transition metals (TM = Mn, Fe, Co, Ni, and Cu) in ReSe2 nanosheets synthesized using a solvothermal reaction. As atomic number TM increases from Mn to Cu, adatoms on sites become more favored over substitution. In case fraction reaches 90%. Ni resulted most effective enhancement HER catalytic performance, which was...
Using the multiscale simulation combining ab initio calculations and kinetic Monte Carlo (KMC) simulations, we theoretically investigate hydrogen evolution reaction (HER) on sulfur vacancy of a MoS2 monolayer. Unlike metal catalysts, protonation step charging proceed independently in semiconducting MoS2. Interestingly, barrier for decreases when site is hyper-reduced with extra electrons. The turnover frequency polarization curve obtained from KMC agree well extant experimental data, major...
Using angle-resolved photoemission spectroscopy, we show direct evidence for charge transfer between adsorbed molecules and metal substrates, i.e., chemisorption of CO on Pt(111) Pt–Sn/Pt(111) 2 × surfaces. The observed band structures a unique signature as atoms are adsorbed, revealing the roles specific orbital characters participating in process. As coverage increases, degree Pt shows clear difference to that Pt–Sn. With comparison density functional theory calculation results, distinct...
Abstract The discovery of multicomponent inorganic compounds can provide direct solutions to scientific and engineering challenges, yet the vast uncharted material space dwarfs synthesis throughput. While crystal structure prediction (CSP) may mitigate this frustration, exponential complexity CSP expensive density functional theory (DFT) calculations prohibit exploration at scale. Herein, we introduce SPINNER, a structure-prediction framework based on random evolutionary searches. Harnessing...
Ternary metal oxides are crucial components in a wide range of applications and have been extensively cataloged experimental materials databases. However, there still exist cation combinations with unknown stability structures their compounds oxide forms. In this study, we employ extensive crystal structure prediction methods, accelerated by machine-learned potentials, to investigate these untapped chemical spaces. We examine 181 ternary systems, encompassing most cations except for...
Abstract MoS 2 composed of earth‐abundant elements is considered as a promising hydrogen evolution reaction (HER) catalyst for p‐type Si photocathode owing to its appropriate adsorption free energy the edge sites and high photochemical stability in acidic electrolytes. However, direct synthesis uniform atomically thin on by usual chemical vapor deposition techniques remains challenging because weak van der Waals interaction between . Herein, controlling gas phase kinetics during...
In this study, we identify the local structures of ex-solved nanoparticles using machine-learned potentials (MLPs). We develop a method for training by sampling heterointerface configurations as set with its efficacy tested on Ni/MgO system, illustrating that error in interface energy is only 0.004 eV/Å2. Using developed scheme, train an MLP Ni/La0.5Ca0.5TiO3 ex-solution system and both exo- endo-type particles. The established model aligns well experimental observations, accurately...
Abstract Although C 1 species such as CO and CH 4 constitute the majority of 2 reduction (CO R) products on known catalysts, recent experiments showed that 1‐propanol with two C−C bonds is produced main R product MoS single crystals in aqueous electrolytes. Herein, mechanism investigated by using first‐principle calculations. Focusing S‐vacancies ( V S ) catalytic site, potential free‐energy pathways to various are obtained means a computational hydrogen electrode model. The results...
Prediction of the stable crystal structure for multinary (ternary or higher) compounds with unexplored compositions demands fast and accurate evaluation free energies in exploring vast configurational space. The machine-learning potential such as neural network (NNP) is poised to meet this requirement but a dearth information on poses challenge choosing training sets. Herein we propose constructing set from densityfunctional-theory (DFT) based dynamical trajectories liquid quenched amorphous...
The emission linewidth of a semiconducting nanocrystal (NC) significantly affects its performance in light-emitting applications, but fundamental limit is still elusive. Herein, we analyze the exciton-phonon coupling (EPC) from Huang-Rhys (HR) factors using ab initio calculations and compute line shapes CdSe NCs. When surface traps are absent, acoustic modes found to dominate EPC. computed linewidths mainly determined by size NCs, being largely insensitive shape crystal structure. Linewidths...
In fuel cell applications, the durability of catalysts is critical for large-scale industrial implementation. However, limited synthesis controllability and spectroscopic resolution impede a comprehensive understanding degradation mechanisms at atomic level. this study, we develop machine-learned potential (MLP) to simulate processes Pt3Co nanoparticles. The precision MLP determined be comparable that density functional theory calculations. Using off-lattice kinetic Monte Carlo simulations...
Controlled phase conversion in polymorphic transition metal dichalcogenides (TMDs) provides a new synthetic route for realizing tunable nanomaterials. Most methods from the stable 2H to metastable 1T are limited kinetically slow cation insertion into atomically thin layered TMDs charge transfer intercalated ions. Here, we report that anion extraction by selective reaction between carbon monoxide (CO) and chalcogen atoms enables predictive scalable TMD polymorph control. Sulfur vacancy,...
Carbon nitrides (C 3 N 4 ) show excellent properties in various optical and optoelectronic applications. However, their application to electroluminescence (EL) devices is limited by the lack of production methods homogeneous dispersions organic solvents, which are critically required for device fabrication. Herein, a strategy generate stable fluorescent, 2D C materials, demonstrate light‐emitting diodes (LEDs) based on them proposed. The treatment urea‐driven (UCN) with methoxy‐benzene...
Practical CO2 reduction (CO2R) catalysts are desirable to have high selectivity for highly reduced products at small overpotentials and minimal activity the hydrogen evolution reaction (HER). Herein, find such catalysts, we investigate CO2R activities of anion vacancies two-dimensional (2D) transition-metal dichalcogenides (TMDs) by first-principles calculations. For 38 TMDs found in experiments, calculate free energies intermediates along pathways C1 products. In most TMDs, steric hindrance...
Abstract Transition metal‐ and nitrogen‐codoped graphene (referred to as M−N−G, where M is a transition metal) has emerged an important type of single‐atom catalysts with high selectivities activities for electrochemical CO 2 reduction (CO R) CO. However, despite extensive previous studies on the catalytic origin, active site in M−N−G remains puzzling. In this study, density functional theory calculations computational hydrogen electrode model used investigate R reaction energies Zn−N−G,...
The luminescence line shape is an important feature of semiconductor quantum dots (QDs) and affects performance in various optical applications. Here, we report a first-principles method to predict the spectrum thousands atom QDs. In our approach, neural network potential calculations are combined with density functional theory describe exciton-phonon coupling (EPC). Using calculated EPC, evaluated within Franck-Condon approximation. Our approach results for InP/ZnSe core/shell QD (3406...
Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI
<div class="section abstract"><div class="htmlview paragraph">To improve the fuel efficiency and satisfy strict emission regulations, development of internal combustion engine gets more complicated in both hardware software perspectives, margins for durability NVH quality become narrower, which could result poor robustness harsh operating conditions. In this paper, we investigate experimentally camshaft impact noise mechanism relating valve train timing chain forces to detailed...