Faculty Research Profile
Seung Geol Lee
학력
· 2011: Ph. D. Georgia Institute of Technology
· 2007: M. S. North Carolina State University
· 2004: B. S. Pusan National University
주요 경력
· 2024~present: Full Professor, UNIST
· 2013~2024: Assistant/Associate/Full Professor, Pusan National University
· 2023~2024: Dept. Chair, Pusan National University
· 2020~2022: Vice Dean, Dept. of Student Affairs, Pusan National University
· 2020~2022: Head, Co-operative Education Center, Pusan National University
· 2012~2013: Senior Research, Agency for Defense Development
· 2011~2012: Postdoctoral Fellow, California Institute of Technology
수상/학회/외부활동
· 2023 Outstanding Paper Award, Korean Fiber Society
· 2020 Industry–Academia–Research Cooperation Award, Korean Carbon Society
· 2018 Academic Award, KSDF
· 2017 Young Researcher Award, Pusan National University
· 2016 Young Investigator Award, Korean Fiber Society
· 2019~present: Advisory Editorial Board Member, Applied Surface Science Advances, Elsevier
· 2019~present: Editorial Board Member, Scientific Reports, Nature Publishing Group
첨단재료설계 연구실
Advanced Materials Design Lab (AMDL)
첨단재료설계연구실(Advanced Materials Design Laboratory, AMDL)은 에너지소재(연료전지소재, 이차전지소재), 극한소재(국방소재) 및 친환경소재(자동차내장소재)의 핵심소재를 연구하고 있습니다. 특히 원자∙분자단위 전산설계 및 기계학습을 이용한 데이터 기반 신소재 연구 및 공정 최적화를 함께 도모하고 있습니다.
The Advanced Materials Design Laboratory (AMDL) focuses on researching core materials in energy materials (fuel cell materials, secondary battery materials), extreme materials (defense materials), and eco-friendly materials (automotive interior materials). Specifically, we aim to promote the development of advanced material research and process optimization using atomic and molecular-level computational design and machine learning techniques.
The Advanced Materials Design Laboratory (AMDL) focuses on researching core materials in energy materials (fuel cell materials, secondary battery materials), extreme materials (defense materials), and eco-friendly materials (automotive interior materials). Specifically, we aim to promote the development of advanced material research and process optimization using atomic and molecular-level computational design and machine learning techniques.
연구분야
전산재료공학, 원자레벨전산모사, 에너지소재(연료전지, 이차전지), 극한소재, 친환경소재 설계
연구 희망분야
원자∙분자레벨전산모사 및 기법개발, 에너지소재 전극설계 및 바인더 개발, 극한소재 물성 분석, 친환경소재 설계 및 공정최적화
연구주제
Computational Materials Science, Molecular Dynamics, Density Functional Theory, Multi-scale Simulations, Fuel Cells (Catalyst Layers, Ionomers, Catalysts), Batteries (Electrodes, Separators), Space and Defense Materials, Eco-materials, Machine Learning
Computational Materials Science, Molecular Dynamics, Density Functional Theory, Multi-scale Simulations, Fuel Cells (Catalyst Layers, Ionomers, Catalysts), Batteries (Electrodes, Separators), Space and Defense Materials, Eco-materials, Machine Learning
국가연구개발사업 기술 분류체계
국가과학기술표준분류
EB. 재료 > EB03. 고분자재료 > EB0307. 에너지/환경산업용 소재기술
논문
· Liquid metal based three-dimensional microelectrode arrays integrated with implantable ultrathin retinal prosthesis for vision restoration, Nature Nanotechnology, 19, 688-697 (2024).
· Ion trap and release dynamics enables non-intrusive tactile augmentation in monolithic sensory neuron, Science Advances, 9, eadi3827 (2023).
· Persulfate activation by nanodiamond-derived carbon onions: Effect of phase transformation of the inner diamond core on reaction kinetics and mechanisms, Applied Catalysis B: Environmental, 293, 120205 (2021).
· Visco-poroelastic electrochemiluminescence skin with piezo-ionic effect, Advanced Materials, 2100321 (2021).
· Novel graphene hydrogel/B-doped graphene quantum dots composites as trifunctional electrocatalysts for Zn−air batteries and overall water splitting, Advanced Energy Materials, 9, 1900945 (2019).
· Facet selectivity of Cu current collector for Li electrodeposition, Energy Storage Materials, 19, 154-162 (2019).
특허
· 이차전지 음극용 바인더, 이의 제조방법 및 이를 이용한 이차전지용 음극 (PCT/KR2022/012603)
· 고강도 에폭시 접착제의 접착강도 예측을 위한 전산모사 기반 데이터베이스 구축 방법 및 이를 수행하는 프로그램을 기록한 컴퓨터로 읽을 수 있는 기록매체 (No. 10-2022-0056204)
· 머신러닝을 이용한 손상된 망막에서 측정된 망막전위도검사(ERG) 신호의 분류 방법 및 이를 이용한 손상된 망막에서 측정된 망막전위도검사(ERG) 신호의 분류 시스템 (No. 10-2434188)