Donghwan Lee
이화여자대학교 의예과 · 의학
Donghwan Lee 교수의 연구실은 촉매 화학과 신소재 개발을 중심으로, 유기합성에서의 고도로 선택적인 C–H 기반 알킬화 반응과 고분자 기반 촉매 시스템의 개발에 주력하고 있습니다. 특히 루테늄 기반 촉매를 활용한 산업적 유용성 높은 반응 설계와 NAND 플래시 메모리의 신뢰성 향상을 위한 신호 분석 알고리즘 개발도 진행 중입니다. 또한 뇌전도도(EEG) 데이터 기반 정신건강 장애 진단을 위한 기계학습 모델 개발을 통해 의료 분야의 정밀의료 기술에도 기여하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Alkenes and alcohols are among the most abundant and commonly used organic feedstock in industrial processes. We report a selective catalytic alkylation reaction of alkenes with alcohols that forms a carbon-carbon bond between vinyl carbon-hydrogen (C-H) and carbon-hydroxy centers with the concomitant loss of water. The cationic ruthenium complex [(C(6)H(6))(PCy(3))(CO)RuH](+)BF(4)(-) (Cy, cyclohexyl) catalyzes the alkylation in solution within 2 to 8 hours at temperatures ranging from 75° to 11
The associations of Internet addiction (IA) and smartphone addiction (SA) with mental health problems have been widely studied. We investigated the effects of IA and SA on depression and anxiety while adjusting for sociodemographic variables. In this study, 4854 participants completed a cross-sectional web-based survey including socio-demographic items, the Korean Scale for Internet Addiction, the Smartphone Addiction Proneness Scale, and the subscales of the Symptom Checklist 90 Items-Revised.
We aimed to develop a machine learning (ML) classifier to detect and compare major psychiatric disorders using electroencephalography (EEG). We retrospectively collected data from medical records, intelligence quotient (IQ) scores from psychological assessments, and quantitative EEG (QEEG) at resting-state assessments from 945 subjects [850 patients with major psychiatric disorders (six large-categorical and nine specific disorders) and 95 healthy controls (HCs)]. A combination of QEEG parameter
As the feature size of NAND flash memory decreases, the threshold voltage signal becomes less reliable, and its distribution varies significantly with the number of program-erase (PE) cycles and the data retention time. We have developed parameter estimation algorithms to find the means and variances of the threshold voltage distribution that is modeled as a Gaussian mixture. The proposed methods find the best-fit parameters by minimizing the squared Euclidean distance between the measured thres
Abstract A mesoporous LTA zeolite (MP‐LTA)‐supported palladium catalyst was developed for the highly efficient Suzuki–Miyaura reaction of aryl and heteroaryl chlorides. The couplings of various aryl chlorides with arylboronic acids in aqueous ethanol were efficiently achieved in the presence of 1.0 mol% of the catalyst. Furthermore, the scope of this catalyst was extended to the coupling of heteroaryl chlorides. Regardless of the substituents, all of the coupling reactions were very clean and hi
The new method has better performance than several existing methods, particularly in the estimation of the loading vectors.
A new β-diketiminatophosphane Pd catalyst was found to be highly effective in the mono and double Hiyama coupling reactions of unactivated aryl chlorides in water.
High-density NAND flash memory suffers from the data retention problem because even small charge leakage incurs a large threshold voltage shift as the transistor size shrinks. In this paper, we develop a decision directed estimation (DDE) algorithm to know the effects of charge leakage in NAND flash memory using the error pattern of the accessed data. While the conventional sensing directed estimation (SDE) method demands extra memory sensing to know the signal distribution, the proposed DDE alg
Internet and smartphone addiction have become important social issues. Various studies have demonstrated their association with clinical and psychological factors, including depression, anxiety, aggression, anger expression, and behavioral inhibition, and behavioral activation systems. However, these two addictions are also highly correlated with each other, so the consideration of the relationship between internet and smartphone addiction can enhance the analysis. In this study, we considered t
Internet gaming disorder (IGD) has become an important social and psychiatric issue in recent years. To prevent IGD and provide the appropriate intervention, an accurate prediction method for identifying IGD is necessary. In this study, we investigated machine learning methods of multimodal neuroimaging data including Positron Emission Tomography (PET), Electroencephalography (EEG), and clinical features to enhance prediction accuracy. Unlike the conventional methods which usually concatenate al
